diff options
author | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-04-22 21:13:21 +0100 |
---|---|---|
committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-05-18 14:48:39 +0000 |
commit | 856f66e6c61b77d03f754cd0fa8439891f0e4aca (patch) | |
tree | f9379cd0853ac407109e54c3d53b385ceee066c2 /src/core | |
parent | 37f4b2ef1ea225a90ccb563fcb2c08f8fb0fb5d5 (diff) | |
download | ComputeLibrary-856f66e6c61b77d03f754cd0fa8439891f0e4aca.tar.gz |
Port CLGEMM to memory injecting interface
Moves the following kernels:
- CLGEMMMatrixMultiplyKernel
- CLGEMMMatrixMultiplyNativeKernel
- CLGEMMMatrixMultipluReshapedKernel
- CLGEMMMatrixMultiplyReshapedOnlyRHSKernel
Moves the following functions
- CLGEMM
Introduces facilities to easy handling of auxiliary temporary buffers
under then new run interface. Such are:
- CLAuxTensorHandler: That allows wrapping of workspace buffers memory
to CLBuffer objects
- Ability to inject TensorInfo to allocator without transferring
ownership. This reduce the copy overhead if needed.
Resolves: COMPMID-4188
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: I7055435d831b05b749b26302082e4ac45f26dfb0
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5498
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/core')
46 files changed, 2701 insertions, 1741 deletions
diff --git a/src/core/CL/CLKernels.h b/src/core/CL/CLKernels.h index 63978cea3f..1302d52180 100644 --- a/src/core/CL/CLKernels.h +++ b/src/core/CL/CLKernels.h @@ -54,12 +54,6 @@ #include "src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel.h" #include "src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ScaleKernel.h" #include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h" -#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h" -#include "src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h" -#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h" -#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h" -#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h" -#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h" #include "src/core/CL/kernels/CLGatherKernel.h" #include "src/core/CL/kernels/CLGenerateProposalsLayerKernel.h" #include "src/core/CL/kernels/CLIm2ColKernel.h" diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedKernel.h b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedKernel.h index 100100b1b1..06a73f173d 100644 --- a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedKernel.h +++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2020 Arm Limited. + * Copyright (c) 2019-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -32,7 +32,9 @@ class ICLTensor; /** OpenCL kernel to multiply matrices when both the input matrices LHS (input0) and RHS (input1) have been reshaped * - * @note The input matrices @p input0 and @p input1 must be reshaped through @ref CLGEMMReshapeLHSMatrixKernel and @ref CLGEMMReshapeRHSMatrixKernel + * @note The input matrices @p input0 and @p input1 must be reshaped through: + * - @ref opencl::kernels::ClGemmReshapeLhsMatrixKernel + * - @ref opencl::kernels::ClGemmReshapeRhsMatrixKernel */ class CLGEMMLowpMatrixMultiplyReshapedKernel : public ICLKernel { diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h index 222a8615e4..e79f6dfe05 100644 --- a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h +++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2020 Arm Limited. + * Copyright (c) 2019-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -33,7 +33,7 @@ class ICLTensor; /** OpenCL kernel to multiply matrices with QASYMM8 data type when only the input matrix RHS (input1) has been reshaped * - * @note The input matrix input1 must be reshaped through @ref CLGEMMReshapeRHSMatrixKernel + * @note The input matrix input1 must be reshaped through @ref opencl::kernels::ClGemmReshapeRhsMatrixKernel * @note For fused output stage, only GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT type is supported */ class CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel : public ICLKernel diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h deleted file mode 100644 index 71d223b8ac..0000000000 --- a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h +++ /dev/null @@ -1,122 +0,0 @@ -/* - * Copyright (c) 2017-2020 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_CLGEMMMATRIXMULTIPLYKERNEL_H -#define ARM_COMPUTE_CLGEMMMATRIXMULTIPLYKERNEL_H - -#include "src/core/CL/ICLKernel.h" - -namespace arm_compute -{ -class ICLTensor; - -/** OpenCL kernel to multiply two input matrices "A" and "B" and add a martix "C" if provided. All elements of the output matrix will be multiplied by alpha. In case matrix C is passed, it will be added to the previous result. - * For the matrix C, the broadcast addition is supported if the flag "broadcast_bias" is set in the GEMMReshapeInfo object - * - * @note If the input tensors @p input0 and @p input1 have been reshaped respectively with @ref CLGEMMReshapeLHSMatrixKernel" and @ref CLGEMMReshapeRHSMatrixKernel, - * the flag @p is_interleaved_transposed must be set to true - * - * @attention @p input1 tensor must have at least 2 dimensions (matrix) - * - */ -class CLGEMMMatrixMultiplyKernel : public ICLKernel -{ -public: - /** Default constructor */ - CLGEMMMatrixMultiplyKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLGEMMMatrixMultiplyKernel(const CLGEMMMatrixMultiplyKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLGEMMMatrixMultiplyKernel &operator=(const CLGEMMMatrixMultiplyKernel &) = delete; - /** Allow instances of this class to be moved */ - CLGEMMMatrixMultiplyKernel(CLGEMMMatrixMultiplyKernel &&) = default; - /** Allow instances of this class to be moved */ - CLGEMMMatrixMultiplyKernel &operator=(CLGEMMMatrixMultiplyKernel &&) = default; - /** Initialise the kernel's input, output and alpha - * - * @param[in] input0 Input tensor containing the Matrix A. Data types supported: F16/F32 - * @param[in] input1 Input tensor containing the Matrix B. Data type supported: same as @p input0 - * @param[in] input2 Input tensor containing the Matrix C (bias). Can be nullptr. Data type supported: same as @p input0 - * @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0 - * @param[in] alpha Weight of the matrix product - * @param[in] beta (Optional) Weight of vector C. Default value is 0. Only beta = 1 is currently supported. - * @param[in] is_interleaved_transposed (Optional) True if input0 and input1 have been reshaped respectively using @ref CLGEMMReshapeLHSMatrixKernel and @ref CLGEMMReshapeRHSMatrixKernel - * @param[in] reshape_info (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped - * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy - * @param[in] activation_info (Optional) Activation to apply after the matrix multiplication - * - */ - void configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta = 0.f, - bool is_interleaved_transposed = true, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo(), bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo()); - /** Initialise the kernel's input, output and alpha - * - * @param[in] compile_context The compile context to be used. - * @param[in] input0 Input tensor containing the Matrix A. Data types supported: F16/F32 - * @param[in] input1 Input tensor containing the Matrix B. Data type supported: same as @p input0 - * @param[in] input2 Input tensor containing the Matrix C (bias). Can be nullptr. Data type supported: same as @p input0 - * @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0 - * @param[in] alpha Weight of the matrix product - * @param[in] beta (Optional) Weight of vector C. Default value is 0. Only beta = 1 is currently supported. - * @param[in] is_interleaved_transposed (Optional) True if input0 and input1 have been reshaped respectively using @ref CLGEMMReshapeLHSMatrixKernel and @ref CLGEMMReshapeRHSMatrixKernel - * @param[in] reshape_info (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped - * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy - * @param[in] activation_info (Optional) Activation to apply after the matrix multiplication - * - */ - void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta = 0.f, - bool is_interleaved_transposed = true, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo(), bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo()); - /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMMatrixMultiplyKernel - * - * @param[in] input0 Input tensor containing the Matrix A info. Data types supported: F16/F32 - * @param[in] input1 Input tensor containing the Matrix B info. Data type supported: same as @p input0 - * @param[in] input2 Input tensor containing the Matrix C (bias) info. Can be nullptr. Data type supported: same as @p input0 - * @param[in] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0 - * @param[in] alpha Weight of the matrix product - * @param[in] beta Weight of vector C. Default value is 0. Only beta = 1 is currently supported. - * @param[in] is_interleaved_transposed True if input0 and input1 have been reshaped respectively using @ref CLGEMMReshapeLHSMatrixKernel and @ref CLGEMMReshapeRHSMatrixKernel - * @param[in] reshape_info GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped - * @param[in] gpu_target GPU Target - * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy - * @param[in] activation_info (Optional) Activation to apply after the matrix multiplication - * - * @return a status - */ - static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, - bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo()); - - // Inherited methods overridden: - void run(const Window &window, cl::CommandQueue &queue) override; - -public: - const ICLTensor *_input0; - const ICLTensor *_input1; - const ICLTensor *_input2; - ICLTensor *_output; - bool _slide_matrix_b; - bool _reinterpret_input_as_3d; - bool _reinterpret_output_as_3d; - bool _add_bias; - bool _broadcast_bias; -}; -} // namespace arm_compute -#endif /* ARM_COMPUTE_CLGEMMMATRIXMULTIPLYKERNEL_H */ diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h b/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h deleted file mode 100644 index 6b6004b464..0000000000 --- a/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h +++ /dev/null @@ -1,127 +0,0 @@ -/* - * Copyright (c) 2019-2020 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_CLGEMMMATRIXMULTIPLYNATIVEKERNEL_H -#define ARM_COMPUTE_CLGEMMMATRIXMULTIPLYNATIVEKERNEL_H - -#include "src/core/CL/ICLKernel.h" - -#include "arm_compute/core/KernelDescriptors.h" - -namespace arm_compute -{ -class ICLTensor; - -/** OpenCL kernel to multiply matrices when neither of the input matrices have been reshaped */ -class CLGEMMMatrixMultiplyNativeKernel : public ICLKernel -{ -public: - /** Default Constructor */ - CLGEMMMatrixMultiplyNativeKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLGEMMMatrixMultiplyNativeKernel(const CLGEMMMatrixMultiplyNativeKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLGEMMMatrixMultiplyNativeKernel &operator=(const CLGEMMMatrixMultiplyNativeKernel &) = delete; - /** Allow instances of this class to be moved */ - CLGEMMMatrixMultiplyNativeKernel(CLGEMMMatrixMultiplyNativeKernel &&) = default; - /** Allow instances of this class to be moved */ - CLGEMMMatrixMultiplyNativeKernel &operator=(CLGEMMMatrixMultiplyNativeKernel &&) = default; - /** Initialise the kernel's input and output. - * - * @param[in] input0 Input tensor for the LHS matrix. Data type supported: F32. The number of dimensions for the LHS matrix must be less or equal than 4. - * @param[in] input1 Input tensor for the RHS matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3. - * @param[in] input2 Input tensor containing the bias matrix. Data type supported: same as @p input0. - * @param[out] output Output tensor info. Data type supported: same as @p input0 - * @param[in] alpha Weight of the matrix product - * @param[in] beta Weight of the matrix bias - * @param[in] lhs_info LHS matrix information used to retrieve the number of rows and accumulations to be processed by each thread. Only the following values are supported: - * lhs_info.m0: 1,2,3,4,5,6,7,8 - * lhs_info.k0: 2,3,4,8,16 - * @param[in] rhs_info RHS matrix information used to retrieve the number of columns and accumulations to be processed by each thread. Only the following values are supported: - * rhs_info.n0: 2,3,4,8,16 - * rhs_info.k0: same of lhs_info.k0 - * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices - */ - void configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, - const GEMMKernelInfo &gemm_info); - /** Initialise the kernel's input and output. - * - * @param[in] compile_context The compile context to be used. - * @param[in] input0 Input tensor for the LHS matrix. Data type supported: F32. The number of dimensions for the LHS matrix must be less or equal than 4. - * @param[in] input1 Input tensor for the RHS matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3. - * @param[in] input2 Input tensor containing the bias matrix. Data type supported: same as @p input0. - * @param[out] output Output tensor info. Data type supported: same as @p input0 - * @param[in] alpha Weight of the matrix product - * @param[in] beta Weight of the matrix bias - * @param[in] lhs_info LHS matrix information used to retrieve the number of rows and accumulations to be processed by each thread. Only the following values are supported: - * lhs_info.m0: 1,2,3,4,5,6,7,8 - * lhs_info.k0: 2,3,4,8,16 - * @param[in] rhs_info RHS matrix information used to retrieve the number of columns and accumulations to be processed by each thread. Only the following values are supported: - * rhs_info.n0: 2,3,4,8,16 - * rhs_info.k0: same of lhs_info.k0 - * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices - */ - void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, - const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, - const GEMMKernelInfo &gemm_info); - /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMMatrixMultiplyNativeKernel - * - * @param[in] input0 Input tensor info for the LHS matrix. Data type supported: F32. The number of dimensions for the LHS matrix must be less or equal than 4. - * @param[in] input1 Input tensor info for the RHS matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3. - * @param[in] input2 Input tensor info containing the bias matrix. Data type supported: same as @p input0. - * @param[in] output Output tensor info. Data type supported: same as @p input0 - * @param[in] alpha Weight of the matrix product - * @param[in] beta Weight of the matrix bias - * @param[in] lhs_info LHS matrix information used to retrieve the number of rows and accumulations to be processed by each thread. Only the following values are supported: - * lhs_info.m0: 1,2,3,4,5,6,7,8 - * lhs_info.k0: 2,3,4,8,16 - * @param[in] rhs_info RHS matrix information used to retrieve the number of columns and accumulations to be processed by each thread. Only the following values are supported: - * rhs_info.n0: 2,3,4,8,16 - * rhs_info.k0: same of lhs_info.k0 - * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices - * - * @return a status - */ - static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, - const GEMMKernelInfo &gemm_info); - - // Inherited methods overridden: - void run(const Window &window, cl::CommandQueue &queue) override; - -private: - const ICLTensor *_input0; - const ICLTensor *_input1; - const ICLTensor *_input2; - ICLTensor *_output; - bool _slide_matrix_b; - bool _reinterpret_input_as_3d; - bool _reinterpret_output_as_3d; - bool _use_dummy_work_items; - bool _add_bias; - bool _broadcast_bias; -}; -} // namespace arm_compute -#endif /*ARM_COMPUTE_CLGEMMMATRIXMULTIPLYNATIVEKERNEL_H*/ diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h b/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h deleted file mode 100644 index 2ffc322def..0000000000 --- a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h +++ /dev/null @@ -1,188 +0,0 @@ -/* - * Copyright (c) 2018-2020 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_CLGEMMMATRIXMULTIPLYRESHAPEDKERNEL_H -#define ARM_COMPUTE_CLGEMMMATRIXMULTIPLYRESHAPEDKERNEL_H - -#include "src/core/CL/ICLKernel.h" - -#include "arm_compute/core/KernelDescriptors.h" - -namespace arm_compute -{ -class ICLTensor; - -/** OpenCL kernel to multiply matrices when both the input matrices LHS (input0) and RHS (input1) have been reshaped - * - * @note The input matrices @p input0 and @p input1 must be reshaped through @ref CLGEMMReshapeLHSMatrixKernel and @ref CLGEMMReshapeRHSMatrixKernel - */ -class CLGEMMMatrixMultiplyReshapedKernel : public ICLKernel -{ -public: - /** Default Constructor */ - CLGEMMMatrixMultiplyReshapedKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLGEMMMatrixMultiplyReshapedKernel(const CLGEMMMatrixMultiplyReshapedKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLGEMMMatrixMultiplyReshapedKernel &operator=(const CLGEMMMatrixMultiplyReshapedKernel &) = delete; - /** Allow instances of this class to be moved */ - CLGEMMMatrixMultiplyReshapedKernel(CLGEMMMatrixMultiplyReshapedKernel &&) = default; - /** Allow instances of this class to be moved */ - CLGEMMMatrixMultiplyReshapedKernel &operator=(CLGEMMMatrixMultiplyReshapedKernel &&) = default; - /** Initialise the kernel's input and output. - * - * @note The F16 computation also supports mixed precision through the gemm_info.fp_mixed_precision flag. - * Mixed precision combines different floating precisions during the computation, in particular, F32 for the accumulations and F16 for the - * multiplications. i.e. float c = (half)a * (half)b - * - * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function. - * Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer, - * the following conditions are required: - * -# rhs_info.n0 can only be 4, 8 and 16 - * -# rhs_info.k0 can only be 4, 8 and 16 - * -# Data type can only be F32 - * -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension - * -# The stride Y for the input1 should satisfy the OpenCL pitch alignment requirement - * -# input1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4) - * -# input1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT - * - * @param[in] input0 Input tensor containing the LHS reshaped matrix. Data type supported: F16/F32 (only F32 if rhs_info.export_to_cl_image = true). The number of dimensions for the LHS matrix must be less or equal than 4 - * @param[in] input1 Input tensor containing the RHS reshaped matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3 - * @param[in] input2 Input tensor containing the bias matrix. Data type supported: same as @p input0. - * @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0 - * @param[in] alpha Weight of the matrix product - * @param[in] beta Weight of the matrix bias - * @param[in] lhs_info LHS matrix information used for reshaping the input0 tensor. Only the following values are supported: - * lhs_info.m0: 2,3,4,5,6,7,8 - * lhs_info.k0: 2,3,4,8,16 - * lhs_info.transpose: false - * @param[in] rhs_info RHS matrix information used for reshaping the input1 tensor. Only the following values are supported: - * rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true) - * rhs_info.k0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true) - * rhs_info.transpose: true - * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices - * - * @note lhs_info.k0 must be equal to rhs_info.k0 - */ - void configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, - const GEMMKernelInfo &gemm_info); - /** Initialise the kernel's input and output. - * - * @note The F16 computation also supports mixed precision through the gemm_info.fp_mixed_precision flag. - * Mixed precision combines different floating precisions during the computation, in particular, F32 for the accumulations and F16 for the - * multiplications. i.e. float c = (half)a * (half)b - * - * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function. - * Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer, - * the following conditions are required: - * -# rhs_info.n0 can only be 4, 8 and 16 - * -# rhs_info.k0 can only be 4, 8 and 16 - * -# Data type can only be F32 - * -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension - * -# The stride Y for the input1 should satisfy the OpenCL pitch alignment requirement - * -# input1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4) - * -# input1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT - * - * @param[in] compile_context The compile context to be used. - * @param[in] input0 Input tensor containing the LHS reshaped matrix. Data type supported: F16/F32 (only F32 if rhs_info.export_to_cl_image = true). The number of dimensions for the LHS matrix must be less or equal than 4 - * @param[in] input1 Input tensor containing the RHS reshaped matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3 - * @param[in] input2 Input tensor containing the bias matrix. Data type supported: same as @p input0. - * @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0 - * @param[in] alpha Weight of the matrix product - * @param[in] beta Weight of the matrix bias - * @param[in] lhs_info LHS matrix information used for reshaping the input0 tensor. Only the following values are supported: - * lhs_info.m0: 2,3,4,5,6,7,8 - * lhs_info.k0: 2,3,4,8,16 - * lhs_info.transpose: false - * @param[in] rhs_info RHS matrix information used for reshaping the input1 tensor. Only the following values are supported: - * rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true) - * rhs_info.k0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true) - * rhs_info.transpose: true - * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices - * - * @note lhs_info.k0 must be equal to rhs_info.k0 - */ - void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, - const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, - const GEMMKernelInfo &gemm_info); - /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMMatrixMultiplyReshapedKernel - * - * @note The F16 computation also supports mixed precision through the gemm_info.fp_mixed_precision flag. - * Mixed precision combines different floating precisions during the computation, in particular, F32 for the accumulations and F16 for the - * multiplications. i.e. float c = (half)a * (half)b - * - * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function. - * Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer, - * the following conditions are required: - * -# rhs_info.n0 can only be 4, 8 and 16 - * -# rhs_info.k0 can only be 4, 8 and 16 - * -# Data type can only be F32 - * -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension - * -# The stride Y for the input1 should satisfy the OpenCL pitch alignment requirement - * -# input1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4) - * -# input1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT - * - * @param[in] input0 Input tensor containing the LHS reshaped matrix. Data type supported: F16/F32 (only F32 if rhs_info.export_to_cl_image = true). The number of dimensions for the LHS matrix must be less or equal than 4 - * @param[in] input1 Input tensor containing the RHS reshaped matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3 - * @param[in] input2 Input tensor info containing the bias matrix. Data type supported: same as @p input0. - * @param[in] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0 - * @param[in] alpha Weight of the matrix product - * @param[in] beta Weight of the matrix bias - * @param[in] lhs_info LHS matrix information used for reshaping the input0 tensor. Only the following values are supported: - * lhs_info.m0: 2,3,4,5,6,7,8 - * lhs_info.k0: 2,3,4,8,16 - * lhs_info.transpose: false - * @param[in] rhs_info RHS matrix information used for reshaping the input1 tensor. Only the following values are supported: - * rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true) - * rhs_info.k0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true) - * rhs_info.transpose: true - * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices - * - * @note lhs_info.k0 must be equal to rhs_info.k0 - * - * @return a status - */ - static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, - const GEMMKernelInfo &gemm_info); - - // Inherited methods overridden: - void run(const Window &window, cl::CommandQueue &queue) override; - -private: - const ICLTensor *_input0; - const ICLTensor *_input1; - const ICLTensor *_input2; - ICLTensor *_output; - bool _slide_matrix_b; - bool _reinterpret_output_as_3d; - bool _use_dummy_work_items; - bool _add_bias; - bool _broadcast_bias; - bool _export_to_cl_image; - unsigned int _k; -}; -} // namespace arm_compute -#endif /*ARM_COMPUTE_CLGEMMMATRIXMULTIPLYRESHAPEDKERNEL_H*/
\ No newline at end of file diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h b/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h deleted file mode 100644 index 5b96679a46..0000000000 --- a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h +++ /dev/null @@ -1,168 +0,0 @@ -/* - * Copyright (c) 2019-2020 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_CLGEMMMATRIXMULTIPLYRESHAPEDONLYRHSKERNEL_H -#define ARM_COMPUTE_CLGEMMMATRIXMULTIPLYRESHAPEDONLYRHSKERNEL_H - -#include "src/core/CL/ICLKernel.h" - -#include "arm_compute/core/KernelDescriptors.h" - -namespace arm_compute -{ -class ICLTensor; - -/** OpenCL kernel to multiply matrices when only the input matrix RHS (input1) has been reshaped - * - * @note The input matrix input1 must be reshaped through @ref CLGEMMReshapeRHSMatrixKernel - */ -class CLGEMMMatrixMultiplyReshapedOnlyRHSKernel : public ICLKernel -{ -public: - /** Default Constructor */ - CLGEMMMatrixMultiplyReshapedOnlyRHSKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLGEMMMatrixMultiplyReshapedOnlyRHSKernel(const CLGEMMMatrixMultiplyReshapedOnlyRHSKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLGEMMMatrixMultiplyReshapedOnlyRHSKernel &operator=(const CLGEMMMatrixMultiplyReshapedOnlyRHSKernel &) = delete; - /** Allow instances of this class to be moved */ - CLGEMMMatrixMultiplyReshapedOnlyRHSKernel(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel &&) = default; - /** Allow instances of this class to be moved */ - CLGEMMMatrixMultiplyReshapedOnlyRHSKernel &operator=(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel &&) = default; - /** Initialise the kernel's input and output. - * - * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function. - * Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer, - * the following conditions are required: - * -# rhs_info.n0 can only be 4, 8 and 16 - * -# rhs_info.k0 can only be 4, 8 and 16 - * -# Data type can only be F32 - * -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension - * -# The stride Y for the input1 should satisfy the OpenCL pitch alignment requirement - * -# input1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4) - * -# input1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT - * - * @param[in] input0 Input tensor containing the LHS matrix. Data type supported: F16/F32 (only F32 if rhs_info.export_to_cl_image = true). - * The number of dimensions for the LHS matrix must be less or equal than 4. - * @param[in] input1 Input tensor containing the RHS reshaped matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3. - * @param[in] input2 Input tensor containing the bias matrix. Data type supported: same as @p input0. - * @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0 - * @param[in] alpha Weight of the matrix product - * @param[in] beta Weight of the matrix bias - * @param[in] lhs_info LHS matrix information used to retrieve the number of rows to be processed by each thread. Only the following values are supported: - * lhs_info.m0: 1,2,3,4,5,6,7,8 - * @param[in] rhs_info RHS matrix information used for reshaping the input1 tensor. Only the following values are supported: - * rhs_info.k0: 2,3,4,8,16 - * rhs_info.n0: 2,3,4,8,16 - * rhs_info.transpose: true,false - * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices - */ - void configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, - const GEMMKernelInfo &gemm_info); - /** Initialise the kernel's input and output. - * - * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function. - * Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer, - * the following conditions are required: - * -# rhs_info.n0 can only be 4, 8 and 16 - * -# rhs_info.k0 can only be 4, 8 and 16 - * -# Data type can only be F32 - * -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension - * -# The stride Y for the input1 should satisfy the OpenCL pitch alignment requirement - * -# input1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4) - * -# input1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT - * - * @param[in] compile_context The compile context to be used. - * @param[in] input0 Input tensor containing the LHS matrix. Data type supported: F16/F32 (only F32 if rhs_info.export_to_cl_image = true). - * The number of dimensions for the LHS matrix must be less or equal than 4. - * @param[in] input1 Input tensor containing the RHS reshaped matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3. - * @param[in] input2 Input tensor containing the bias matrix. Data type supported: same as @p input0. - * @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0 - * @param[in] alpha Weight of the matrix product - * @param[in] beta Weight of the matrix bias - * @param[in] lhs_info LHS matrix information used to retrieve the number of rows to be processed by each thread. Only the following values are supported: - * lhs_info.m0: 1,2,3,4,5,6,7,8 - * @param[in] rhs_info RHS matrix information used for reshaping the input1 tensor. Only the following values are supported: - * rhs_info.k0: 2,3,4,8,16 - * rhs_info.n0: 2,3,4,8,16 - * rhs_info.transpose: true,false - * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices - */ - void configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, - const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, - const GEMMKernelInfo &gemm_info); - /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel - * - * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function. - * Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer, - * the following conditions are required: - * -# rhs_info.n0 can only be 4, 8 and 16 - * -# rhs_info.k0 can only be 4, 8 and 16 - * -# Data type can only be F32 - * -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension - * -# The stride Y for the input1 should satisfy the OpenCL pitch alignment requirement - * -# input1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4) - * -# input1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT - * - * @param[in] input0 Input tensor info for the LHS matrix. Data type supported: F16/F32 (only F32 if rhs_info.export_to_cl_image = true). - * The number of dimensions for the LHS matrix must be less or equal than 4. - * @param[in] input1 Input tensor info for the RHS reshaped matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3. - * @param[in] input2 Input tensor info containing the bias matrix. Data type supported: same as @p input0. - * @param[in] output Output tensor info. Data type supported: same as @p input0 - * @param[in] alpha Weight of the matrix product - * @param[in] beta Weight of the matrix bias - * @param[in] lhs_info LHS matrix information used to retrieve the number of rows to be processed by each thread. Only the following values are supported: - * lhs_info.m0: 1,2,3,4,5,6,7,8 - * @param[in] rhs_info RHS matrix information used for reshaping the input1 tensor. Only the following values are supported: - * rhs_info.k0: 2,3,4,8,16 - * rhs_info.n0: 2,3,4,8,16 - * rhs_info.transpose: true,false - * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices - * - * @return a status - */ - static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, - const GEMMKernelInfo &gemm_info); - - // Inherited methods overridden: - void run(const Window &window, cl::CommandQueue &queue) override; - -private: - const ICLTensor *_input0; - const ICLTensor *_input1; - const ICLTensor *_input2; - ICLTensor *_output; - bool _slide_matrix_b; - bool _reinterpret_input_as_3d; - bool _reinterpret_output_as_3d; - bool _use_dummy_work_items; - bool _add_bias; - bool _broadcast_bias; - bool _export_to_cl_image; - bool _has_pad_y; -}; -} // namespace arm_compute -#endif /*ARM_COMPUTE_CLGEMMMATRIXMULTIPLYRESHAPEDONLYRHSKERNEL_H*/ diff --git a/src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h b/src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h deleted file mode 100644 index 92202a26fc..0000000000 --- a/src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h +++ /dev/null @@ -1,105 +0,0 @@ -/* - * Copyright (c) 2018-2020 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_CLGEMMRESHAPELHSMATRIXKERNEL_H -#define ARM_COMPUTE_CLGEMMRESHAPELHSMATRIXKERNEL_H - -#include "src/core/CL/ICLKernel.h" - -namespace arm_compute -{ -class ICLTensor; - -/** OpenCL kernel to reshape the LHS matrix when performing the matrix multiplication. - * In particular, this function splits the input matrix in blocks of size M0xK0 (defined through GEMMLHSInfo) and - * stores each one in the output matrix unrolling the values - */ -class CLGEMMReshapeLHSMatrixKernel : public ICLKernel -{ -public: - /** Default constructor */ - CLGEMMReshapeLHSMatrixKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLGEMMReshapeLHSMatrixKernel(const CLGEMMReshapeLHSMatrixKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLGEMMReshapeLHSMatrixKernel &operator=(const CLGEMMReshapeLHSMatrixKernel &) = delete; - /** Allow instances of this class to be moved */ - CLGEMMReshapeLHSMatrixKernel(CLGEMMReshapeLHSMatrixKernel &&) = default; - /** Allow instances of this class to be moved */ - CLGEMMReshapeLHSMatrixKernel &operator=(CLGEMMReshapeLHSMatrixKernel &&) = default; - /** Initialise the kernel's input and output. - * - * @param[in] input Input tensor. Data types supported: All - * @param[out] output Output tensor. Data type supported: same as @p input - * @param[in] lhs_info LHS matrix information to be used for reshaping. This object contains all the necessary - * information to reshape the input tensor. Only the following values are supported: - * lhs_info.m0: 2,3,4,5,6,7,8 - * lhs_info.k0: 2,3,4,8,16 - * lhs_info.v0: greater than 0 - * lhs_info.transpose: true, false - * lhs_info.interleave: true, false - * @param[in] reinterpret_input_as_3d (Optional) True if the input has to be reinterpreted as 3D tensor - */ - void configure(const ICLTensor *input, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d = false); - /** Initialise the kernel's input and output. - * - * @param[in] compile_context The compile context to be used. - * @param[in] input Input tensor. Data types supported: All - * @param[out] output Output tensor. Data type supported: same as @p input - * @param[in] lhs_info LHS matrix information to be used for reshaping. This object contains all the necessary - * information to reshape the input tensor. Only the following values are supported: - * lhs_info.m0: 2,3,4,5,6,7,8 - * lhs_info.k0: 2,3,4,8,16 - * lhs_info.v0: greater than 0 - * lhs_info.transpose: true, false - * lhs_info.interleave: true, false - * @param[in] reinterpret_input_as_3d (Optional) True if the input has to be reinterpreted as 3D tensor - */ - void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d = false); - /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMReshapeLHSMatrixKernel - * - * @param[in] input Input tensor info. Data types supported: All - * @param[in] output Output tensor info which stores the interleaved matrix. Data type supported: same as @p input. - * @param[in] lhs_info LHS matrix information to be used for reshaping. This object contains all the necessary - * information to reshape the input tensor. Only the following values are supported: - * lhs_info.m0: 2,3,4,5,6,7,8 - * lhs_info.k0: 2,3,4,8,16 - * lhs_info.v0: greater than 0 - * lhs_info.transpose: true, false - * lhs_info.interleave: true, false - * @param[in] reinterpret_input_as_3d True if the input has to be reinterpreted as 3D tensor - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d); - - // Inherited methods overridden - void run(const Window &window, cl::CommandQueue &queue) override; - -private: - const ICLTensor *_input; - ICLTensor *_output; - bool _reinterpret_input_as_3d; -}; -} // namespace arm_compute -#endif /* ARM_COMPUTE_CLGEMMRESHAPELHSMATRIXKERNEL_H */
\ No newline at end of file diff --git a/src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h b/src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h deleted file mode 100644 index 911484ea76..0000000000 --- a/src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h +++ /dev/null @@ -1,135 +0,0 @@ -/* - * Copyright (c) 2018-2020 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_CLGEMMRESHAPERHSMATRIXKERNEL_H -#define ARM_COMPUTE_CLGEMMRESHAPERHSMATRIXKERNEL_H - -#include "src/core/CL/ICLKernel.h" - -namespace arm_compute -{ -class ICLTensor; - -/** OpenCL kernel to reshape the RHS matrix when performing the matrix multiplication - * In particular, this kernel splits the input matrix in blocks of size K0xN0 and stores each one in - * the output matrix unrolling the values */ -class CLGEMMReshapeRHSMatrixKernel : public ICLKernel -{ -public: - /** Default constructor */ - CLGEMMReshapeRHSMatrixKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLGEMMReshapeRHSMatrixKernel(const CLGEMMReshapeRHSMatrixKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLGEMMReshapeRHSMatrixKernel &operator=(const CLGEMMReshapeRHSMatrixKernel &) = delete; - /** Allow instances of this class to be moved */ - CLGEMMReshapeRHSMatrixKernel(CLGEMMReshapeRHSMatrixKernel &&) = default; - /** Allow instances of this class to be moved */ - CLGEMMReshapeRHSMatrixKernel &operator=(CLGEMMReshapeRHSMatrixKernel &&) = default; - /** Default destructor */ - ~CLGEMMReshapeRHSMatrixKernel() = default; - /** Initialise the kernel's input and output. - * - * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will guarantee the OpenCL pitch alignment for the output tensor, - * required to create a OpenCL image object from buffer in @ref CLGEMMMatrixMultiplyReshapedKernel and in @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel - * Since the OpenCL image object is created importing the OpenCL buffer, the following conditions are required: - * -# rhs_info.n0 can only be 4, 8 and 16 - * -# rhs_info.k0 can only be 4, 8 and 16 - * -# Data type can only be F32, F16 - * -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension - * -# output width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4) - * -# output (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT - * -# The output tensor should be only consumed by @ref CLGEMMMatrixMultiplyReshapedKernel or @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel - * - * @param[in] input Input tensor. Data types supported: All - * @param[out] output Output tensor. Data type supported: same as @p input - * @param[in] rhs_info RHS matrix information to be used for reshaping. This object contains all the necessary - * information to reshape the input tensor. Only the following values are supported: - * rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image == true) - * rhs_info.k0: 1,2,3,4,8,16 (k0 = 1 only if rhs_info.transpose = false), (only 4, 8 and 16 if rhs_info.export_to_cl_image == true) - * rhs_info.h0: greater than 0 - * rhs_info.transpose: true, false - * rhs_info.interleave: true, false - */ - void configure(const ICLTensor *input, ICLTensor *output, const GEMMRHSMatrixInfo &rhs_info); - /** Initialise the kernel's input and output. - * - * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will guarantee the OpenCL pitch alignment for the output tensor, - * required to create a OpenCL image object from buffer in @ref CLGEMMMatrixMultiplyReshapedKernel and in @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel - * Since the OpenCL image object is created importing the OpenCL buffer, the following conditions are required: - * -# rhs_info.n0 can only be 4, 8 and 16 - * -# rhs_info.k0 can only be 4, 8 and 16 - * -# Data type can only be F32, F16 - * -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension - * -# output width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4) - * -# output (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT - * -# The output tensor should be only consumed by @ref CLGEMMMatrixMultiplyReshapedKernel or @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel - * - * @param[in] compile_context The compile context to be used. - * @param[in] input Input tensor. Data types supported: All - * @param[out] output Output tensor. Data type supported: same as @p input - * @param[in] rhs_info RHS matrix information to be used for reshaping. This object contains all the necessary - * information to reshape the input tensor. Only the following values are supported: - * rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image == true) - * rhs_info.k0: 1,2,3,4,8,16 (k0 = 1 only if rhs_info.transpose = false), (only 4, 8 and 16 if rhs_info.export_to_cl_image == true) - * rhs_info.h0: greater than 0 - * rhs_info.transpose: true, false - * rhs_info.interleave: true, false - */ - void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMRHSMatrixInfo &rhs_info); - /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMReshapeRHSMatrixKernel - * - * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will guarantee the OpenCL pitch alignment for the output tensor, - * required to create a OpenCL image object from buffer in @ref CLGEMMMatrixMultiplyReshapedKernel and in @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel - * Since the OpenCL image object is created importing the OpenCL buffer, the following conditions are required: - * -# rhs_info.n0 can only be 4, 8 and 16 - * -# rhs_info.k0 can only be 4, 8 and 16 - * -# Data type can only be F32, F16 - * -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension - * -# output width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4) - * -# output (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT - * -# The output tensor should be only consumed by @ref CLGEMMMatrixMultiplyReshapedKernel or @ref CLGEMMMatrixMultiplyReshapedOnlyRHSKernel - * - * @param[in] input Input tensor info. Data types supported: All - * @param[in] output Output tensor info which stores the interleaved matrix. Data type supported: same as @p input. - * @param[in] rhs_info RHS matrix information to be used for reshaping. This object contains all the necessary - * information to reshape the input tensor. Only the following values are supported: - * rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image == true) - * rhs_info.k0: 1,2,3,4,8,16 (k0 = 1 only if rhs_info.transpose = false),(only 4, 8 and 16 if rhs_info.export_to_cl_image == true) - * rhs_info.h0: greater than 0 - * rhs_info.transpose: true, false - * rhs_info.interleave: true, false - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output, const GEMMRHSMatrixInfo &rhs_info); - - // Inherited methods overridden - void run(const Window &window, cl::CommandQueue &queue) override; - -private: - const ICLTensor *_input; - ICLTensor *_output; -}; -} // namespace arm_compute -#endif /* ARM_COMPUTE_CLGEMMRESHAPERHSMATRIXKERNEL_H */
\ No newline at end of file diff --git a/src/core/ITensorPack.cpp b/src/core/ITensorPack.cpp index 546f669985..9eaeece271 100644 --- a/src/core/ITensorPack.cpp +++ b/src/core/ITensorPack.cpp @@ -27,14 +27,23 @@ namespace arm_compute { +ITensorPack::ITensorPack(std::initializer_list<PackElement> l) + : _pack() +{ + for(auto &e : l) + { + _pack[e.id] = e; + } +} + void ITensorPack::add_tensor(int id, ITensor *tensor) { - _pack[id] = PackElement(tensor); + _pack[id] = PackElement(id, tensor); } void ITensorPack::add_tensor(int id, const ITensor *tensor) { - _pack[id] = PackElement(tensor); + _pack[id] = PackElement(id, tensor); } void ITensorPack::add_const_tensor(int id, const ITensor *tensor) diff --git a/src/core/gpu/cl/kernels/ClDirectConvolutionKernel.cpp b/src/core/gpu/cl/kernels/ClDirectConvolutionKernel.cpp index 18d648d2f2..0a5101f564 100644 --- a/src/core/gpu/cl/kernels/ClDirectConvolutionKernel.cpp +++ b/src/core/gpu/cl/kernels/ClDirectConvolutionKernel.cpp @@ -35,7 +35,7 @@ #include "src/core/AccessWindowStatic.h" #include "src/core/CL/CLUtils.h" #include "src/core/CL/CLValidate.h" -#include "src/core/CL/gemm/CLGEMMHelpers.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "support/Cast.h" @@ -416,7 +416,7 @@ void ClDirectConvolutionKernel::configure(const CLCompileContext &compile_contex const unsigned int n0 = win_config.second.x().step(); const unsigned int m0 = win_config.second.y().step(); - const unsigned int k0 = adjust_vec_size(is_data_type_quantized(data_type)? 16u : 8u, src->dimension(channel_idx)); + const unsigned int k0 = adjust_vec_size(is_data_type_quantized(data_type) ? 16u : 8u, src->dimension(channel_idx)); const unsigned int partial_store_n0 = dst->dimension(channel_idx) % n0; const unsigned int pad_left = conv_info.pad_left(); const unsigned int pad_top = conv_info.pad_top(); @@ -425,7 +425,7 @@ void ClDirectConvolutionKernel::configure(const CLCompileContext &compile_contex // Update the padding for the weights tensor if we can export to cl_image if(export_to_cl_image) { - arm_compute::cl_gemm::update_padding_for_cl_image(weights); + gemm::update_padding_for_cl_image(weights); } if(biases != nullptr) diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp index 479c06330d..817a105b14 100644 --- a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp +++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp @@ -21,7 +21,7 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h" +#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" @@ -36,54 +36,54 @@ #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "src/core/utils/helpers/float_ops.h" +#include "support/Cast.h" #include "support/StringSupport.h" -#include <set> -#include <string> - namespace arm_compute { -using namespace arm_compute::misc::shape_calculator; - +namespace opencl +{ +namespace kernels +{ namespace { using ElementsProcessed = Steps; -inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float beta, +inline Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision) { - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input0); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((fp_mixed_precision && (input0->data_type() != DataType::F16)), "Mixed precision floating point is supported only for F16 data"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3"); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src0); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((fp_mixed_precision && (src0->data_type() != DataType::F16)), "Mixed precision floating point is supported only for F16 data"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_interleaved_transposed && reshape_info.reinterpret_input_as_3d(), "The input tensor cannot be reinterpreted as 3D if is_interleaved_transposed is true"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 2 && reshape_info.reinterpret_input_as_3d(), "The input1 tensor cannot have more than 2 dimensions if input0 has to be reinterpreted as 3D"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((reshape_info.reinterpret_input_as_3d() || reshape_info.depth_output_gemm3d() != 0) && (input2 != nullptr) + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 2 && reshape_info.reinterpret_input_as_3d(), "The src1 tensor cannot have more than 2 dimensions if src0 has to be reinterpreted as 3D"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((reshape_info.reinterpret_input_as_3d() || reshape_info.depth_output_gemm3d() != 0) && (src2 != nullptr) && (!reshape_info.broadcast_bias()), - "Bias addition only supported with broadcast mode in case the input or output has to be reinterpreted as 3D"); + "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D"); if(!is_interleaved_transposed) { - ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1)); + ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != src1->dimension(1)); - if(input2 != nullptr && !(helpers::float_ops::is_zero(beta))) + if(src2 != nullptr && !(helpers::float_ops::is_zero(beta))) { - const unsigned int m = reshape_info.reinterpret_input_as_3d() ? input0->dimension(1) * input0->dimension(2) : input0->dimension(1); - const unsigned int n = input1->dimension(0); - const unsigned int input2_dim0 = input2->dimension(0); - const unsigned int input2_dim1 = input2->dimension(1); + const unsigned int m = reshape_info.reinterpret_input_as_3d() ? src0->dimension(1) * src0->dimension(2) : src0->dimension(1); + const unsigned int n = src1->dimension(0); + const unsigned int src2_dim0 = src2->dimension(0); + const unsigned int src2_dim1 = src2->dimension(1); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1); if(reshape_info.broadcast_bias()) { - ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); } else { - ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix"); } } } @@ -96,7 +96,7 @@ inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *i const int k = reshape_info.k(); const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width(); const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height(); - rhs_info.n0 = max_cl_vector_width / input1->element_size(); + rhs_info.n0 = max_cl_vector_width / src1->element_size(); rhs_info.k0 = 1; rhs_info.h0 = mult_transpose1xW_width; rhs_info.interleave = false; @@ -107,51 +107,51 @@ inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *i lhs_info.interleave = true; lhs_info.transpose = true; - TensorShape tensor_shape0{ input0->tensor_shape() }; + TensorShape tensor_shape0{ src0->tensor_shape() }; tensor_shape0.set(0, k); tensor_shape0.set(1, m); - TensorShape tensor_shape1{ input1->tensor_shape() }; + TensorShape tensor_shape1{ src1->tensor_shape() }; tensor_shape1.set(0, n); tensor_shape1.set(1, k); - const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0); - const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1); + const TensorInfo tensor_info0 = src0->clone()->set_tensor_shape(tensor_shape0); + const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1); - const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(compute_lhs_reshaped_shape(tensor_info0, lhs_info)); - const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info)); + const TensorInfo tensor_info_reshaped0 = src0->clone()->set_tensor_shape(misc::shape_calculator::compute_lhs_reshaped_shape(tensor_info0, lhs_info)); + const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(tensor_info1, rhs_info)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src0, &tensor_info_reshaped0); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1); - if(input2 != nullptr && !(helpers::float_ops::is_zero(beta))) + if(src2 != nullptr && !(helpers::float_ops::is_zero(beta))) { - const unsigned int input2_dim0 = input2->dimension(0); - const unsigned int input2_dim1 = input2->dimension(1); + const unsigned int src2_dim0 = src2->dimension(0); + const unsigned int src2_dim1 = src2->dimension(1); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1); if(reshape_info.broadcast_bias()) { - ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); } else { - ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix"); } } } - if(output->total_size() != 0) + if(dst->total_size() != 0) { - const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output); + const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, is_interleaved_transposed, reshape_info)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst); } return Status{}; } -inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, +inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, ElementsProcessed &num_elements_processed) { @@ -160,13 +160,13 @@ inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *inpu Window win{}; Window win_out{}; - const DataType data_type = input0->data_type(); + const DataType data_type = src0->data_type(); unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0]; unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1]; bool reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d(); bool reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0); - // In case both input and output have to be reinterpreted as 3D tensors, + // In case both input and dst have to be reinterpreted as 3D tensors, // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. if(reinterpret_input_as_3d == reinterpret_output_as_3d) { @@ -174,16 +174,16 @@ inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *inpu reinterpret_output_as_3d = false; } - // Output tensor auto inizialitation if not yet initialized - auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info))); + // dst tensor auto inizialitation if not yet initialized + auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, is_interleaved_transposed, reshape_info))); - TensorInfo tmp_info(*output); + TensorInfo tmp_info(*dst); if(reinterpret_output_as_3d) { - // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, + // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, // the window needs to be constructed on the 2D collapsed version of the tensor - TensorShape tmp_shape(output->tensor_shape()); + TensorShape tmp_shape(dst->tensor_shape()); tmp_shape.collapse(2U, 1U); tmp_info.set_tensor_shape(tmp_shape); } @@ -198,63 +198,63 @@ inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *inpu num_elems_processed_per_iteration_y = 4; win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - if(input2 != nullptr) + if(src2 != nullptr) { const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x; const int bias_processed_per_iteration_y = reshape_info.broadcast_bias() ? 1 : num_elems_processed_per_iteration_y; - AccessWindowStatic input2_access(input2, 0, 0, - ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x), - ceil_to_multiple(input2->dimension(1), bias_processed_per_iteration_y)); + AccessWindowStatic src2_access(src2, 0, 0, + ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x), + ceil_to_multiple(src2->dimension(1), bias_processed_per_iteration_y)); - window_changed = update_window_and_padding(win, input2_access); // window used by the execute_window_loop + window_changed = update_window_and_padding(win, src2_access); // window used by the execute_window_loop } } else // The input tensors have not been reshaped { - // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor. num_elems_processed_per_iteration_x is set up for the default case. + // Special case for 1xN, 2xN, 3xN and 4xN src0 tensor. num_elems_processed_per_iteration_x is set up for the default case. num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type); - num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4); + num_elems_processed_per_iteration_y = std::min(static_cast<int>(dst->dimension(1)), 4); // Create kernels according to the architecture, data type and input size. GPUTarget arch_target = get_arch_from_target(gpu_target); if(arch_target == GPUTarget::BIFROST && data_type == DataType::F32) { - num_elems_processed_per_iteration_x = (input1->dimension(0) <= 1000 && input0->num_dimensions() == 1) ? 2 : 4; + num_elems_processed_per_iteration_x = (src1->dimension(0) <= 1000 && src0->num_dimensions() == 1) ? 2 : 4; } // Configure window win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - AccessWindowStatic input0_access(input0, 0, 0, input0->dimension(0), input0->dimension(1)); - AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1)); - AccessWindowStatic output_access(output, 0, 0, - output->dimension(0), - output->dimension(1)); - - if(input2 != nullptr) + win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + AccessWindowStatic src0_access(src0, 0, 0, src0->dimension(0), src0->dimension(1)); + AccessWindowStatic src1_access(src1, 0, 0, ceil_to_multiple(src1->dimension(0), num_elems_processed_per_iteration_x), src1->dimension(1)); + AccessWindowStatic dst_access(dst, 0, 0, + dst->dimension(0), + dst->dimension(1)); + + if(src2 != nullptr) { const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x; - AccessWindowStatic input2_access(input2, 0, 0, - ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x), - input2->dimension(1)); + AccessWindowStatic src2_access(src2, 0, 0, + ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x), + src2->dimension(1)); - window_changed = update_window_and_padding(win, input0_access, input1_access, input2_access) || // window used by the execute_window_loop - update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor + window_changed = update_window_and_padding(win, src0_access, src1_access, src2_access) || // window used by the execute_window_loop + update_window_and_padding(win_out, dst_access); // window used to update the padding requirements of dst tensor } else { - window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop - update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor + window_changed = update_window_and_padding(win, src0_access, src1_access) || // window used by the execute_window_loop + update_window_and_padding(win_out, dst_access); // window used to update the padding requirements of dst tensor } } // Collapse along the Z direction // This collapse needs to be here in order to tune the Z dimension of LWS Window collapsed = win; - const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u); + const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u); collapsed = win.collapse(win, dimension_to_collapse); Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; @@ -262,40 +262,23 @@ inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *inpu } } // namespace -CLGEMMMatrixMultiplyKernel::CLGEMMMatrixMultiplyKernel() - : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _add_bias(false), - _broadcast_bias(false) -{ -} - -void CLGEMMMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, - bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision, const ActivationLayerInfo &activation_info) -{ - configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, is_interleaved_transposed, reshape_info, fp_mixed_precision, activation_info); -} - -void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, +void ClGemmMatrixMultiplyKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision, const ActivationLayerInfo &activation_info) { - ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); + ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); // Perform validate step - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr) ? input2->info() : nullptr, output->info(), beta, + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, beta, is_interleaved_transposed, reshape_info, fp_mixed_precision)); - auto padding_info = is_interleaved_transposed ? get_padding_info({ input0, input1, output }) : get_padding_info({ input0, output }); + auto padding_info = is_interleaved_transposed ? get_padding_info({ src0, src1, dst }) : get_padding_info({ src0, dst }); - _input0 = input0; - _input1 = input1; - _input2 = helpers::float_ops::is_zero(beta) ? nullptr : input2; - _output = output; _reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d(); _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0); - _add_bias = _input2 != nullptr; - _broadcast_bias = reshape_info.broadcast_bias(); + _add_bias = src2 != nullptr; - // In case both input and output have to be reinterpreted as 3D tensors, + // In case both input and dst have to be reinterpreted as 3D tensors, // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. if(_reinterpret_input_as_3d == _reinterpret_output_as_3d) { @@ -304,11 +287,11 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte } // Check if we need to slide the matrix B - const unsigned int num_dimensions_input0 = _reinterpret_input_as_3d ? _input0->info()->num_dimensions() - 1 : _input0->info()->num_dimensions(); + const unsigned int num_dimensions_src0 = _reinterpret_input_as_3d ? src0->num_dimensions() - 1 : src0->num_dimensions(); - _slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0); + _slide_matrix_b = (src1->num_dimensions() >= num_dimensions_src0); - const DataType data_type = input0->info()->data_type(); + const DataType data_type = src0->data_type(); // Get target architecture GPUTarget gpu_target = get_target(); @@ -316,19 +299,19 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte ElementsProcessed num_elements_processed{}; // Configure kernel window - auto win_config = validate_and_configure_window(input0->info(), input1->info(), (input2 != nullptr) ? input2->info() : nullptr, output->info(), beta, is_interleaved_transposed, reshape_info, + auto win_config = validate_and_configure_window(src0, src1, src2, dst, beta, is_interleaved_transposed, reshape_info, gpu_target, num_elements_processed); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); // If _reinterpret_input_as_3d = _reinterpret_output_as_3d = true, both will be turned off (false) // in which case we will dispatch a batched-GEMM to reduce the complexity of the address calculation within the OpenCL kernel. - // This means that the actual m used by the kernel is given by output->info()->dimension(1) - const unsigned int internal_m = _reinterpret_output_as_3d ? output->info()->dimension(1) * output->info()->dimension(2) : output->info()->dimension(1); - const unsigned int n = output->info()->dimension(0); + // This means that the actual m used by the kernel is given by dst->dimension(1) + const unsigned int internal_m = _reinterpret_output_as_3d ? dst->dimension(1) * dst->dimension(2) : dst->dimension(1); + const unsigned int n = dst->dimension(0); - const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? output->info()->dimension(1) : input0->info()->dimension(1); - const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? output->info()->dimension(2) : input0->info()->dimension(2); + const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(1) : src0->dimension(1); + const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(2) : src0->dimension(2); const unsigned int m0 = num_elements_processed.y(); const unsigned int n0 = num_elements_processed.x(); @@ -341,18 +324,18 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte CLBuildOptions build_opts; build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha)); - build_opts.add_option_if(_input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta)); + build_opts.add_option_if(src2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta)); build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA"); build_opts.add_option_if(reshape_info.broadcast_bias(), "-DBROADCAST_BIAS"); build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D"); build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D"); build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(h_gemm_3d)); build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(d_gemm_3d)); - build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2))); + build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2))); build_opts.add_option_if(activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(activation_info.activation()))); build_opts.add_option_if(activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(activation_info.a())); build_opts.add_option_if(activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(activation_info.b())); - build_opts.add_option("-DIN1_DIM_X=" + support::cpp11::to_string(input1->info()->dimension(0))); + build_opts.add_option("-DIN1_DIM_X=" + support::cpp11::to_string(src1->dimension(0))); const bool is_bifrost = get_arch_from_target(gpu_target) == GPUTarget::BIFROST; @@ -364,7 +347,7 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte build_opts.add_option("-DM=" + support::cpp11::to_string(internal_m)); build_opts.add_option("-DN=" + support::cpp11::to_string(n)); - build_opts.add_option("-DK=" + support::cpp11::to_string(input1->info()->dimension(0) / (n0 * mult_transpose1xW_width))); + build_opts.add_option("-DK=" + support::cpp11::to_string(src1->dimension(0) / (n0 * mult_transpose1xW_width))); build_opts.add_option("-DH0=" + support::cpp11::to_string(mult_transpose1xW_width)); build_opts.add_option("-DV0=" + support::cpp11::to_string(mult_interleave4x4_height)); build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0)); @@ -387,7 +370,7 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte else // The input tensors have not been reshaped { build_opts.add_option("-DN=" + support::cpp11::to_string(n)); - build_opts.add_option("-DK=" + support::cpp11::to_string(input0->info()->dimension(0))); + build_opts.add_option("-DK=" + support::cpp11::to_string(src0->dimension(0))); build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); build_opts.add_option("-DM0=" + support::cpp11::to_string(m0)); build_opts.add_option("-DN0=" + support::cpp11::to_string(n0)); @@ -399,7 +382,7 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte { kernel_name = "gemm_mm_floating_point"; - if(input0->info()->num_dimensions() != 1) + if(src0->num_dimensions() != 1) { kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost"; if(fp_mixed_precision && data_type == DataType::F16) @@ -408,10 +391,10 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte kernel_name += "_acc32"; } } - else if(input1->info()->dimension(0) <= 1000 && data_type == DataType::F32) + else if(src1->dimension(0) <= 1000 && data_type == DataType::F32) { - // The first kernel is optimized for the case of 1000 or less output elements (e.g. FC8 of AlexNet and VGG-16, and - // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 output elements (e.g. + // The first kernel is optimized for the case of 1000 or less dst elements (e.g. FC8 of AlexNet and VGG-16, and + // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 dst elements (e.g. // FC6 and FC7 of AlexNet and VGG-16). kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost_1000"; } @@ -432,37 +415,37 @@ void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_conte _config_id = "gemm_"; _config_id += (is_interleaved_transposed ? "reshaped_" : ""); _config_id += (_add_bias ? "add_bias_" : ""); - _config_id += (_broadcast_bias ? "broadcast_bias_" : ""); + _config_id += (reshape_info.broadcast_bias() ? "broadcast_bias_" : ""); _config_id += (fp_mixed_precision ? "fp_mixed_" : ""); _config_id += (_reinterpret_input_as_3d ? "3di_" : ""); _config_id += (_reinterpret_output_as_3d ? "3do_" : ""); - _config_id += lower_string(string_from_data_type(input0->info()->data_type())); + _config_id += lower_string(string_from_data_type(src0->data_type())); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(1)); + _config_id += support::cpp11::to_string(dst->dimension(1)); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(0)); + _config_id += support::cpp11::to_string(dst->dimension(0)); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(2)); + _config_id += support::cpp11::to_string(dst->dimension(2)); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(3)); + _config_id += support::cpp11::to_string(dst->dimension(3)); _config_id += "_"; - _config_id += (is_interleaved_transposed ? support::cpp11::to_string(input1->info()->dimension(0)) : support::cpp11::to_string(input1->info()->dimension(1))); + _config_id += (is_interleaved_transposed ? support::cpp11::to_string(src1->dimension(0)) : support::cpp11::to_string(src1->dimension(1))); ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); } -Status CLGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, +Status ClGemmMatrixMultiplyKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, bool fp_mixed_precision, const ActivationLayerInfo &activation_info) { // Note: num_elements_processed will be set in validate_and_configure_window() ElementsProcessed num_elements_processed{}; ARM_COMPUTE_UNUSED(alpha); ARM_COMPUTE_UNUSED(activation_info); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, beta, is_interleaved_transposed, reshape_info, fp_mixed_precision)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), - input1->clone().get(), - (input2 != nullptr) ? input2->clone().get() : nullptr, - output->clone().get(), + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, src2, dst, beta, is_interleaved_transposed, reshape_info, fp_mixed_precision)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src0->clone().get(), + src1->clone().get(), + (src2 != nullptr) ? src2->clone().get() : nullptr, + dst->clone().get(), beta, is_interleaved_transposed, reshape_info, @@ -473,15 +456,23 @@ Status CLGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITe return Status{}; } -void CLGEMMMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue &queue) +void ClGemmMatrixMultiplyKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); - if(_input1->info()->num_dimensions() < 3) + const auto src0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0)); + const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); + const auto src2 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); + auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); + + ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_ERROR_ON(_add_bias && src2 == nullptr); + + if(src1->info()->num_dimensions() < 3) { // The stride_z for matrix B must be zero if we do not slice - ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0); + ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0); } Window slice = window.first_slice_window_3D(); @@ -496,15 +487,15 @@ void CLGEMMMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue &que { // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + num_arguments_bias; - const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->info()->padding().bottom; + const unsigned int total_cross_plane_pad = src0->info()->padding().top + src0->info()->padding().bottom; _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad)); } if(_reinterpret_output_as_3d) { - // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor + // Pass bottom paddings to the kernel if the dst has to be reinterpreted as 3D tensor const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0) + num_arguments_bias; - const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom; + const unsigned int total_cross_plane_pad = dst->info()->padding().top + dst->info()->padding().bottom; _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad)); } @@ -519,22 +510,24 @@ void CLGEMMMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue &que } unsigned int idx = 0; - add_2D_tensor_argument(idx, _input0, slice); - add_2D_tensor_argument(idx, _input1, slice_b); + add_2D_tensor_argument(idx, src0, slice); + add_2D_tensor_argument(idx, src1, slice_b); if(_add_bias) { - add_2D_tensor_argument(idx, _input2, slice); + add_2D_tensor_argument(idx, src2, slice); } - add_2D_tensor_argument(idx, _output, slice); - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2])); - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2])); + add_2D_tensor_argument(idx, dst, slice); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[2])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[2])); if(_add_bias) { - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input2->info()->strides_in_bytes()[2])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src2->info()->strides_in_bytes()[2])); } - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[2])); enqueue(queue, *this, slice, lws_hint()); } while(window.slide_window_slice_3D(slice)); } +} // namespace kernels +} // namespace opencl } // namespace arm_compute diff --git a/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h new file mode 100644 index 0000000000..c1601335ee --- /dev/null +++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h @@ -0,0 +1,88 @@ +/* + * Copyright (c) 2017-2021 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_CL_GEMM_MATRIXMULTIPLY_KERNEL_H +#define ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_KERNEL_H + +#include "src/core/common/Macros.h" +#include "src/core/gpu/cl/ClCompileContext.h" +#include "src/core/gpu/cl/IClKernel.h" + +namespace arm_compute +{ +namespace opencl +{ +namespace kernels +{ +/** OpenCL kernel to multiply two input matrices "A" and "B" and add a martix "C" if provided. All elements of the output matrix will be multiplied by alpha. In case matrix C is passed, it will be added to the previous result. + * For the matrix C, the broadcast addition is supported if the flag "broadcast_bias" is set in the GEMMReshapeInfo object + * + * @note If the input tensors @p src0 and @p src1 have been reshaped respectively with @ref ClGemmReshapeLhsMatrixKernel" and @ref ClGemmReshapeRhsMatrixKernel, + * the flag @p is_interleaved_transposed must be set to true + * + * @attention @p src1 tensor must have at least 2 dimensions (matrix) + */ +class ClGemmMatrixMultiplyKernel : public IClKernel +{ +public: + ClGemmMatrixMultiplyKernel() = default; + ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClGemmMatrixMultiplyKernel); + /** Initialise the kernel's input, output and alpha + * + * @param[in] compile_context The compile context to be used. + * @param[in] src0 Input tensor containing the Matrix A. Data types supported: F16/F32 + * @param[in] src1 Input tensor containing the Matrix B. Data type supported: same as @p src0 + * @param[in] src2 Input tensor containing the Matrix C (bias). Can be nullptr. Data type supported: same as @p src0 + * @param[out] dst Output tensor to store the result of matrix multiplication. Data type supported: same as @p src0 + * @param[in] alpha Weight of the matrix product + * @param[in] beta (Optional) Weight of vector C. Default value is 0. Only beta = 1 is currently supported. + * @param[in] is_interleaved_transposed (Optional) True if input0 and input1 have been reshaped respectively using @ref ClGemmReshapeLhsMatrixKernel and @ref ClGemmReshapeRhsMatrixKernel + * @param[in] reshape_info (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped + * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy + * @param[in] activation_info (Optional) Activation to apply after the matrix multiplication + * + */ + void configure(const ClCompileContext &compile_context, ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta = 0.f, + bool is_interleaved_transposed = true, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo(), bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo()); + /** Static function to check if given info will lead to a valid configuration + * + * Similar to @ref ClGemmMatrixMultiplyKernel::configure() + * + * @return a status + */ + static Status validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, + bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo()); + + // Inherited methods overridden: + void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override; + +public: + bool _slide_matrix_b{ true }; + bool _reinterpret_input_as_3d{ false }; + bool _reinterpret_output_as_3d{ false }; + bool _add_bias{ false }; +}; +} // namespace kernels +} // namespace opencl +} // namespace arm_compute +#endif /* ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_KERNEL_H */ diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp index 1fe298c0a1..97d64c433c 100644 --- a/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp +++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp @@ -21,7 +21,7 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h" +#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" @@ -36,37 +36,36 @@ #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "src/core/utils/helpers/float_ops.h" +#include "support/Cast.h" #include "support/StringSupport.h" -#include <cstddef> -#include <cstdint> -#include <tuple> - -using namespace arm_compute::misc::shape_calculator; - namespace arm_compute { +namespace opencl +{ +namespace kernels +{ namespace { using ElementsProcessed = Steps; -Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, +Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) { ARM_COMPUTE_UNUSED(alpha); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3"); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.k0 & (rhs_info.k0 - 1)) && rhs_info.k0 != 3), "Only 2,3,4,8,16 are supported for k0"); ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.k0 > 16); ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 1 || lhs_info.m0 > 8); ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3), "Only 2,3,4,8,16 are supported for n0"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (input2 != nullptr) + ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (src2 != nullptr) && (!gemm_info.broadcast_bias), - "Bias addition only supported with broadcast mode in case the input or output has to be reinterpreted as 3D"); + "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision, "Mixed precision not supported"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_info.export_to_cl_image, "Export to CLImage not supported for GEMM native"); @@ -78,45 +77,45 @@ Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, ARM_COMPUTE_UNUSED(n); ARM_COMPUTE_UNUSED(k); - ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != k); - ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != n); - ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(1) != k); + ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != k); + ARM_COMPUTE_RETURN_ERROR_ON(src1->dimension(0) != n); + ARM_COMPUTE_RETURN_ERROR_ON(src1->dimension(1) != k); if(gemm_info.reinterpret_input_as_3d) { - ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != m); + ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) * src0->dimension(2) != m); } else { - ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != m); + ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) != m); } - if(input2 != nullptr && !(helpers::float_ops::is_zero(beta))) + if(src2 != nullptr && !(helpers::float_ops::is_zero(beta))) { - const unsigned int input2_dim0 = input2->dimension(0); - const unsigned int input2_dim1 = input2->dimension(1); + const unsigned int src2_dim0 = src2->dimension(0); + const unsigned int src2_dim1 = src2->dimension(1); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1); if(gemm_info.broadcast_bias) { - ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); } else { - ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix"); } } - if(output->total_size() != 0) + if(dst->total_size() != 0) { - const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output); + const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst); } return Status{}; } -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info, ElementsProcessed &num_elements_processed) { @@ -129,23 +128,23 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe Window win_out{}; bool window_changed = false; - // In case both input and output have to be reinterpreted as 3D tensors, + // In case both input and dst have to be reinterpreted as 3D tensors, // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. if(reinterpret_input_as_3d == reinterpret_output_as_3d) { reinterpret_output_as_3d = false; } - // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info))); + // dst tensor auto initialization if not yet initialized + auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info))); - TensorInfo tmp_info(*output); + TensorInfo tmp_info(*dst); if(reinterpret_output_as_3d) { - // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, + // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, // the window needs to be constructed on the 2D collapsed version of the tensor - TensorShape tmp_shape(output->tensor_shape()); + TensorShape tmp_shape(dst->tensor_shape()); tmp_shape.collapse(2U, 1U); tmp_info.set_tensor_shape(tmp_shape); } @@ -155,39 +154,39 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe num_elems_processed_per_iteration_y = lhs_info.m0; win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - - AccessWindowStatic input0_access(input0, 0, 0, - input0->dimension(0), - input0->dimension(1)); - AccessWindowStatic input1_access(input1, 0, 0, - ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), - input1->dimension(1)); - AccessWindowStatic output_access(output, 0, 0, - output->dimension(0), - output->dimension(1)); - - if(input2 != nullptr) + win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + + AccessWindowStatic src0_access(src0, 0, 0, + src0->dimension(0), + src0->dimension(1)); + AccessWindowStatic src1_access(src1, 0, 0, + ceil_to_multiple(src1->dimension(0), num_elems_processed_per_iteration_x), + src1->dimension(1)); + AccessWindowStatic dst_access(dst, 0, 0, + dst->dimension(0), + dst->dimension(1)); + + if(src2 != nullptr) { const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x; - AccessWindowStatic input2_access(input2, 0, 0, - ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x), - input2->dimension(1)); + AccessWindowStatic src2_access(src2, 0, 0, + ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x), + src2->dimension(1)); - window_changed = update_window_and_padding(win, input0_access, input1_access, input2_access) || // window used by the execute_window_loop - update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor + window_changed = update_window_and_padding(win, src0_access, src1_access, src2_access) || // window used by the execute_window_loop + update_window_and_padding(win_out, dst_access); // window used to update the padding requirements of dst tensor } else { - window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop - update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor + window_changed = update_window_and_padding(win, src0_access, src1_access) || // window used by the execute_window_loop + update_window_and_padding(win_out, dst_access); // window used to update the padding requirements of dst tensor } // Collapse along the Z direction // This collapse needs to be here in order to tune the Z dimension of LWS Window collapsed = win; - const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u); + const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u); collapsed = win.collapse(win, dimension_to_collapse); Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; @@ -195,40 +194,22 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe } } // namespace -CLGEMMMatrixMultiplyNativeKernel::CLGEMMMatrixMultiplyNativeKernel() - : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _use_dummy_work_items(false), - _add_bias(false), _broadcast_bias(false) -{ -} - -void CLGEMMMatrixMultiplyNativeKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, - const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) -{ - configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info); -} - -void CLGEMMMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, +void ClGemmMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) { - ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); + ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr ? input2->info() : nullptr), output->info(), alpha, beta, lhs_info, rhs_info, gemm_info)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info)); - auto padding_info = get_padding_info({ input0, output }); - _input0 = input0; - _input1 = input1; - _input2 = helpers::float_ops::is_zero(beta) ? nullptr : input2; - _output = output; + auto padding_info = get_padding_info({ src0, dst }); _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d; _reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0; _use_dummy_work_items = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device()); - _add_bias = _input2 != nullptr; - _broadcast_bias = gemm_info.broadcast_bias; + _add_bias = src2 != nullptr; - // In case both input and output have to be reinterpreted as 3D tensors, + // In case both input and dst have to be reinterpreted as 3D tensors, // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. if(_reinterpret_input_as_3d == _reinterpret_output_as_3d) { @@ -237,23 +218,23 @@ void CLGEMMMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile } // Check if we need to slide the matrix B - const unsigned int num_dimensions_input0 = _input0->info()->num_dimensions(); - _slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0); + const unsigned int num_dimensions_src0 = src0->num_dimensions(); + _slide_matrix_b = (src1->num_dimensions() >= num_dimensions_src0); ElementsProcessed num_elements_processed{}; // Configure kernel window - auto win_config = validate_and_configure_window(input0->info(), input1->info(), input2 != nullptr ? input2->info() : nullptr, output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed); + auto win_config = validate_and_configure_window(src0, src1, src2 != nullptr ? src2 : nullptr, dst, lhs_info, rhs_info, gemm_info, num_elements_processed); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - ICLKernel::configure_internal(win_config.second); + IClKernel::configure_internal(win_config.second); // If _reinterpret_input_as_3d = _reinterpret_output_as_3d = true, // we will dispatch a batched-GEMM to reduce the complexity of the address calculation within the OpenCL kernel. - // This means that the actual m used by the kernel is given by output->info()->dimension(1) and not by gemm_info.m - const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : output->info()->dimension(1); + // This means that the actual m used by the kernel is given by dst->dimension(1) and not by gemm_info.m + const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : dst->dimension(1); - const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? output->info()->dimension(1) : input0->info()->dimension(1); - const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? output->info()->dimension(2) : input0->info()->dimension(2); + const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(1) : src0->dimension(1); + const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(2) : src0->dimension(2); // Calculate partial (store instead of load) M0 and partial N0 for the partial blocks at the end of a row/column if any. This is to avoid padding. const unsigned int partial_store_m0 = internal_m % lhs_info.m0; @@ -265,16 +246,16 @@ void CLGEMMMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile // Create build options CLBuildOptions build_opts; - build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type())); + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src0->data_type())); build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha)); - build_opts.add_option_if(_input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta)); + build_opts.add_option_if(src2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta)); build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA"); build_opts.add_option_if(gemm_info.broadcast_bias, "-DBROADCAST_BIAS"); build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D"); build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D"); build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(h_gemm_3d)); build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(d_gemm_3d)); - build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2))); + build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2))); build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS"); build_opts.add_option("-DM=" + support::cpp11::to_string(internal_m)); build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n)); @@ -297,19 +278,19 @@ void CLGEMMMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile _config_id = kernel_name; _config_id += "_"; _config_id += (_add_bias ? "add_bias_" : ""); - _config_id += (_broadcast_bias ? "broadcast_bias_" : ""); + _config_id += (gemm_info.broadcast_bias ? "broadcast_bias_" : ""); _config_id += (_reinterpret_input_as_3d ? "3di_" : ""); _config_id += (_reinterpret_output_as_3d ? "3do_" : ""); _config_id += (gemm_info.activation_info.enabled() ? "fused_activation_" : ""); - _config_id += lower_string(string_from_data_type(input0->info()->data_type())); + _config_id += lower_string(string_from_data_type(src0->data_type())); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(1)); + _config_id += support::cpp11::to_string(dst->dimension(1)); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(0)); + _config_id += support::cpp11::to_string(dst->dimension(0)); _config_id += "_"; _config_id += support::cpp11::to_string(gemm_info.k); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(2)); + _config_id += support::cpp11::to_string(dst->dimension(2)); _config_id += "_"; _config_id += support::cpp11::to_string(lhs_info.m0); _config_id += "_"; @@ -320,16 +301,16 @@ void CLGEMMMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); } -Status CLGEMMMatrixMultiplyNativeKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, +Status ClGemmMatrixMultiplyNativeKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) { ElementsProcessed num_elements_processed{}; - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), - input1->clone().get(), - input2 != nullptr ? input2->clone().get() : nullptr, - output->clone().get(), + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src0->clone().get(), + src1->clone().get(), + src2 != nullptr ? src2->clone().get() : nullptr, + dst->clone().get(), lhs_info, rhs_info, gemm_info, @@ -339,15 +320,23 @@ Status CLGEMMMatrixMultiplyNativeKernel::validate(const ITensorInfo *input0, con return Status{}; } -void CLGEMMMatrixMultiplyNativeKernel::run(const Window &window, cl::CommandQueue &queue) +void ClGemmMatrixMultiplyNativeKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); - if(_input1->info()->num_dimensions() < 3) + const auto src0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0)); + const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); + const auto src2 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); + auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); + + ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_ERROR_ON(_add_bias && src2 == nullptr); + + if(src1->info()->num_dimensions() < 3) { // The stride_z for matrix B must be zero if we do not slice - ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0); + ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0); } Window slice = window.first_slice_window_3D(); @@ -368,13 +357,13 @@ void CLGEMMMatrixMultiplyNativeKernel::run(const Window &window, cl::CommandQueu { idx0 = 3 * num_arguments_per_2D_tensor() + 3; } - const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->info()->padding().bottom; + const unsigned int total_cross_plane_pad = src0->info()->padding().top + src0->info()->padding().bottom; _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad)); } if(_reinterpret_output_as_3d) { - // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor + // Pass bottom paddings to the kernel if the dst has to be reinterpreted as 3D tensor unsigned int idx0; if(_add_bias) { @@ -384,7 +373,7 @@ void CLGEMMMatrixMultiplyNativeKernel::run(const Window &window, cl::CommandQueu { idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0); } - const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom; + const unsigned int total_cross_plane_pad = dst->info()->padding().top + dst->info()->padding().bottom; _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad)); } @@ -399,22 +388,24 @@ void CLGEMMMatrixMultiplyNativeKernel::run(const Window &window, cl::CommandQueu } unsigned int idx = 0; - add_2D_tensor_argument(idx, _input0, slice); - add_2D_tensor_argument(idx, _input1, slice_b); + add_2D_tensor_argument(idx, src0, slice); + add_2D_tensor_argument(idx, src1, slice_b); if(_add_bias) { - add_2D_tensor_argument(idx, _input2, slice); + add_2D_tensor_argument(idx, src2, slice); } - add_2D_tensor_argument(idx, _output, slice); - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2])); - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2])); + add_2D_tensor_argument(idx, dst, slice); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[2])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[2])); if(_add_bias) { - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input2->info()->strides_in_bytes()[2])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src2->info()->strides_in_bytes()[2])); } - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[2])); enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items); } while(window.slide_window_slice_3D(slice)); } +} // namespace kernels +} // namespace opencl } // namespace arm_compute diff --git a/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h new file mode 100644 index 0000000000..4770b18b8e --- /dev/null +++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h @@ -0,0 +1,88 @@ +/* + * Copyright (c) 2019-2021 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_CL_GEMM_MATRIXMULTIPLY_NATIVE_KERNEL_H +#define ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_NATIVE_KERNEL_H + +#include "arm_compute/core/KernelDescriptors.h" +#include "src/core/common/Macros.h" +#include "src/core/gpu/cl/ClCompileContext.h" +#include "src/core/gpu/cl/IClKernel.h" + +namespace arm_compute +{ +namespace opencl +{ +namespace kernels +{ +/** OpenCL kernel to multiply matrices when neither of the input matrices have been reshaped */ +class ClGemmMatrixMultiplyNativeKernel : public IClKernel +{ +public: + ClGemmMatrixMultiplyNativeKernel() = default; + ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClGemmMatrixMultiplyNativeKernel); + /** Initialise the kernel's input and dst. + * + * @param[in] compile_context The compile context to be used. + * @param[in] src0 Input tensor for the LHS matrix. Data type supported: F32. The number of dimensions for the LHS matrix must be less or equal than 4. + * @param[in] src1 Input tensor for the RHS matrix. Data type supported: same as @p src0. The number of dimensions for the RHS matrix must be less or equal than 3. + * @param[in] src2 Input tensor containing the bias matrix. Data type supported: same as @p src0. + * @param[out] dst dst tensor info. Data type supported: same as @p src0 + * @param[in] alpha Weight of the matrix product + * @param[in] beta Weight of the matrix bias + * @param[in] lhs_info LHS matrix information used to retrieve the number of rows and accumulations to be processed by each thread. Only the following values are supported: + * lhs_info.m0: 1,2,3,4,5,6,7,8 + * lhs_info.k0: 2,3,4,8,16 + * @param[in] rhs_info RHS matrix information used to retrieve the number of columns and accumulations to be processed by each thread. Only the following values are supported: + * rhs_info.n0: 2,3,4,8,16 + * rhs_info.k0: same of lhs_info.k0 + * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices + */ + void configure(const ClCompileContext &compile_context, ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta, + const GEMMLHSMatrixInfo &lhs_info, + const GEMMRHSMatrixInfo &rhs_info, + const GEMMKernelInfo &gemm_info); + /** Static function to check if given info will lead to a valid configuration + * + * Similar to @ref ClGemmMatrixMultiplyNativeKernel::configure() + * + * @return a status + */ + static Status validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, + const GEMMRHSMatrixInfo &rhs_info, + const GEMMKernelInfo &gemm_info); + + // Inherited methods overridden: + void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override; + +private: + bool _slide_matrix_b{ true }; + bool _reinterpret_input_as_3d{ false }; + bool _reinterpret_output_as_3d{ false }; + bool _use_dummy_work_items{ false }; + bool _add_bias{ false }; +}; +} // namespace kernels +} // namespace opencl +} // namespace arm_compute +#endif /*ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_NATIVE_KERNEL_H*/ diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.cpp index d270f92615..27409b66ac 100644 --- a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp +++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.cpp @@ -21,7 +21,7 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h" +#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" @@ -35,39 +35,38 @@ #include "src/core/AccessWindowStatic.h" #include "src/core/CL/CLUtils.h" #include "src/core/CL/CLValidate.h" -#include "src/core/CL/gemm/CLGEMMHelpers.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "src/core/utils/helpers/float_ops.h" +#include "support/Cast.h" #include "support/StringSupport.h" #include <cstddef> #include <cstdint> #include <tuple> -using namespace arm_compute; -using namespace arm_compute::misc::shape_calculator; - namespace arm_compute { -class Coordinates; -} // namespace arm_compute - +namespace opencl +{ +namespace kernels +{ namespace { using ElementsProcessed = Steps; -Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, +Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) { ARM_COMPUTE_UNUSED(alpha); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input0); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3"); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src0); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3"); ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.k0 != rhs_info.k0); ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.transpose == rhs_info.transpose); ARM_COMPUTE_RETURN_ERROR_ON_MSG(((lhs_info.k0 & (lhs_info.k0 - 1)) && lhs_info.k0 != 3), "Only 2,3,4,8,16 are supported for k0"); @@ -75,60 +74,60 @@ Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 2 || lhs_info.m0 > 8); ARM_COMPUTE_RETURN_ERROR_ON_MSG((lhs_info.transpose) && ((lhs_info.m0 & (lhs_info.m0 - 1)) && lhs_info.m0 != 3), "Only 2,3,4,8,16 are supported for m0"); ARM_COMPUTE_RETURN_ERROR_ON_MSG((rhs_info.transpose) && ((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3), "Only 2,3,4,8,16 are supported for n0"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (input2 != nullptr) + ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (src2 != nullptr) && (!gemm_info.broadcast_bias), - "Bias addition only supported with broadcast mode in case the input or output has to be reinterpreted as 3D"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision && (input0->data_type() == DataType::F32), "Mixed precision only supported for F16 data type"); - ARM_COMPUTE_RETURN_ON_ERROR(cl_gemm::validate_image2d_support_on_rhs(*input1, rhs_info)); + "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision && (src0->data_type() == DataType::F32), "Mixed precision only supported for F16 data type"); + ARM_COMPUTE_RETURN_ON_ERROR(gemm::validate_image2d_support_on_rhs(*src1, rhs_info)); const unsigned int m = gemm_info.m; const unsigned int n = gemm_info.n; const unsigned int k = gemm_info.k; - TensorShape tensor_shape0{ input0->tensor_shape() }; + TensorShape tensor_shape0{ src0->tensor_shape() }; tensor_shape0.set(0, k); tensor_shape0.set(1, m); - TensorShape tensor_shape1{ input1->tensor_shape() }; + TensorShape tensor_shape1{ src1->tensor_shape() }; tensor_shape1.set(0, n); tensor_shape1.set(1, k); - if(input2 != nullptr && !(helpers::float_ops::is_zero(beta))) + if(src2 != nullptr && !(helpers::float_ops::is_zero(beta))) { - const unsigned int input2_dim0 = input2->dimension(0); - const unsigned int input2_dim1 = input2->dimension(1); + const unsigned int src2_dim0 = src2->dimension(0); + const unsigned int src2_dim1 = src2->dimension(1); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1); if(gemm_info.broadcast_bias) { - ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); } else { - ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix"); } } - const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0); - const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1); + const TensorInfo tensor_info0 = src0->clone()->set_tensor_shape(tensor_shape0); + const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1); - const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(compute_lhs_reshaped_shape(tensor_info0, lhs_info)); - const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info)); + const TensorInfo tensor_info_reshaped0 = src0->clone()->set_tensor_shape(misc::shape_calculator::compute_lhs_reshaped_shape(tensor_info0, lhs_info)); + const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(tensor_info1, rhs_info)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src0, &tensor_info_reshaped0); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1); - if(output->total_size() != 0) + if(dst->total_size() != 0) { - const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output); + const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst); } return Status{}; } -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info, ElementsProcessed &num_elements_processed) { @@ -140,16 +139,16 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe Window win_out{}; bool window_changed = false; - // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info))); + // dst tensor auto initialization if not yet initialized + auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info))); - TensorInfo tmp_info(*output); + TensorInfo tmp_info(*dst); if(reinterpret_output_as_3d) { - // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, + // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, // the window needs to be constructed on the 2D collapsed version of the tensor - TensorShape tmp_shape(output->tensor_shape()); + TensorShape tmp_shape(dst->tensor_shape()); tmp_shape.collapse(2U, 1U); tmp_info.set_tensor_shape(tmp_shape); } @@ -159,25 +158,25 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe num_elems_processed_per_iteration_y = lhs_info.m0; win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - if(input2 != nullptr) + if(src2 != nullptr) { const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x; const int bias_processed_per_iteration_y = gemm_info.broadcast_bias ? 1 : num_elems_processed_per_iteration_y; - AccessWindowStatic input2_access(input2, 0, 0, - ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x), - ceil_to_multiple(input2->dimension(1), bias_processed_per_iteration_y)); + AccessWindowStatic src2_access(src2, 0, 0, + ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x), + ceil_to_multiple(src2->dimension(1), bias_processed_per_iteration_y)); - window_changed = update_window_and_padding(win, input2_access); // window used by the execute_window_loop + window_changed = update_window_and_padding(win, src2_access); // window used by the execute_window_loop } // Collapse along the Z direction // This collapse needs to be here in order to tune the Z dimension of LWS Window collapsed = win; - const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u); + const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u); collapsed = win.collapse(win, dimension_to_collapse); Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; @@ -185,56 +184,37 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe } } // namespace -CLGEMMMatrixMultiplyReshapedKernel::CLGEMMMatrixMultiplyReshapedKernel() - : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_output_as_3d(false), _use_dummy_work_items(false), _add_bias(false), - _broadcast_bias(false), _export_to_cl_image(false), _k(1) -{ -} - -void CLGEMMMatrixMultiplyReshapedKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, - const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) -{ - configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info); -} - -void CLGEMMMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, - float beta, - const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) +void ClGemmMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compile_context, + ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta, + const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) { - ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); + ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr ? input2->info() : nullptr), output->info(), alpha, beta, lhs_info, rhs_info, gemm_info)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info)); - auto padding_info = get_padding_info({ input0, output }); - _input0 = input0; - _input1 = input1; - _input2 = helpers::float_ops::is_zero(beta) ? nullptr : input2; - _output = output; + auto padding_info = get_padding_info({ src0, dst }); _reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0; _use_dummy_work_items = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device()); - _add_bias = _input2 != nullptr; - _broadcast_bias = gemm_info.broadcast_bias; + _add_bias = src2 != nullptr; _export_to_cl_image = rhs_info.export_to_cl_image; _k = gemm_info.k; // Check if we need to slide the matrix B - const unsigned int num_dimensions_input0 = _input0->info()->num_dimensions(); - _slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0); + const unsigned int num_dimensions_src0 = src0->num_dimensions(); + _slide_matrix_b = (src1->num_dimensions() >= num_dimensions_src0); ElementsProcessed num_elements_processed{}; // Configure kernel window - auto win_config = validate_and_configure_window(input0->info(), input1->info(), input2 != nullptr ? input2->info() : nullptr, output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed); + auto win_config = validate_and_configure_window(src0, src1, src2, dst, lhs_info, rhs_info, gemm_info, num_elements_processed); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); const bool enable_mixed_precision = gemm_info.fp_mixed_precision; - const DataType data_type = input0->info()->data_type(); + const DataType data_type = src0->data_type(); // Calculate partial (store instead of load) M0 and partial N0 for the partial blocks at the end of a row/column if any. This is to avoid padding. - const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : output->info()->dimension(1); + const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : dst->dimension(1); const unsigned int partial_store_m0 = internal_m % lhs_info.m0; const unsigned int partial_store_n0 = gemm_info.n % rhs_info.n0; @@ -242,13 +222,13 @@ void CLGEMMMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compi // Create build options CLBuildOptions build_opts; build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha)); - build_opts.add_option_if(_input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta)); + build_opts.add_option_if(src2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta)); build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA"); build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D"); - build_opts.add_option_if(_reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1))); - build_opts.add_option_if(_reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2))); + build_opts.add_option_if(_reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(dst->dimension(1))); + build_opts.add_option_if(_reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(dst->dimension(2))); build_opts.add_option_if(gemm_info.broadcast_bias, "-DBROADCAST_BIAS"); - build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2))); + build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2))); build_opts.add_option_if(lhs_info.interleave, "-DLHS_INTERLEAVE"); build_opts.add_option_if(rhs_info.interleave, "-DRHS_INTERLEAVE"); build_opts.add_option_if(lhs_info.transpose, "-DLHS_TRANSPOSE"); @@ -258,7 +238,7 @@ void CLGEMMMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compi build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b())); build_opts.add_option_if(enable_mixed_precision, "-DMIXED_PRECISION"); build_opts.add_option_if(rhs_info.export_to_cl_image, "-DOPENCL_IMAGE_SUPPORT"); - build_opts.add_option("-DRHS_HEIGHT=" + support::cpp11::to_string(input1->info()->dimension(1))); + build_opts.add_option("-DRHS_HEIGHT=" + support::cpp11::to_string(src1->dimension(1))); build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); build_opts.add_option("-DDATA_TYPE_ACCUMULATOR=" + (enable_mixed_precision ? get_cl_type_from_data_type(DataType::F32) : get_cl_type_from_data_type(data_type))); build_opts.add_option("-DM=" + support::cpp11::to_string(gemm_info.m)); @@ -284,19 +264,19 @@ void CLGEMMMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compi _config_id = kernel_name; _config_id += "_"; _config_id += (_add_bias ? "add_bias_" : ""); - _config_id += (_broadcast_bias ? "broadcast_bias_" : ""); + _config_id += (gemm_info.broadcast_bias ? "broadcast_bias_" : ""); _config_id += (_reinterpret_output_as_3d ? "3do_" : ""); _config_id += (gemm_info.activation_info.enabled() ? "fused_activation_" : ""); - _config_id += lower_string(string_from_data_type(input0->info()->data_type())); + _config_id += lower_string(string_from_data_type(src0->data_type())); _config_id += "_"; _config_id += (enable_mixed_precision ? "mixed_precision_" : ""); - _config_id += support::cpp11::to_string(output->info()->dimension(1)); + _config_id += support::cpp11::to_string(dst->dimension(1)); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(0)); + _config_id += support::cpp11::to_string(dst->dimension(0)); _config_id += "_"; _config_id += support::cpp11::to_string(gemm_info.k); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(2)); + _config_id += support::cpp11::to_string(dst->dimension(2)); _config_id += "_"; _config_id += support::cpp11::to_string(lhs_info.m0); _config_id += "_"; @@ -315,16 +295,16 @@ void CLGEMMMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compi ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); } -Status CLGEMMMatrixMultiplyReshapedKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, +Status ClGemmMatrixMultiplyReshapedKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) { ElementsProcessed num_elements_processed{}; - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), - input1->clone().get(), - input2 != nullptr ? input2->clone().get() : nullptr, - output->clone().get(), + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src0->clone().get(), + src1->clone().get(), + src2 != nullptr ? src2->clone().get() : nullptr, + dst->clone().get(), lhs_info, rhs_info, gemm_info, @@ -334,15 +314,23 @@ Status CLGEMMMatrixMultiplyReshapedKernel::validate(const ITensorInfo *input0, c return Status{}; } -void CLGEMMMatrixMultiplyReshapedKernel::run(const Window &window, cl::CommandQueue &queue) +void ClGemmMatrixMultiplyReshapedKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); - if(_input1->info()->num_dimensions() < 3) + const auto src0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0)); + const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); + const auto src2 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); + auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); + + ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_ERROR_ON(_add_bias && src2 == nullptr); + + if(src1->info()->num_dimensions() < 3) { // The stride_z for matrix B must be zero if we do not slice - ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0); + ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0); } Window slice = window.first_slice_window_3D(); @@ -351,16 +339,16 @@ void CLGEMMMatrixMultiplyReshapedKernel::run(const Window &window, cl::CommandQu slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1)); slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1)); - const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom; + const unsigned int total_cross_plane_pad = dst->info()->padding().top + dst->info()->padding().bottom; - cl::Image2D input1_image2d; + cl::Image2D src1_image2d; if(_export_to_cl_image) { - const TensorShape shape2d(_input1->info()->dimension(0) / 4, _input1->info()->dimension(1) * _input1->info()->dimension(2)); - const size_t image_row_pitch = _input1->info()->strides_in_bytes()[1]; + const TensorShape shape2d(src1->info()->dimension(0) / 4, src1->info()->dimension(1) * src1->info()->dimension(2)); + const size_t image_row_pitch = src1->info()->strides_in_bytes()[1]; - input1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), _input1->cl_buffer(), shape2d, _input1->info()->data_type(), image_row_pitch); + src1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), src1->cl_buffer(), shape2d, src1->info()->data_type(), image_row_pitch); } do @@ -376,41 +364,41 @@ void CLGEMMMatrixMultiplyReshapedKernel::run(const Window &window, cl::CommandQu unsigned int idx = 0; // LHS buffer - add_2D_tensor_argument(idx, _input0, slice); + add_2D_tensor_argument(idx, src0, slice); // RHS buffer or RHS OpenCL image (_export_to_cl_image == true) if(_export_to_cl_image) { - _kernel.setArg(idx++, input1_image2d); + _kernel.setArg(idx++, src1_image2d); } else { - add_2D_tensor_argument(idx, _input1, slice_b); + add_2D_tensor_argument(idx, src1, slice_b); } // Bias buffer (_add_bias == true) - add_2D_tensor_argument_if(_add_bias, idx, _input2, slice); + add_2D_tensor_argument_if(_add_bias, idx, src2, slice); - // Output buffer - add_2D_tensor_argument(idx, _output, slice); + // dst buffer + add_2D_tensor_argument(idx, dst, slice); // K dimension (not used if _export_to_cl_image == true) _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_k)); // LHS stride_z - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[2])); // RHS stride_z (not used if _export_to_cl_image == true) - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[2])); // Bias stride_z (if _add_bias == true) if(_add_bias) { - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input2->info()->strides_in_bytes()[2])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src2->info()->strides_in_bytes()[2])); } - // Output stride_z - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2])); + // dst stride_z + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[2])); // Cross-plan padding (if _reinterpret_output_as_3d = true) if(_reinterpret_output_as_3d) @@ -423,3 +411,6 @@ void CLGEMMMatrixMultiplyReshapedKernel::run(const Window &window, cl::CommandQu } while(window.slide_window_slice_3D(slice)); } +} // namespace kernels +} // namespace opencl +} // namespace arm_compute
\ No newline at end of file diff --git a/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.h b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.h new file mode 100644 index 0000000000..ab648f15ae --- /dev/null +++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.h @@ -0,0 +1,113 @@ +/* + * Copyright (c) 2018-2021 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_CL_GEMM_MATRIXMULTIPLY_RESHAPED_KERNEL_H +#define ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_RESHAPED_KERNEL_H + +#include "src/core/common/Macros.h" +#include "src/core/gpu/cl/ClCompileContext.h" +#include "src/core/gpu/cl/IClKernel.h" + +#include "arm_compute/core/KernelDescriptors.h" + +namespace arm_compute +{ +namespace opencl +{ +namespace kernels +{ +/** OpenCL kernel to multiply matrices when both the input matrices LHS (src0) and RHS (src1) have been reshaped + * + * @note The input matrices @p src0 and @p src1 must be reshaped through: + * - @ref ClGemmReshapeLhsMatrixKernel + * - @ref ClGemmReshapeRhsMatrixKernel + */ +class ClGemmMatrixMultiplyReshapedKernel : public IClKernel +{ +public: + ClGemmMatrixMultiplyReshapedKernel() = default; + ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClGemmMatrixMultiplyReshapedKernel); + /** Initialise the kernel's input and output. + * + * @note The F16 computation also supports mixed precision through the gemm_info.fp_mixed_precision flag. + * Mixed precision combines different floating precisions during the computation, in particular, F32 for the accumulations and F16 for the + * multiplications. i.e. float c = (half)a * (half)b + * + * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function. + * Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer, + * the following conditions are required: + * -# rhs_info.n0 can only be 4, 8 and 16 + * -# rhs_info.k0 can only be 4, 8 and 16 + * -# Data type can only be F32 + * -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension + * -# The stride Y for the src1 should satisfy the OpenCL pitch alignment requirement + * -# src1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4) + * -# src1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT + * + * @param[in] compile_context The compile context to be used. + * @param[in] src0 Input tensor containing the LHS reshaped matrix. Data type supported: F16/F32 (only F32 if rhs_info.export_to_cl_image = true). The number of dimensions for the LHS matrix must be less or equal than 4 + * @param[in] src1 Input tensor containing the RHS reshaped matrix. Data type supported: same as @p src0. The number of dimensions for the RHS matrix must be less or equal than 3 + * @param[in] src2 Input tensor containing the bias matrix. Data type supported: same as @p src0. + * @param[out] dst dst tensor to store the result of matrix multiplication. Data type supported: same as @p src0 + * @param[in] alpha Weight of the matrix product + * @param[in] beta Weight of the matrix bias + * @param[in] lhs_info LHS matrix information used for reshaping the src0 tensor. Only the following values are supported: + * lhs_info.m0: 2,3,4,5,6,7,8 + * lhs_info.k0: 2,3,4,8,16 + * lhs_info.transpose: false + * @param[in] rhs_info RHS matrix information used for reshaping the src1 tensor. Only the following values are supported: + * rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true) + * rhs_info.k0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image = true) + * rhs_info.transpose: true + * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices + * + * @note lhs_info.k0 must be equal to rhs_info.k0 + */ + void configure(const ClCompileContext &compile_context, + ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta, + const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info); + /** Static function to check if given info will lead to a valid configuration + * + * Similar to @ref ClGemmMatrixMultiplyReshapedKernel::configure() + * + * @return a status + */ + static Status validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, + const GEMMRHSMatrixInfo &rhs_info, + const GEMMKernelInfo &gemm_info); + + // Inherited methods overridden: + void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override; + +private: + bool _slide_matrix_b{ true }; + bool _reinterpret_output_as_3d{ false }; + bool _use_dummy_work_items{ false }; + bool _add_bias{ false }; + bool _export_to_cl_image{ false }; + unsigned int _k{ 1 }; +}; +} // namespace kernels +} // namespace opencl +} // namespace arm_compute +#endif /* ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_RESHAPED_KERNEL_H */
\ No newline at end of file diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.cpp b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.cpp index 3dee4f24cd..4eea2c6f76 100644 --- a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.cpp +++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.cpp @@ -21,7 +21,7 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h" +#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h" #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/Helpers.h" @@ -31,96 +31,95 @@ #include "src/core/AccessWindowStatic.h" #include "src/core/CL/CLUtils.h" #include "src/core/CL/CLValidate.h" -#include "src/core/CL/gemm/CLGEMMHelpers.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "src/core/utils/helpers/float_ops.h" +#include "support/Cast.h" #include "support/StringSupport.h" -#include <tuple> - -using namespace arm_compute::misc::shape_calculator; - namespace arm_compute { +namespace opencl +{ +namespace kernels +{ namespace { using ElementsProcessed = Steps; -Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, - const GEMMKernelInfo &gemm_info) +Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, + const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) { ARM_COMPUTE_UNUSED(alpha); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input0); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3"); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src0); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_info.m0 < 1 || lhs_info.m0 > 8, "Only 1,2,3,4,5,6,7,8 are supported for m0"); ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.k0 > 16 || rhs_info.k0 < 2); ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.k0 & (rhs_info.k0 - 1)) && rhs_info.k0 != 3), "Only 2,3,4,8,16 are supported for k0"); ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.n0 > 16 || rhs_info.n0 < 2); ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3), "Only 2,3,4,8,16 are supported for n0"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (input2 != nullptr) + ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (src2 != nullptr) && (!gemm_info.broadcast_bias), - "Bias addition only supported with broadcast mode in case the input or output has to be reinterpreted as 3D"); + "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision, "Mixed precision not supported"); - ARM_COMPUTE_RETURN_ON_ERROR(cl_gemm::validate_image2d_support_on_rhs(*input1, rhs_info)); + ARM_COMPUTE_RETURN_ON_ERROR(gemm::validate_image2d_support_on_rhs(*src1, rhs_info)); const unsigned int m = gemm_info.m; const unsigned int n = gemm_info.n; const unsigned int k = gemm_info.k; - TensorShape tensor_shape1{ input1->tensor_shape() }; + TensorShape tensor_shape1{ src1->tensor_shape() }; tensor_shape1.set(0, n); tensor_shape1.set(1, k); - if(input2 != nullptr && !(helpers::float_ops::is_zero(beta))) + if(src2 != nullptr && !(helpers::float_ops::is_zero(beta))) { - const unsigned int input2_dim0 = input2->dimension(0); - const unsigned int input2_dim1 = input2->dimension(1); + const unsigned int src2_dim0 = src2->dimension(0); + const unsigned int src2_dim1 = src2->dimension(1); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input0); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src0); if(gemm_info.broadcast_bias) { - ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); } else { - ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix"); } } - const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1); + const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1); - const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info)); + const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(tensor_info1, rhs_info)); - ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != k); + ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != k); if(gemm_info.reinterpret_input_as_3d) { - ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != m); + ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) * src0->dimension(2) != m); } else { - ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != m); + ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) != m); } - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1); - if(output->total_size() != 0) + if(dst->total_size() != 0) { - const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output); + const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst); } return Status{}; } -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, - const GEMMKernelInfo &gemm_info, ElementsProcessed &num_elements_processed) +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, + const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info, ElementsProcessed &num_elements_processed) { unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0]; unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1]; @@ -131,24 +130,24 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe Window win_out{}; bool window_changed = false; - // In case both input and output have to be reinterpreted as 3D tensors, + // In case both input and dst have to be reinterpreted as 3D tensors, // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. - // This approach should only be used when the input/output tensors have pad on the y direction + // This approach should only be used when the input/dst tensors have pad on the y direction if((reinterpret_input_as_3d == reinterpret_output_as_3d) && gemm_info.has_pad_y) { reinterpret_output_as_3d = false; } - // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info))); + // dst tensor auto initialization if not yet initialized + auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info))); - TensorInfo tmp_info(*output); + TensorInfo tmp_info(*dst); if(reinterpret_output_as_3d) { - // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, + // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, // the window needs to be constructed on the 2D collapsed version of the tensor - TensorShape tmp_shape(output->tensor_shape()); + TensorShape tmp_shape(dst->tensor_shape()); tmp_shape.collapse(2U, 1U); tmp_info.set_tensor_shape(tmp_shape); } @@ -158,23 +157,23 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe num_elems_processed_per_iteration_y = lhs_info.m0; win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - if(input2 != nullptr) + if(src2 != nullptr) { const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x; - AccessWindowStatic input2_access(input2, 0, 0, - ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x), - input2->dimension(1)); + AccessWindowStatic src2_access(src2, 0, 0, + ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x), + src2->dimension(1)); - window_changed = update_window_and_padding(win, input2_access); + window_changed = update_window_and_padding(win, src2_access); } // Collapse along the Z direction // This collapse needs to be here in order to tune the Z dimension of LWS Window collapsed = win; - const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u); + const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u); collapsed = win.collapse(win, dimension_to_collapse); Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; @@ -182,44 +181,24 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe } } // namespace -CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::CLGEMMMatrixMultiplyReshapedOnlyRHSKernel() - : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _use_dummy_work_items(false), - _add_bias(false), _broadcast_bias(false), _export_to_cl_image(false), _has_pad_y(false) -{ -} - -void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, - const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) -{ - configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info); -} - -void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, - float alpha, - float beta, - const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) +void ClGemmMatrixMultiplyReshapedOnlyRhsKernel::configure(const CLCompileContext &compile_context, + ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta, + const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) { - ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); + ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr ? input2->info() : nullptr), output->info(), alpha, beta, lhs_info, rhs_info, gemm_info)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info)); - _input0 = input0; - _input1 = input1; - _input2 = helpers::float_ops::is_zero(beta) ? nullptr : input2; - _output = output; _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d; _reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0; _use_dummy_work_items = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device()); - _add_bias = _input2 != nullptr; - _broadcast_bias = gemm_info.broadcast_bias; + _add_bias = src2 != nullptr; _export_to_cl_image = rhs_info.export_to_cl_image; _has_pad_y = gemm_info.has_pad_y; - auto padding_info = get_padding_info({ input0, input1, output }); + auto padding_info = get_padding_info({ src0, src1, dst }); - // In case both input and output have to be reinterpreted as 3D tensors, + // In case both input and dst have to be reinterpreted as 3D tensors, // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. if((_reinterpret_input_as_3d == _reinterpret_output_as_3d) && _has_pad_y) { @@ -228,24 +207,24 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const CLCompileContext } // Check if we need to slide the matrix B - const unsigned int num_dimensions_input0 = _input0->info()->num_dimensions(); - _slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0); + const unsigned int num_dimensions_src0 = src0->num_dimensions(); + _slide_matrix_b = (src1->num_dimensions() >= num_dimensions_src0); ElementsProcessed num_elements_processed{}; // Configure kernel window - auto win_config = validate_and_configure_window(input0->info(), input1->info(), input2 != nullptr ? input2->info() : nullptr, output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed); + auto win_config = validate_and_configure_window(src0, src1, src2, dst, lhs_info, rhs_info, gemm_info, num_elements_processed); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); - // If _reinterpret_input_as_3d = _reinterpret_output_as_3d = true, + // If _reinterpret_input_as_3d = reinterpret_output_as_3d = true, // we will dispatch a batched-GEMM to reduce the complexity of the address calculation within the OpenCL kernel. - // This means that the actual m used by the kernel is given by output->info()->dimension(1) and not by gemm_info.m - const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : output->info()->dimension(1); + // This means that the actual m used by the kernel is given by dst->dimension(1) and not by gemm_info.m + const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : dst->dimension(1); // These variables are used only if gemm_info.has_pad_y == true - const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? output->info()->dimension(1) : input0->info()->dimension(1); - const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? output->info()->dimension(2) : input0->info()->dimension(2); + const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(1) : src0->dimension(1); + const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(2) : src0->dimension(2); // Shrink M0 to be always <= M (internal_m) to prevent out-of-bounds reads. // NOTE: This might have implications on heuristics and performance @@ -257,16 +236,16 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const CLCompileContext // Create build options CLBuildOptions build_opts; - build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type())); + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src0->data_type())); build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha)); - build_opts.add_option_if(_input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta)); + build_opts.add_option_if(src2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta)); build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA"); build_opts.add_option_if(gemm_info.broadcast_bias, "-DBROADCAST_BIAS"); - build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2))); + build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2))); build_opts.add_option_if(rhs_info.interleave, "-DRHS_INTERLEAVE"); build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS"); build_opts.add_option_if(rhs_info.export_to_cl_image, "-DOPENCL_IMAGE_SUPPORT"); - build_opts.add_option("-DRHS_HEIGHT=" + support::cpp11::to_string(input1->info()->dimension(1))); + build_opts.add_option("-DRHS_HEIGHT=" + support::cpp11::to_string(src1->dimension(1))); build_opts.add_option("-DM=" + support::cpp11::to_string(internal_m)); build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n)); build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k)); @@ -299,19 +278,19 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const CLCompileContext _config_id += "_"; _config_id += (_has_pad_y ? "" : "no_pad_y_"); _config_id += (_add_bias ? "add_bias_" : ""); - _config_id += (_broadcast_bias ? "broadcast_bias_" : ""); + _config_id += (gemm_info.broadcast_bias ? "broadcast_bias_" : ""); _config_id += (_reinterpret_input_as_3d ? "3di_" : ""); _config_id += (_reinterpret_output_as_3d ? "3do_" : ""); _config_id += (gemm_info.activation_info.enabled() ? "fused_activation_" : ""); - _config_id += lower_string(string_from_data_type(input0->info()->data_type())); + _config_id += lower_string(string_from_data_type(src0->data_type())); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(1)); + _config_id += support::cpp11::to_string(dst->dimension(1)); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(0)); + _config_id += support::cpp11::to_string(dst->dimension(0)); _config_id += "_"; _config_id += support::cpp11::to_string(gemm_info.k); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(2)); + _config_id += support::cpp11::to_string(dst->dimension(2)); _config_id += "_"; _config_id += support::cpp11::to_string(lhs_info.m0); _config_id += "_"; @@ -326,16 +305,16 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const CLCompileContext ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); } -Status CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, +Status ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) { ElementsProcessed num_elements_processed{}; - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), - input1->clone().get(), - input2 != nullptr ? input2->clone().get() : nullptr, - output->clone().get(), + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src0->clone().get(), + src1->clone().get(), + src2 != nullptr ? src2->clone().get() : nullptr, + dst->clone().get(), lhs_info, rhs_info, gemm_info, @@ -345,15 +324,23 @@ Status CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(const ITensorInfo *in return Status{}; } -void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::run(const Window &window, cl::CommandQueue &queue) +void ClGemmMatrixMultiplyReshapedOnlyRhsKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); - if(_input1->info()->num_dimensions() < 3) + const auto src0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0)); + const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); + const auto src2 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); + auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); + + ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_ERROR_ON(_add_bias && src2 == nullptr); + + if(src1->info()->num_dimensions() < 3) { // The stride_z for matrix B must be zero if we do not slice - ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0); + ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0); } const size_t lhs_idx_batch_size = _reinterpret_input_as_3d && !_has_pad_y ? 3u : 2u; @@ -368,20 +355,20 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::run(const Window &window, cl::Co slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1)); // Get cross plane pads - const unsigned int total_cross_plane_pad_lhs = _input0->info()->padding().top + _input0->info()->padding().bottom; - const unsigned int total_cross_plane_pad_out = _output->info()->padding().top + _output->info()->padding().bottom; + const unsigned int total_cross_plane_pad_lhs = src0->info()->padding().top + src0->info()->padding().bottom; + const unsigned int total_cross_plane_pad_out = dst->info()->padding().top + dst->info()->padding().bottom; // The execution should fail if we try to run with has_pad_y = false but we have padding in either the LHS or DST tensor ARM_COMPUTE_ERROR_ON(!_has_pad_y && ((total_cross_plane_pad_lhs != 0) || (total_cross_plane_pad_out != 0))); - cl::Image2D input1_image2d; + cl::Image2D src1_image2d; if(_export_to_cl_image) { - const TensorShape shape2d(_input1->info()->dimension(0) / 4, _input1->info()->dimension(1) * _input1->info()->dimension(2)); - const size_t image_row_pitch = _input1->info()->strides_in_bytes()[1]; + const TensorShape shape2d(src1->info()->dimension(0) / 4, src1->info()->dimension(1) * src1->info()->dimension(2)); + const size_t image_row_pitch = src1->info()->strides_in_bytes()[1]; - input1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), _input1->cl_buffer(), shape2d, _input1->info()->data_type(), image_row_pitch); + src1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), src1->cl_buffer(), shape2d, src1->info()->data_type(), image_row_pitch); } do @@ -397,38 +384,38 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::run(const Window &window, cl::Co unsigned int idx = 0; // LHS buffer - add_2D_tensor_argument(idx, _input0, slice); + add_2D_tensor_argument(idx, src0, slice); // RHS buffer or RHS OpenCL image (_export_to_cl_image == true) if(_export_to_cl_image) { - _kernel.setArg(idx++, input1_image2d); + _kernel.setArg(idx++, src1_image2d); } else { - add_2D_tensor_argument(idx, _input1, slice_b); + add_2D_tensor_argument(idx, src1, slice_b); } // Bias buffer (_add_bias == true) - add_2D_tensor_argument_if(_add_bias, idx, _input2, slice); + add_2D_tensor_argument_if(_add_bias, idx, src2, slice); - // Output buffer - add_2D_tensor_argument(idx, _output, slice); + // dst buffer + add_2D_tensor_argument(idx, dst, slice); // LHS stride_z - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[lhs_idx_batch_size])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[lhs_idx_batch_size])); // RHS stride_z (not used if _export_to_cl_image == true) - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[rhs_idx_batch_size])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[rhs_idx_batch_size])); // Bias stride_z (if _add_bias == true) if(_add_bias) { - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input2->info()->strides_in_bytes()[bia_idx_batch_size])); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src2->info()->strides_in_bytes()[bia_idx_batch_size])); } - // Output stride_z - _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[out_idx_batch_size])); + // dst stride_z + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[out_idx_batch_size])); // Cross-plan padding (if _reinterpret_input_as_3d = true) if(_reinterpret_input_as_3d && _has_pad_y) @@ -436,7 +423,7 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::run(const Window &window, cl::Co _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(total_cross_plane_pad_lhs)); } - // Cross-plan padding (if _reinterpret_output_as_3d = true) + // Cross-plan padding (if reinterpret_output_as_3d = true) if(_reinterpret_output_as_3d && _has_pad_y) { _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(total_cross_plane_pad_out)); @@ -446,4 +433,6 @@ void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::run(const Window &window, cl::Co } while(window.slide_window_slice_3D(slice)); } +} // namespace kernels +} // namespace opencl } // namespace arm_compute diff --git a/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h new file mode 100644 index 0000000000..ff6c391e15 --- /dev/null +++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h @@ -0,0 +1,104 @@ +/* + * Copyright (c) 2019-2021 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_CL_GEMM_MATRIXMULTIPLY_RESHAPED_ONLY_RHS_KERNEL_H +#define ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_RESHAPED_ONLY_RHS_KERNEL_H + +#include "src/core/common/Macros.h" +#include "src/core/gpu/cl/ClCompileContext.h" +#include "src/core/gpu/cl/IClKernel.h" + +#include "arm_compute/core/KernelDescriptors.h" + +namespace arm_compute +{ +namespace opencl +{ +namespace kernels +{ +/** OpenCL kernel to multiply matrices when only the input matrix RHS (src1) has been reshaped + * + * @note The input matrix src1 must be reshaped through @ref ClGemmReshapeRhsMatrixKernel + */ +class ClGemmMatrixMultiplyReshapedOnlyRhsKernel : public ICLKernel +{ +public: + ClGemmMatrixMultiplyReshapedOnlyRhsKernel() = default; + ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClGemmMatrixMultiplyReshapedOnlyRhsKernel); + /** Initialise the kernel's input and output. + * + * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will fetch the RHS data using the OpenCL read_image built-in function. + * Reading from the OpenCL image object can increase the performance. However, since the OpenCL image object is created importing the OpenCL buffer, + * the following conditions are required: + * -# rhs_info.n0 can only be 4, 8 and 16 + * -# rhs_info.k0 can only be 4, 8 and 16 + * -# Data type can only be F32 + * -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension + * -# The stride Y for the src1 should satisfy the OpenCL pitch alignment requirement + * -# src1 width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4) + * -# src1 (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT + * + * @param[in] compile_context The compile context to be used. + * @param[in] src0 Input tensor containing the LHS matrix. Data type supported: F16/F32 (only F32 if rhs_info.export_to_cl_image = true). + * The number of dimensions for the LHS matrix must be less or equal than 4. + * @param[in] src1 Input tensor containing the RHS reshaped matrix. Data type supported: same as @p src0. The number of dimensions for the RHS matrix must be less or equal than 3. + * @param[in] src2 Input tensor containing the bias matrix. Data type supported: same as @p src0. + * @param[out] dst Output tensor to store the result of matrix multiplication. Data type supported: same as @p src0 + * @param[in] alpha Weight of the matrix product + * @param[in] beta Weight of the matrix bias + * @param[in] lhs_info LHS matrix information used to retrieve the number of rows to be processed by each thread. Only the following values are supported: + * lhs_info.m0: 1,2,3,4,5,6,7,8 + * @param[in] rhs_info RHS matrix information used for reshaping the src1 tensor. Only the following values are supported: + * rhs_info.k0: 2,3,4,8,16 + * rhs_info.n0: 2,3,4,8,16 + * rhs_info.transpose: true,false + * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices + */ + void configure(const ClCompileContext &compile_context, + ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta, + const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info); + /** Static function to check if given info will lead to a valid configuration + * + * Similar to @ref ClGemmMatrixMultiplyReshapedOnlyRhsKernel::configure() + * + * @return a status + */ + static Status validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, + const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info); + + // Inherited methods overridden: + void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override; + +private: + bool _slide_matrix_b{ true }; + bool _reinterpret_input_as_3d{ false }; + bool _reinterpret_output_as_3d{ false }; + bool _use_dummy_work_items{ false }; + bool _add_bias{ false }; + bool _export_to_cl_image{ false }; + bool _has_pad_y{ false }; +}; +} // namespace kernels +} // namespace opencl +} // namespace arm_compute +#endif /* ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_RESHAPED_ONLY_RHS_KERNEL_H */ diff --git a/src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.cpp b/src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.cpp index cc95315894..98161edfff 100644 --- a/src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.cpp +++ b/src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.cpp @@ -21,7 +21,7 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h" +#include "src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" @@ -35,17 +35,20 @@ #include "src/core/CL/CLValidate.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" +#include "support/Cast.h" #include "support/StringSupport.h" namespace arm_compute { -using namespace arm_compute::misc::shape_calculator; - +namespace opencl +{ +namespace kernels +{ namespace { -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d) +Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d) { - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst); ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 == 0); ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.k0 == 0); ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.v0 == 0); @@ -53,50 +56,51 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.k0 > 16); ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 2 || lhs_info.m0 > 8); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); - ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::UNKNOWN); + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src); + ARM_COMPUTE_RETURN_ERROR_ON(src->data_type() == DataType::UNKNOWN); - if(output->total_size() != 0) + if(dst->total_size() != 0) { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), compute_lhs_reshaped_shape(*input, lhs_info, reinterpret_input_as_3d)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(), + misc::shape_calculator::compute_lhs_reshaped_shape(*src, lhs_info, reinterpret_input_as_3d)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(src, dst); } return Status{}; } -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d) +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d) { const unsigned int num_elems_processed_per_iteration_x = lhs_info.k0; const unsigned int num_elems_processed_per_iteration_y = lhs_info.m0; bool window_changed = false; - TensorInfo tmp_info(*input); + TensorInfo tmp_info(*src); if(reinterpret_input_as_3d) { - // Since the input tensor has to be reinterpreted as 3D and the execute window is based on a 2D interleave, + // Since the src tensor has to be reinterpreted as 3D and the execute window is based on a 2D interleave, // the window needs to be constructed on the 2D collapsed version of the tensor - TensorShape tmp_shape(input->tensor_shape()); + TensorShape tmp_shape(src->tensor_shape()); tmp_shape.collapse(2U, 1U); tmp_info.set_tensor_shape(tmp_shape); } - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*input, lhs_info, reinterpret_input_as_3d))); + // dst auto inizialitation if not yet initialized + auto_init_if_empty(*dst, src->clone()->set_tensor_shape(misc::shape_calculator::compute_lhs_reshaped_shape(*src, lhs_info, reinterpret_input_as_3d))); // Configure window Window win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - Window win_in = calculate_max_window(*input, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + Window win_in = calculate_max_window(*src, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - AccessWindowStatic input_access(input, 0, 0, - input->dimension(0), - input->dimension(1)); - AccessWindowStatic output_access(output, 0, 0, output->dimension(0), output->dimension(1)); + AccessWindowStatic src_access(src, 0, 0, + src->dimension(0), + src->dimension(1)); + AccessWindowStatic dst_access(dst, 0, 0, dst->dimension(0), dst->dimension(1)); - window_changed = update_window_and_padding(win_in, input_access) || // window used by the execute_window_loop - update_window_and_padding(win, output_access); // window used to update the padding requirements of output tensor + window_changed = update_window_and_padding(win_in, src_access) || // window used by the execute_window_loop + update_window_and_padding(win, dst_access); // window used to update the padding requirements of dst tensor // Collapse along the Z direction // This collapse needs to be here in order to tune the Z dimension of LWS @@ -107,31 +111,19 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen } } // namespace -CLGEMMReshapeLHSMatrixKernel::CLGEMMReshapeLHSMatrixKernel() - : _input(nullptr), _output(nullptr), _reinterpret_input_as_3d(false) +void ClGemmReshapeLhsMatrixKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d) { -} - -void CLGEMMReshapeLHSMatrixKernel::configure(const ICLTensor *input, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d) -{ - configure(CLKernelLibrary::get().get_compile_context(), input, output, lhs_info, reinterpret_input_as_3d); -} - -void CLGEMMReshapeLHSMatrixKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); // Perform validate step - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), lhs_info, reinterpret_input_as_3d)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, lhs_info, reinterpret_input_as_3d)); - auto padding_info = get_padding_info({ input }); + auto padding_info = get_padding_info({ src }); - _input = input; - _output = output; _reinterpret_input_as_3d = reinterpret_input_as_3d; - const unsigned int src_w = input->info()->dimension(0); - const unsigned int src_h = _reinterpret_input_as_3d ? input->info()->dimension(1) * input->info()->dimension(2) : input->info()->dimension(1); + const unsigned int src_w = src->dimension(0); + const unsigned int src_h = _reinterpret_input_as_3d ? src->dimension(1) * src->dimension(2) : src->dimension(1); const unsigned int partial_load_m0 = src_h % lhs_info.m0; const unsigned int partial_load_k0 = src_w % lhs_info.k0; @@ -144,9 +136,9 @@ void CLGEMMReshapeLHSMatrixKernel::configure(const CLCompileContext &compile_con build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src_h)); build_opts.add_option_if(lhs_info.interleave, "-DINTERLEAVE"); build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D"); - build_opts.add_option_if(_reinterpret_input_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(input->info()->dimension(1))); - build_opts.add_option_if(_reinterpret_input_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(input->info()->dimension(2))); - build_opts.add_option("-DDATA_TYPE=" + get_cl_unsigned_type_from_element_size(input->info()->element_size())); + build_opts.add_option_if(_reinterpret_input_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(src->dimension(1))); + build_opts.add_option_if(_reinterpret_input_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(src->dimension(2))); + build_opts.add_option("-DDATA_TYPE=" + get_cl_unsigned_type_from_element_size(src->element_size())); build_opts.add_option("-DPARTIAL_LOAD_M0=" + support::cpp11::to_string(partial_load_m0)); build_opts.add_option("-DPARTIAL_LOAD_K0=" + support::cpp11::to_string(partial_load_k0)); @@ -157,20 +149,20 @@ void CLGEMMReshapeLHSMatrixKernel::configure(const CLCompileContext &compile_con _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); // Configure kernel window - auto win_config = validate_and_configure_window(input->info(), output->info(), lhs_info, reinterpret_input_as_3d); + auto win_config = validate_and_configure_window(src, dst, lhs_info, reinterpret_input_as_3d); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); // Set config_id for enabling LWS tuning _config_id = "gemm_reshape_lhs_matrix_"; _config_id += (_reinterpret_input_as_3d ? "3d_" : ""); - _config_id += lower_string(string_from_data_type(input->info()->data_type())); + _config_id += lower_string(string_from_data_type(src->data_type())); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(0)); + _config_id += support::cpp11::to_string(dst->dimension(0)); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(1)); + _config_id += support::cpp11::to_string(dst->dimension(1)); _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(2)); + _config_id += support::cpp11::to_string(dst->dimension(2)); _config_id += "_"; _config_id += support::cpp11::to_string(lhs_info.m0); _config_id += "_"; @@ -185,36 +177,43 @@ void CLGEMMReshapeLHSMatrixKernel::configure(const CLCompileContext &compile_con ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); } -Status CLGEMMReshapeLHSMatrixKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d) +Status ClGemmReshapeLhsMatrixKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, lhs_info, reinterpret_input_as_3d)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), lhs_info, reinterpret_input_as_3d).first); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, lhs_info, reinterpret_input_as_3d)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(), lhs_info, reinterpret_input_as_3d).first); return Status{}; } -void CLGEMMReshapeLHSMatrixKernel::run(const Window &window, cl::CommandQueue &queue) +void ClGemmReshapeLhsMatrixKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + const auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC)); + auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); + + ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); + Window slice = window.first_slice_window_3D(); if(_reinterpret_input_as_3d) { - // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor + // Pass bottom paddings to the kernel if the src has to be reinterpreted as 3D tensor const unsigned int idx0 = 2 * num_arguments_per_3D_tensor(); - const unsigned int total_cross_plane_pad = _input->info()->padding().top + _input->info()->padding().bottom; + const unsigned int total_cross_plane_pad = src->info()->padding().top + src->info()->padding().bottom; _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad)); } do { unsigned int idx = 0; - add_3D_tensor_argument(idx, _input, slice); - add_3D_tensor_argument(idx, _output, slice); + add_3D_tensor_argument(idx, src, slice); + add_3D_tensor_argument(idx, dst, slice); enqueue(queue, *this, slice, lws_hint()); } while(window.slide_window_slice_3D(slice)); } +} // namespace kernels +} // namespace opencl } // namespace arm_compute diff --git a/src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h b/src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h new file mode 100644 index 0000000000..b830ba02b4 --- /dev/null +++ b/src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h @@ -0,0 +1,78 @@ +/* + * Copyright (c) 2018-2021 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_CL_GEMM_RESHAPE_LHS_MATRIX_KERNEL_H +#define ARM_COMPUTE_CL_GEMM_RESHAPE_LHS_MATRIX_KERNEL_H + +#include "src/core/common/Macros.h" +#include "src/core/gpu/cl/ClCompileContext.h" +#include "src/core/gpu/cl/IClKernel.h" + +namespace arm_compute +{ +namespace opencl +{ +namespace kernels +{ +/** OpenCL kernel to reshape the LHS matrix when performing the matrix multiplication. + * In particular, this function splits the src matrix in blocks of size M0xK0 (defined through GEMMLHSInfo) and + * stores each one in the dst matrix unrolling the values + */ +class ClGemmReshapeLhsMatrixKernel : public ICLKernel +{ +public: + ClGemmReshapeLhsMatrixKernel() = default; + ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClGemmReshapeLhsMatrixKernel); + /** Initialise the kernel's input and output. + * + * @param[in] compile_context The compile context to be used. + * @param[in] src Input tensor. Data types supported: All + * @param[out] dst Output tensor. Data type supported: same as @p src + * @param[in] lhs_info LHS matrix information to be used for reshaping. This object contains all the necessary + * information to reshape the src tensor. Only the following values are supported: + * lhs_info.m0: 2,3,4,5,6,7,8 + * lhs_info.k0: 2,3,4,8,16 + * lhs_info.v0: greater than 0 + * lhs_info.transpose: true, false + * lhs_info.interleave: true, false + * @param[in] reinterpret_src_as_3d (Optional) True if the src has to be reinterpreted as 3D tensor + */ + void configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_src_as_3d = false); + /** Static function to check if given info will lead to a valid configuration + * + * Similar to @ref ClGemmReshapeLhsMatrixKernel::configure() + * + * @return a status + */ + static Status validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_src_as_3d); + + // Inherited methods overridden: + void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override; + +private: + bool _reinterpret_input_as_3d{ false }; +}; +} // namespace kernels +} // namespace opencl +} // namespace arm_compute +#endif /* ARM_COMPUTE_CL_GEMM_RESHAPE_LHS_MATRIX_KERNEL_H */
\ No newline at end of file diff --git a/src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.cpp b/src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.cpp index 1c4092c0e5..e1ef7c61aa 100644 --- a/src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.cpp +++ b/src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.cpp @@ -21,7 +21,7 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h" +#include "src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" @@ -33,18 +33,21 @@ #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "src/core/AccessWindowStatic.h" #include "src/core/CL/CLValidate.h" -#include "src/core/CL/gemm/CLGEMMHelpers.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" +#include "support/Cast.h" #include "support/StringSupport.h" namespace arm_compute { -using namespace arm_compute::misc::shape_calculator; - +namespace opencl +{ +namespace kernels +{ namespace { -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const GEMMRHSMatrixInfo &rhs_info) +Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const GEMMRHSMatrixInfo &rhs_info) { ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.n0 == 0); ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.k0 == 0); @@ -55,44 +58,44 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.k0 > 16); ARM_COMPUTE_RETURN_ERROR_ON((rhs_info.k0 == 1) && (rhs_info.transpose)); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); - ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::UNKNOWN); + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src); + ARM_COMPUTE_RETURN_ERROR_ON(src->data_type() == DataType::UNKNOWN); if(rhs_info.export_to_cl_image) { - const TensorInfo tensor_reshaped_info(compute_rhs_reshaped_shape(*input, rhs_info), 1, input->data_type()); - ARM_COMPUTE_RETURN_ON_ERROR(cl_gemm::validate_image2d_support_on_rhs(tensor_reshaped_info, rhs_info)); + const TensorInfo tensor_reshaped_info(misc::shape_calculator::compute_rhs_reshaped_shape(*src, rhs_info), 1, src->data_type()); + ARM_COMPUTE_RETURN_ON_ERROR(gemm::validate_image2d_support_on_rhs(tensor_reshaped_info, rhs_info)); } - if(output->total_size() != 0) + if(dst->total_size() != 0) { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), compute_rhs_reshaped_shape(*input, rhs_info)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(), misc::shape_calculator::compute_rhs_reshaped_shape(*src, rhs_info)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(src, dst); } return Status{}; } -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const GEMMRHSMatrixInfo &rhs_info) +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst, const GEMMRHSMatrixInfo &rhs_info) { const unsigned int num_elems_processed_per_iteration_x = rhs_info.n0; const unsigned int num_elems_processed_per_iteration_y = rhs_info.k0; bool window_changed = false; - // Output auto initialization if not yet initialized - auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*input, rhs_info))); + // dst auto initialization if not yet initialized + auto_init_if_empty(*dst, src->clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(*src, rhs_info))); // Configure window - Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + Window win = calculate_max_window(*src, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - AccessWindowRectangle input_access(input, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); + AccessWindowRectangle src_access(src, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); - window_changed = update_window_and_padding(win, input_access); + window_changed = update_window_and_padding(win, src_access); if(rhs_info.export_to_cl_image) { - arm_compute::cl_gemm::update_padding_for_cl_image(output); + gemm::update_padding_for_cl_image(dst); } // Collapse along the Z direction @@ -104,25 +107,12 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen } } // namespace -CLGEMMReshapeRHSMatrixKernel::CLGEMMReshapeRHSMatrixKernel() - : _input(nullptr), _output(nullptr) -{ -} - -void CLGEMMReshapeRHSMatrixKernel::configure(const ICLTensor *input, ICLTensor *output, const GEMMRHSMatrixInfo &rhs_info) +void ClGemmReshapeRhsMatrixKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const GEMMRHSMatrixInfo &rhs_info) { - configure(CLKernelLibrary::get().get_compile_context(), input, output, rhs_info); -} - -void CLGEMMReshapeRHSMatrixKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const GEMMRHSMatrixInfo &rhs_info) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); // Perform validate step - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), rhs_info)); - - _input = input; - _output = output; + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, rhs_info)); // Create build options CLBuildOptions build_opts; @@ -131,8 +121,8 @@ void CLGEMMReshapeRHSMatrixKernel::configure(const CLCompileContext &compile_con build_opts.add_option("-DH0=" + support::cpp11::to_string(rhs_info.h0)); build_opts.add_option_if(rhs_info.transpose, "-DTRANSPOSE"); build_opts.add_option_if(rhs_info.interleave, "-DINTERLEAVE"); - build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1))); - build_opts.add_option("-DDATA_TYPE=" + get_cl_unsigned_type_from_element_size(input->info()->element_size())); + build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(1))); + build_opts.add_option("-DDATA_TYPE=" + get_cl_unsigned_type_from_element_size(src->element_size())); std::string kernel_name("gemm_reshape_rhs_matrix_"); kernel_name += rhs_info.transpose ? "t" : "nt"; @@ -141,33 +131,40 @@ void CLGEMMReshapeRHSMatrixKernel::configure(const CLCompileContext &compile_con _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); // Configure kernel window - auto win_config = validate_and_configure_window(input->info(), output->info(), rhs_info); + auto win_config = validate_and_configure_window(src, dst, rhs_info); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); } -Status CLGEMMReshapeRHSMatrixKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const GEMMRHSMatrixInfo &rhs_info) +Status ClGemmReshapeRhsMatrixKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMRHSMatrixInfo &rhs_info) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, rhs_info)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), rhs_info).first); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, rhs_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(), rhs_info).first); return Status{}; } -void CLGEMMReshapeRHSMatrixKernel::run(const Window &window, cl::CommandQueue &queue) +void ClGemmReshapeRhsMatrixKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + const auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC)); + auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); + + ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); + Window slice = window.first_slice_window_3D(); do { unsigned int idx = 0; - add_3D_tensor_argument(idx, _input, slice); - add_3D_tensor_argument(idx, _output, slice); + add_3D_tensor_argument(idx, src, slice); + add_3D_tensor_argument(idx, dst, slice); enqueue(queue, *this, slice, lws_hint()); } while(window.slide_window_slice_3D(slice)); } -} // namespace arm_compute +} // namespace kernels +} // namespace opencl +} // namespace arm_compute
\ No newline at end of file diff --git a/src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h b/src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h new file mode 100644 index 0000000000..e877d87408 --- /dev/null +++ b/src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h @@ -0,0 +1,84 @@ +/* + * Copyright (c) 2018-2021 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_CL_GEMM_RESHAPE_RHS_MATRIX_KERNEL_H +#define ARM_COMPUTE_CL_GEMM_RESHAPE_RHS_MATRIX_KERNEL_H + +#include "src/core/common/Macros.h" +#include "src/core/gpu/cl/ClCompileContext.h" +#include "src/core/gpu/cl/IClKernel.h" + +namespace arm_compute +{ +namespace opencl +{ +namespace kernels +{ +/** OpenCL kernel to reshape the RHS matrix when performing the matrix multiplication + * In particular, this kernel splits the src matrix in blocks of size K0xN0 and stores each one in + * the dst matrix unrolling the values */ +class ClGemmReshapeRhsMatrixKernel : public ICLKernel +{ +public: + ClGemmReshapeRhsMatrixKernel() = default; + ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClGemmReshapeRhsMatrixKernel); + /** Initialise the kernel's input and output. + * + * @note If rhs_info.export_to_cl_image = true, this OpenCL kernel will guarantee the OpenCL pitch alignment for the output tensor, + * required to create a OpenCL image object from buffer in @ref ClGemmMatrixMultiplyReshapedKernel and in @ref ClGemmMatrixMultiplyReshapedOnlyRhsKernel + * Since the OpenCL image object is created importing the OpenCL buffer, the following conditions are required: + * -# rhs_info.n0 can only be 4, 8 and 16 + * -# rhs_info.k0 can only be 4, 8 and 16 + * -# Data type can only be F32, F16 + * -# The platform should support the OpenCL cl_khr_image2d_from_buffer extension + * -# output width should be less or equal to (CL_DEVICE_IMAGE2D_MAX_WIDTH * 4) + * -# output (height * depth) should be less or equal to CL_DEVICE_IMAGE2D_MAX_HEIGHT + * -# The output tensor should be only consumed by @ref ClGemmMatrixMultiplyReshapedKernel or @ref ClGemmMatrixMultiplyReshapedOnlyRhsKernel + * + * @param[in] compile_context The compile context to be used. + * @param[in] src Input tensor. Data types supported: All + * @param[out] dst Output tensor. Data type supported: same as @p src + * @param[in] rhs_info RHS matrix information to be used for reshaping. This object contains all the necessary + * information to reshape the src tensor. Only the following values are supported: + * rhs_info.n0: 2,3,4,8,16 (only 4, 8 and 16 if rhs_info.export_to_cl_image == true) + * rhs_info.k0: 1,2,3,4,8,16 (k0 = 1 only if rhs_info.transpose = false), (only 4, 8 and 16 if rhs_info.export_to_cl_image == true) + * rhs_info.h0: greater than 0 + * rhs_info.transpose: true, false + * rhs_info.interleave: true, false + */ + void configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const GEMMRHSMatrixInfo &rhs_info); + /** Static function to check if given info will lead to a valid configuration + * + * Similar to @ref ClGemmReshapeRhsMatrixKernel::configure() + * + * @return a status + */ + static Status validate(const ITensorInfo *src, const ITensorInfo *dst, const GEMMRHSMatrixInfo &rhs_info); + + // Inherited methods overridden: + void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override; +}; +} // namespace kernels +} // namespace opencl +} // namespace arm_compute +#endif /* ARM_COMPUTE_CL_GEMM_RESHAPE_RHS_MATRIX_KERNEL_H */
\ No newline at end of file diff --git a/src/core/CL/gemm/CLGEMMHelpers.cpp b/src/core/gpu/cl/kernels/gemm/ClGemmHelpers.cpp index 61aa962198..0a8ba971ed 100644 --- a/src/core/CL/gemm/CLGEMMHelpers.cpp +++ b/src/core/gpu/cl/kernels/gemm/ClGemmHelpers.cpp @@ -21,22 +21,23 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/CL/gemm/CLGEMMHelpers.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/CL/OpenCL.h" -#include "arm_compute/core/ITensorInfo.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include <utility> namespace arm_compute { -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { -using namespace arm_compute::misc::shape_calculator; - std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_lhs_rhs_info(unsigned int m, unsigned int n, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0, bool lhs_interleave, bool rhs_interleave, bool lhs_transpose, bool rhs_transpose, bool export_to_cl_image) { @@ -55,7 +56,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> select_lhs_rhs_info(std::pair<GE unsigned int n, unsigned int k, unsigned int b, DataType data_type) { const TensorInfo tensor_rhs_info(TensorShape(n, k, b), 1, data_type); - const TensorShape shape = compute_rhs_reshaped_shape(tensor_rhs_info, info_img.second); + const TensorShape shape = misc::shape_calculator::compute_rhs_reshaped_shape(tensor_rhs_info, info_img.second); const TensorInfo tensor_reshaped_info(shape, 1, data_type); if(bool(validate_image2d_support_on_rhs(tensor_reshaped_info, info_img.second))) @@ -73,7 +74,7 @@ void update_padding_for_cl_image(ITensorInfo *tensor) constexpr unsigned int num_floats_per_pixel = 4; const unsigned int stride_y_in_elements = tensor->strides_in_bytes()[1] / tensor->element_size(); - const unsigned int pixel_alignment = get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()); + const unsigned int pixel_alignment = get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()); ARM_COMPUTE_ERROR_ON_MSG(pixel_alignment == 0, "Cannot retrieve cl_image pitch alignment"); if(pixel_alignment == 0) @@ -109,5 +110,7 @@ Status validate_image2d_support_on_rhs(const ITensorInfo &tensor_reshaped_info, return Status{}; } -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute diff --git a/src/core/CL/gemm/CLGEMMHelpers.h b/src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h index 57624673c0..3fce8c9173 100644 --- a/src/core/CL/gemm/CLGEMMHelpers.h +++ b/src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2020 Arm Limited. + * Copyright (c) 2019-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,18 +21,19 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_CLGEMMHELPERS_H -#define ARM_COMPUTE_CLGEMMHELPERS_H +#ifndef ARM_COMPUTE_CL_GEMM_HELPERS_H +#define ARM_COMPUTE_CL_GEMM_HELPERS_H #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" namespace arm_compute { -class ITensorInfo; -struct GEMMRHSMatrixInfo; - -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { /** Configure @ref GEMMLHSMatrixInfo and @ref GEMMRHSMatrixInfo * @@ -87,6 +88,8 @@ void update_padding_for_cl_image(ITensorInfo *tensor); * @return Status reporting if we can use the image2d OpenCL object on the RHS reshaped matrix */ Status validate_image2d_support_on_rhs(const ITensorInfo &tensor_reshaped_info, const GEMMRHSMatrixInfo &rhs_info); -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute -#endif /*ARM_COMPUTE_CLGEMMHELPERS_H */ +#endif /* ARM_COMPUTE_CL_GEMM_HELPERS_H */ diff --git a/src/core/CL/ICLGEMMKernelConfiguration.h b/src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h index 886905ecd0..a49836cfda 100644 --- a/src/core/CL/ICLGEMMKernelConfiguration.h +++ b/src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h @@ -21,15 +21,22 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_ICLGEMMKERNELCONFIGURATION_H -#define ARM_COMPUTE_ICLGEMMKERNELCONFIGURATION_H +#ifndef ARM_COMPUTE_ICL_GEMM_KERNEL_CONFIG_H +#define ARM_COMPUTE_ICL_GEMM_KERNEL_CONFIG_H #include "arm_compute/core/GPUTarget.h" #include "arm_compute/core/Types.h" +#include "src/core/common/Macros.h" #include <array> namespace arm_compute { +namespace opencl +{ +namespace kernels +{ +namespace gemm +{ /** Basic container for the OpenCL GEMM configuration functions */ template <class T> class CLGEMMConfigArray @@ -82,27 +89,20 @@ private: }; /** Basic interface for the GEMM kernel configuration */ -class ICLGEMMKernelConfiguration +class IClGemmKernelConfig { public: /** Constructor * * @param[in] arch GPU target */ - ICLGEMMKernelConfiguration(GPUTarget arch) + IClGemmKernelConfig(GPUTarget arch) : _target(arch) { } - /** Prevent instances of this class from being copied (As this class contains pointers) */ - ICLGEMMKernelConfiguration(const ICLGEMMKernelConfiguration &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - ICLGEMMKernelConfiguration &operator=(const ICLGEMMKernelConfiguration &) = delete; - /** Default Move Constructor. */ - ICLGEMMKernelConfiguration(ICLGEMMKernelConfiguration &&) = default; - /** Default move assignment operator */ - ICLGEMMKernelConfiguration &operator=(ICLGEMMKernelConfiguration &&) = default; + ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(IClGemmKernelConfig); /** Virtual destructor */ - virtual ~ICLGEMMKernelConfiguration() = default; + virtual ~IClGemmKernelConfig() = default; /** Given M, N, K and B, this method returns the @ref GEMMLHSMatrixInfo and @ref GEMMRHSMatrixInfo to be used * * @param[in] m Number of rows LHS matrix @@ -116,5 +116,8 @@ public: protected: GPUTarget _target; }; +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute -#endif /*ARM_COMPUTE_ICLGEMMKERNELCONFIGURATION_H */ +#endif /* ARM_COMPUTE_ICL_GEMM_KERNEL_CONFIG_H */ diff --git a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.cpp b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.cpp index 52023dd835..9d11006703 100644 --- a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.cpp +++ b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.cpp @@ -21,40 +21,44 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.h" +#include "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/GPUTarget.h" -#include "src/core/CL/gemm/CLGEMMHelpers.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include <utility> namespace arm_compute { -namespace cl_gemm +namespace opencl { -CLGEMMDefaultConfigNativeBifrost::CLGEMMDefaultConfigNativeBifrost(GPUTarget gpu) - : ICLGEMMKernelConfiguration(gpu) +namespace kernels +{ +namespace gemm +{ +ClGemmDefaultConfigNativeBifrost::ClGemmDefaultConfigNativeBifrost(GPUTarget gpu) + : IClGemmKernelConfig(gpu) { } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) { - using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (CLGEMMDefaultConfigNativeBifrost::*)(unsigned int m, unsigned int n, unsigned int k, + using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigNativeBifrost::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G71(&CLGEMMDefaultConfigNativeBifrost::configure_G71_f32, - &CLGEMMDefaultConfigNativeBifrost::configure_G71_f32, // We use the F32 heuristic - &CLGEMMDefaultConfigNativeBifrost::configure_G71_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G71(&ClGemmDefaultConfigNativeBifrost::configure_G71_f32, + &ClGemmDefaultConfigNativeBifrost::configure_G71_f32, // We use the F32 heuristic + &ClGemmDefaultConfigNativeBifrost::configure_G71_u8); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G76(&CLGEMMDefaultConfigNativeBifrost::configure_G76_f32, - &CLGEMMDefaultConfigNativeBifrost::configure_G76_f32, // We use the F32 heuristic - &CLGEMMDefaultConfigNativeBifrost::configure_G76_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G76(&ClGemmDefaultConfigNativeBifrost::configure_G76_f32, + &ClGemmDefaultConfigNativeBifrost::configure_G76_f32, // We use the F32 heuristic + &ClGemmDefaultConfigNativeBifrost::configure_G76_u8); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G7x(&CLGEMMDefaultConfigNativeBifrost::configure_default_f32, - &CLGEMMDefaultConfigNativeBifrost::configure_default_f32, // We use the F32 heuristic - &CLGEMMDefaultConfigNativeBifrost::configure_default_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G7x(&ClGemmDefaultConfigNativeBifrost::configure_default_f32, + &ClGemmDefaultConfigNativeBifrost::configure_default_f32, // We use the F32 heuristic + &ClGemmDefaultConfigNativeBifrost::configure_default_u8); ConfigurationFunctionExecutorPtr func = nullptr; @@ -75,7 +79,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost return (this->*func)(m, n, k, b); } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost::configure_G71_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost::configure_G71_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -101,7 +105,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost::configure_G71_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost::configure_G71_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -155,7 +159,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost::configure_G76_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost::configure_G76_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -188,7 +192,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost::configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost::configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -221,7 +225,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost::configure_default_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost::configure_default_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -229,12 +233,14 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost return configure_lhs_rhs_info(m, n, 5, 4, 4, 1, 1, false, false, false, false); } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeBifrost::configure_default_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost::configure_default_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); return configure_lhs_rhs_info(m, n, 5, 2, 16, 1, 1, false, false, false, false); } -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute
\ No newline at end of file diff --git a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.h b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.h index 78d47a8195..385b96e40e 100644 --- a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.h +++ b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2020 Arm Limited. + * Copyright (c) 2019-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,24 +21,28 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEBIFROST_H -#define ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEBIFROST_H +#ifndef ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_BIFROST_H +#define ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_BIFROST_H -#include "src/core/CL/ICLGEMMKernelConfiguration.h" +#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h" namespace arm_compute { -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { /** Bifrost based OpenCL GEMMNative configuration */ -class CLGEMMDefaultConfigNativeBifrost final : public ICLGEMMKernelConfiguration +class ClGemmDefaultConfigNativeBifrost final : public IClGemmKernelConfig { public: /** Constructor * * @param[in] gpu GPU target */ - CLGEMMDefaultConfigNativeBifrost(GPUTarget gpu); + ClGemmDefaultConfigNativeBifrost(GPUTarget gpu); // Inherited overridden method std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) override; @@ -51,6 +55,8 @@ private: std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_default_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b); std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_default_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b); }; -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute -#endif /*ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEBIFROST_H */ +#endif /* ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_BIFROST_H */ diff --git a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.cpp b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.cpp index cf9bb1828f..e3c129e3be 100644 --- a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.cpp +++ b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.cpp @@ -21,39 +21,43 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.h" +#include "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/GPUTarget.h" -#include "src/core/CL/gemm/CLGEMMHelpers.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include <utility> namespace arm_compute { -namespace cl_gemm +namespace opencl { -CLGEMMDefaultConfigNativeMidgard::CLGEMMDefaultConfigNativeMidgard(GPUTarget gpu) - : ICLGEMMKernelConfiguration(gpu) +namespace kernels +{ +namespace gemm +{ +ClGemmDefaultConfigNativeMidgard::ClGemmDefaultConfigNativeMidgard(GPUTarget gpu) + : IClGemmKernelConfig(gpu) { } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeMidgard::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeMidgard::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) { - using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (CLGEMMDefaultConfigNativeMidgard::*)(unsigned int m, unsigned int n, unsigned int k, + using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigNativeMidgard::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b); CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_default(nullptr, nullptr, - &CLGEMMDefaultConfigNativeMidgard::default_q8); + &ClGemmDefaultConfigNativeMidgard::default_q8); auto func = configs_default.get_function(data_type); ARM_COMPUTE_ERROR_ON_MSG(func == nullptr, "Data type not support for GEMM"); return (this->*func)(m, n, k, b); } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeMidgard::default_q8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeMidgard::default_q8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -63,5 +67,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeMidgard return configure_lhs_rhs_info(m, n, m0, n0, 2, 1, 1, false, false, false, false); } -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute
\ No newline at end of file diff --git a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.h b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.h index 40c91d42b1..0ff5471f7c 100644 --- a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.h +++ b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020 Arm Limited. + * Copyright (c) 2020-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,24 +21,28 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEMIDGARD_H -#define ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEMIDGARD_H +#ifndef ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_MIDGARD_H +#define ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_MIDGARD_H -#include "src/core/CL/ICLGEMMKernelConfiguration.h" +#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h" namespace arm_compute { -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { /** Midgard based OpenCL GEMMNative configuration */ -class CLGEMMDefaultConfigNativeMidgard final : public ICLGEMMKernelConfiguration +class ClGemmDefaultConfigNativeMidgard final : public IClGemmKernelConfig { public: /** Constructor * * @param[in] gpu GPU target */ - CLGEMMDefaultConfigNativeMidgard(GPUTarget gpu); + ClGemmDefaultConfigNativeMidgard(GPUTarget gpu); // Inherited overridden method std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) override; @@ -46,6 +50,8 @@ public: private: std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> default_q8(unsigned int m, unsigned int n, unsigned int k, unsigned int b); }; -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute -#endif /*ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEMIDGARD_H */ +#endif /* ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_MIDGARD_H */ diff --git a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.cpp b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.cpp index 3b55be747f..92767aca52 100644 --- a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.cpp +++ b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.cpp @@ -21,39 +21,43 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.h" +#include "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/GPUTarget.h" -#include "src/core/CL/gemm/CLGEMMHelpers.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include <utility> namespace arm_compute { -namespace cl_gemm +namespace opencl { -CLGEMMDefaultConfigNativeValhall::CLGEMMDefaultConfigNativeValhall(GPUTarget gpu) - : ICLGEMMKernelConfiguration(gpu) +namespace kernels +{ +namespace gemm +{ +ClGemmDefaultConfigNativeValhall::ClGemmDefaultConfigNativeValhall(GPUTarget gpu) + : IClGemmKernelConfig(gpu) { } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeValhall::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeValhall::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) { - using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (CLGEMMDefaultConfigNativeValhall::*)(unsigned int m, unsigned int n, unsigned int k, + using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigNativeValhall::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_default(&CLGEMMDefaultConfigNativeValhall::configure_G77_f32, - &CLGEMMDefaultConfigNativeValhall::configure_G77_f16, - &CLGEMMDefaultConfigNativeValhall::configure_G77_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_default(&ClGemmDefaultConfigNativeValhall::configure_G77_f32, + &ClGemmDefaultConfigNativeValhall::configure_G77_f16, + &ClGemmDefaultConfigNativeValhall::configure_G77_u8); auto func = configs_default.get_function(data_type); ARM_COMPUTE_ERROR_ON_MSG(func == nullptr, "Data type not support for GEMM"); return (this->*func)(m, n, k, b); } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeValhall::configure_G77_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeValhall::configure_G77_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -79,7 +83,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeValhall } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeValhall::configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeValhall::configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -105,7 +109,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeValhall } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeValhall::configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeValhall::configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -158,5 +162,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigNativeValhall } } } -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute
\ No newline at end of file diff --git a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.h b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.h index 08d2d57a3e..17e4c9d339 100644 --- a/src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.h +++ b/src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020 Arm Limited. + * Copyright (c) 2020-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,24 +21,28 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEVALHALL_H -#define ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEVALHALL_H +#ifndef ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_VALHALL_H +#define ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_VALHALL_H -#include "src/core/CL/ICLGEMMKernelConfiguration.h" +#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h" namespace arm_compute { -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { /** Valhall based OpenCL GEMMNative configuration */ -class CLGEMMDefaultConfigNativeValhall final : public ICLGEMMKernelConfiguration +class ClGemmDefaultConfigNativeValhall final : public IClGemmKernelConfig { public: /** Constructor * * @param[in] gpu GPU target */ - CLGEMMDefaultConfigNativeValhall(GPUTarget gpu); + ClGemmDefaultConfigNativeValhall(GPUTarget gpu); // Inherited overridden method std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) override; @@ -48,6 +52,8 @@ private: std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b); std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b); }; -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute -#endif /*ARM_COMPUTE_CLGEMMDEFAULTCONFIGNATIVEVALHALL_H */ +#endif /* ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_NATIVE_VALHALL_H */ diff --git a/src/core/CL/gemm/native/CLGEMMNativeKernelConfiguration.h b/src/core/gpu/cl/kernels/gemm/native/ClGemmNativeKernelConfig.h index 39a534e817..ff6a0128af 100644 --- a/src/core/CL/gemm/native/CLGEMMNativeKernelConfiguration.h +++ b/src/core/gpu/cl/kernels/gemm/native/ClGemmNativeKernelConfig.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2020 Arm Limited. + * Copyright (c) 2019-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,22 +21,26 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_CLGEMMNATIVEKERNELCONFIGURATION_H -#define ARM_COMPUTE_CLGEMMNATIVEKERNELCONFIGURATION_H +#ifndef ARM_COMPUTE_CL_GEMM_NATIVE_KERNEL_CONFIGURATION_H +#define ARM_COMPUTE_CL_GEMM_NATIVE_KERNEL_CONFIGURATION_H -#include "src/core/CL/ICLGEMMKernelConfiguration.h" -#include "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.h" -#include "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.h" -#include "src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.h" +#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h" +#include "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.h" +#include "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.h" +#include "src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.h" #include <memory> namespace arm_compute { -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { /** CLGEMMNative factory class */ -class CLGEMMNativeKernelConfigurationFactory final +class ClGemmNativeKernelConfigurationFactory final { public: /** Static method to construct CLGEMMNative kernel object accordingly with the GPU target @@ -45,21 +49,23 @@ public: * * @return CLGEMMNative kernel configuration class */ - static std::unique_ptr<ICLGEMMKernelConfiguration> create(GPUTarget gpu) + static std::unique_ptr<IClGemmKernelConfig> create(GPUTarget gpu) { switch(get_arch_from_target(gpu)) { case GPUTarget::MIDGARD: - return std::make_unique<CLGEMMDefaultConfigNativeMidgard>(gpu); + return std::make_unique<ClGemmDefaultConfigNativeMidgard>(gpu); case GPUTarget::BIFROST: - return std::make_unique<CLGEMMDefaultConfigNativeBifrost>(gpu); + return std::make_unique<ClGemmDefaultConfigNativeBifrost>(gpu); case GPUTarget::VALHALL: - return std::make_unique<CLGEMMDefaultConfigNativeValhall>(gpu); + return std::make_unique<ClGemmDefaultConfigNativeValhall>(gpu); default: ARM_COMPUTE_ERROR("Not supported GPU target"); } } }; -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute -#endif /*ARM_COMPUTE_CLGEMMNATIVEKERNELCONFIGURATION_H */ +#endif /*ARM_COMPUTE_CL_GEMM_NATIVE_KERNEL_CONFIGURATION_H */ diff --git a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.cpp b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.cpp index 5877ab96e7..b030913a87 100644 --- a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.cpp +++ b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.cpp @@ -21,7 +21,7 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.h" +#include "src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" @@ -29,36 +29,40 @@ #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "src/core/CL/gemm/CLGEMMHelpers.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include <utility> namespace arm_compute { -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { using namespace arm_compute::misc::shape_calculator; -CLGEMMDefaultConfigReshapedBifrost::CLGEMMDefaultConfigReshapedBifrost(GPUTarget gpu) - : ICLGEMMKernelConfiguration(gpu) +ClGemmDefaultConfigReshapedBifrost::ClGemmDefaultConfigReshapedBifrost(GPUTarget gpu) + : IClGemmKernelConfig(gpu) { } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) { - using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (CLGEMMDefaultConfigReshapedBifrost::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b); + using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigReshapedBifrost::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G7x(&CLGEMMDefaultConfigReshapedBifrost::configure_G7x_f32, - &CLGEMMDefaultConfigReshapedBifrost::configure_G7x_f16, - &CLGEMMDefaultConfigReshapedBifrost::configure_G7x_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G7x(&ClGemmDefaultConfigReshapedBifrost::configure_G7x_f32, + &ClGemmDefaultConfigReshapedBifrost::configure_G7x_f16, + &ClGemmDefaultConfigReshapedBifrost::configure_G7x_u8); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G52(&CLGEMMDefaultConfigReshapedBifrost::configure_G52_f32, - &CLGEMMDefaultConfigReshapedBifrost::configure_G52_f16, - &CLGEMMDefaultConfigReshapedBifrost::configure_G7x_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G52(&ClGemmDefaultConfigReshapedBifrost::configure_G52_f32, + &ClGemmDefaultConfigReshapedBifrost::configure_G52_f16, + &ClGemmDefaultConfigReshapedBifrost::configure_G7x_u8); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G76(&CLGEMMDefaultConfigReshapedBifrost::configure_G76_f32, - &CLGEMMDefaultConfigReshapedBifrost::configure_G76_f16, - &CLGEMMDefaultConfigReshapedBifrost::configure_G76_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G76(&ClGemmDefaultConfigReshapedBifrost::configure_G76_f32, + &ClGemmDefaultConfigReshapedBifrost::configure_G76_f16, + &ClGemmDefaultConfigReshapedBifrost::configure_G76_u8); ConfigurationFunctionExecutorPtr func = nullptr; @@ -79,7 +83,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro return (this->*func)(m, n, k, b); } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G7x_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G7x_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -94,7 +98,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G7x_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G7x_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -109,7 +113,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G7x_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G7x_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -138,7 +142,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G52_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G52_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { const float r_mn = static_cast<float>(m) / static_cast<float>(n); const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f; @@ -237,7 +241,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G52_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G52_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); @@ -253,7 +257,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G76_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G76_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -303,7 +307,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G76_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G76_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f; const float r_mk = static_cast<float>(m) / static_cast<float>(k); @@ -332,7 +336,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifrost::configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedBifrost::configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -346,5 +350,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedBifro return configure_lhs_rhs_info(m, n, 4, 4, 16, 2, 2, false, true, false, true); } } -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute diff --git a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.h b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.h index 814b831b69..52e6ce3f48 100644 --- a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.h +++ b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2020 Arm Limited. + * Copyright (c) 2019-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,24 +21,28 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDBIFROST_H -#define ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDBIFROST_H +#ifndef ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_BIFROST_H +#define ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_BIFROST_H -#include "src/core/CL/ICLGEMMKernelConfiguration.h" +#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h" namespace arm_compute { -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { /** Bifrost based OpenCL GEMMReshaped configuration */ -class CLGEMMDefaultConfigReshapedBifrost final : public ICLGEMMKernelConfiguration +class ClGemmDefaultConfigReshapedBifrost final : public IClGemmKernelConfig { public: /** Constructor * * @param[in] gpu GPU target */ - CLGEMMDefaultConfigReshapedBifrost(GPUTarget gpu); + ClGemmDefaultConfigReshapedBifrost(GPUTarget gpu); // Inherited overridden method std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) override; @@ -53,6 +57,8 @@ private: std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G7x_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b); std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b); }; -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute -#endif /*ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDBIFROST_H */ +#endif /* ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_BIFROST_H */ diff --git a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.cpp b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.cpp index b07092ab83..57e42c92b3 100644 --- a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.cpp +++ b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.cpp @@ -21,35 +21,39 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.h" +#include "src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/GPUTarget.h" -#include "src/core/CL/gemm/CLGEMMHelpers.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include <utility> namespace arm_compute { -namespace cl_gemm +namespace opencl { -CLGEMMDefaultConfigReshapedValhall::CLGEMMDefaultConfigReshapedValhall(GPUTarget gpu) - : ICLGEMMKernelConfiguration(gpu) +namespace kernels +{ +namespace gemm +{ +ClGemmDefaultConfigReshapedValhall::ClGemmDefaultConfigReshapedValhall(GPUTarget gpu) + : IClGemmKernelConfig(gpu) { } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValhall::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedValhall::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) { - using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (CLGEMMDefaultConfigReshapedValhall::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b); + using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigReshapedValhall::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G77(&CLGEMMDefaultConfigReshapedValhall::configure_G77_f32, - &CLGEMMDefaultConfigReshapedValhall::configure_G77_f16, - &CLGEMMDefaultConfigReshapedValhall::configure_G77_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G77(&ClGemmDefaultConfigReshapedValhall::configure_G77_f32, + &ClGemmDefaultConfigReshapedValhall::configure_G77_f16, + &ClGemmDefaultConfigReshapedValhall::configure_G77_u8); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G78(&CLGEMMDefaultConfigReshapedValhall::configure_G78_f32, - &CLGEMMDefaultConfigReshapedValhall::configure_G78_f16, - &CLGEMMDefaultConfigReshapedValhall::configure_G77_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G78(&ClGemmDefaultConfigReshapedValhall::configure_G78_f32, + &ClGemmDefaultConfigReshapedValhall::configure_G78_f16, + &ClGemmDefaultConfigReshapedValhall::configure_G77_u8); ConfigurationFunctionExecutorPtr func = nullptr; @@ -68,7 +72,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValha return (this->*func)(m, n, k, b); } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValhall::configure_G77_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedValhall::configure_G77_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -83,7 +87,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValha } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValhall::configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedValhall::configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -208,7 +212,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValha } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValhall::configure_G78_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedValhall::configure_G78_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { const float r_mn = static_cast<float>(m) / static_cast<float>(n); const float r_mk = static_cast<float>(m) / static_cast<float>(k); @@ -449,7 +453,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValha } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValhall::configure_G78_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedValhall::configure_G78_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { const float r_mn = static_cast<float>(m) / static_cast<float>(n); const float r_nk = static_cast<float>(n) / static_cast<float>(k); @@ -514,7 +518,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValha } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValhall::configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedValhall::configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -528,5 +532,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedValha return configure_lhs_rhs_info(m, n, 4, 4, 16, 2, 2, 0, 1, 0, 1); } } -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute diff --git a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.h b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.h index 52b83b09b6..588cd64e0e 100644 --- a/src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.h +++ b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.h @@ -21,24 +21,28 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDVALHALL_H -#define ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDVALHALL_H +#ifndef ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_VALHALL_H +#define ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_VALHALL_H -#include "src/core/CL/ICLGEMMKernelConfiguration.h" +#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h" namespace arm_compute { -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { /** Valhall based OpenCL GEMMReshaped configuration */ -class CLGEMMDefaultConfigReshapedValhall final : public ICLGEMMKernelConfiguration +class ClGemmDefaultConfigReshapedValhall final : public IClGemmKernelConfig { public: /** Constructor * * @param[in] gpu GPU target */ - CLGEMMDefaultConfigReshapedValhall(GPUTarget gpu); + ClGemmDefaultConfigReshapedValhall(GPUTarget gpu); // Inherited overridden method std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) override; @@ -50,6 +54,8 @@ private: std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G78_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b); std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b); }; -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute -#endif /*ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDVALHALL_H */ +#endif /* ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_VALHALL_H */ diff --git a/src/core/CL/gemm/reshaped/CLGEMMReshapedKernelConfiguration.h b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmReshapedKernelConfig.h index de60698a91..c990c89a91 100644 --- a/src/core/CL/gemm/reshaped/CLGEMMReshapedKernelConfiguration.h +++ b/src/core/gpu/cl/kernels/gemm/reshaped/ClGemmReshapedKernelConfig.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2020 Arm Limited. + * Copyright (c) 2019-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,21 +21,25 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_CLGEMMRESHAPEDKERNELCONFIGURATION_H -#define ARM_COMPUTE_CLGEMMRESHAPEDKERNELCONFIGURATION_H +#ifndef ARM_COMPUTE_CL_GEMM_RESHAPED_KERNEL_CONFIGURATION_H +#define ARM_COMPUTE_CL_GEMM_RESHAPED_KERNEL_CONFIGURATION_H -#include "src/core/CL/ICLGEMMKernelConfiguration.h" -#include "src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.h" -#include "src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.h" +#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h" +#include "src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.h" +#include "src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.h" #include <memory> namespace arm_compute { -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { /** CLGEMMReshaped factory class */ -class CLGEMMReshapedKernelConfigurationFactory final +class ClGemmReshapedKernelConfigurationFactory final { public: /** Static method to call the CLGEMMReshaped kernel configuration class accordingly with the GPU target @@ -44,20 +48,22 @@ public: * * @return CLGEMMReshaped kernel configuration class */ - static std::unique_ptr<ICLGEMMKernelConfiguration> create(GPUTarget gpu) + static std::unique_ptr<IClGemmKernelConfig> create(GPUTarget gpu) { switch(get_arch_from_target(gpu)) { case GPUTarget::MIDGARD: case GPUTarget::BIFROST: - return std::make_unique<CLGEMMDefaultConfigReshapedBifrost>(gpu); + return std::make_unique<ClGemmDefaultConfigReshapedBifrost>(gpu); case GPUTarget::VALHALL: - return std::make_unique<CLGEMMDefaultConfigReshapedValhall>(gpu); + return std::make_unique<ClGemmDefaultConfigReshapedValhall>(gpu); default: ARM_COMPUTE_ERROR("Not supported GPU target"); } } }; -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute -#endif /*ARM_COMPUTE_CLGEMMRESHAPEDKERNELCONFIGURATION_H */ +#endif /* ARM_COMPUTE_CL_GEMM_RESHAPED_KERNEL_CONFIGURATION_H */ diff --git a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.cpp b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.cpp index 3645a0e141..7ed6b39f3e 100644 --- a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.cpp +++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.cpp @@ -21,7 +21,7 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.h" +#include "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" @@ -29,41 +29,45 @@ #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "src/core/CL/gemm/CLGEMMHelpers.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include <utility> namespace arm_compute { -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { using namespace arm_compute::misc::shape_calculator; -CLGEMMDefaultConfigReshapedRHSOnlyBifrost::CLGEMMDefaultConfigReshapedRHSOnlyBifrost(GPUTarget gpu) - : ICLGEMMKernelConfiguration(gpu) +ClGemmDefaultConfigReshapedRhsOnlyBifrost::ClGemmDefaultConfigReshapedRhsOnlyBifrost(GPUTarget gpu) + : IClGemmKernelConfig(gpu) { } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) { - using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (CLGEMMDefaultConfigReshapedRHSOnlyBifrost::*)(unsigned int m, unsigned int n, unsigned int k, + using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigReshapedRhsOnlyBifrost::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G51(&CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G51_f32, - &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G51_f16, - &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G51_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G51(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f32, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f16, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_u8); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G52(&CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G52_f32, - &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G52_f16, - &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G7x_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G52(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f32, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f16, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_u8); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G76(&CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G76_f32, - &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G76_f16, - &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G76_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G76(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f32, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f16, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_u8); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G7x(&CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G7x_f32, - &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G7x_f16, - &CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G7x_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G7x(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f32, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f16, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_u8); ConfigurationFunctionExecutorPtr func = nullptr; @@ -87,7 +91,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn return (this->*func)(m, n, k, b); } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G7x_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -109,7 +113,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G76_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -182,7 +186,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G52_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f; const float r_nk = static_cast<float>(n) / static_cast<float>(k); @@ -226,7 +230,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G51_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -243,7 +247,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G7x_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -267,7 +271,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G52_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { const float r_mn = static_cast<float>(m) / static_cast<float>(n); const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f; @@ -354,7 +358,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G76_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); @@ -426,7 +430,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G51_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -443,7 +447,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G7x_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -475,7 +479,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -491,7 +495,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyBifrost::configure_G51_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -508,5 +512,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute diff --git a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.h b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.h index db89d8317c..7b1a1fb04d 100644 --- a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.h +++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2020 Arm Limited. + * Copyright (c) 2019-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,24 +21,28 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDRHSONLYBIFROST_H -#define ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDRHSONLYBIFROST_H +#ifndef ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_RHS_ONLY_BIFROST_H +#define ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_RHS_ONLY_BIFROST_H -#include "src/core/CL/ICLGEMMKernelConfiguration.h" +#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h" namespace arm_compute { -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { /** Bifrost based OpenCL GEMMReshapedOnlyRHS configuration */ -class CLGEMMDefaultConfigReshapedRHSOnlyBifrost final : public ICLGEMMKernelConfiguration +class ClGemmDefaultConfigReshapedRhsOnlyBifrost final : public IClGemmKernelConfig { public: /** Constructor * * @param[in] gpu GPU target */ - CLGEMMDefaultConfigReshapedRHSOnlyBifrost(GPUTarget gpu); + ClGemmDefaultConfigReshapedRhsOnlyBifrost(GPUTarget gpu); // Inherited overridden method std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) override; @@ -56,6 +60,8 @@ private: std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b); std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G51_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b); }; -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute -#endif /*ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDRHSONLYBIFROST_H */ +#endif /* ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_RHS_ONLY_BIFROST_H */ diff --git a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.cpp b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.cpp index a3f0509eda..4c6e633896 100644 --- a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.cpp +++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.cpp @@ -21,7 +21,7 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.h" +#include "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" @@ -29,33 +29,37 @@ #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "src/core/CL/gemm/CLGEMMHelpers.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include <utility> namespace arm_compute { -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { using namespace arm_compute::misc::shape_calculator; -CLGEMMDefaultConfigReshapedRHSOnlyValhall::CLGEMMDefaultConfigReshapedRHSOnlyValhall(GPUTarget gpu) - : ICLGEMMKernelConfiguration(gpu) +ClGemmDefaultConfigReshapedRhsOnlyValhall::ClGemmDefaultConfigReshapedRhsOnlyValhall(GPUTarget gpu) + : IClGemmKernelConfig(gpu) { } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) { - using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (CLGEMMDefaultConfigReshapedRHSOnlyValhall::*)(unsigned int m, unsigned int n, unsigned int k, + using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigReshapedRhsOnlyValhall::*)(unsigned int m, unsigned int n, unsigned int k, unsigned int b); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G77(&CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G77_f32, - &CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G77_f16, - &CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G77_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G77(&ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f32, + &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f16, + &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_u8); - CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G78(&CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G78_f32, - &CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G78_f16, - &CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G77_u8); + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G78(&ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f32, + &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f16, + &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_u8); ConfigurationFunctionExecutorPtr func = nullptr; @@ -74,7 +78,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn return (this->*func)(m, n, k, b); } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G77_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { if(m == 1) { @@ -180,7 +184,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -230,7 +234,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_UNUSED(b); @@ -254,7 +258,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G78_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { const float r_mn = static_cast<float>(m) / static_cast<float>(n); const float r_mk = static_cast<float>(m) / static_cast<float>(k); @@ -397,7 +401,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } -std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOnlyValhall::configure_G78_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) { const float r_mn = static_cast<float>(m) / static_cast<float>(n); const float r_mk = static_cast<float>(m) / static_cast<float>(k); @@ -560,5 +564,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> CLGEMMDefaultConfigReshapedRHSOn } } } -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute diff --git a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.h b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.h index a3b556c441..6a11ddb748 100644 --- a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.h +++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.h @@ -21,24 +21,28 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDRHSONLYVALHALL_H -#define ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDRHSONLYVALHALL_H +#ifndef ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_RHS_ONLY_VALHALL_H +#define ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_RHS_ONLY_VALHALL_H -#include "src/core/CL/ICLGEMMKernelConfiguration.h" +#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h" namespace arm_compute { -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { /** Valhall based OpenCL GEMMReshapedOnlyRHS configuration */ -class CLGEMMDefaultConfigReshapedRHSOnlyValhall final : public ICLGEMMKernelConfiguration +class ClGemmDefaultConfigReshapedRhsOnlyValhall final : public IClGemmKernelConfig { public: /** Constructor * * @param[in] gpu GPU target */ - CLGEMMDefaultConfigReshapedRHSOnlyValhall(GPUTarget gpu); + ClGemmDefaultConfigReshapedRhsOnlyValhall(GPUTarget gpu); // Inherited overridden method std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) override; @@ -50,6 +54,8 @@ private: std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G78_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b); std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b); }; -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute -#endif /*ARM_COMPUTE_CLGEMMDEFAULTCONFIGRESHAPEDRHSONLYVALHALL_H */ +#endif /* ARM_COMPUTE_CL_GEMM_DEFAULT_CONFIG_RESHAPED_RHS_ONLY_VALHALL_H */ diff --git a/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyBifrost.cpp b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyBifrost.cpp new file mode 100644 index 0000000000..7ed6b39f3e --- /dev/null +++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyBifrost.cpp @@ -0,0 +1,518 @@ +/* + * Copyright (c) 2019-2021 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. + */ +#include "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/GPUTarget.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" + +#include <utility> + +namespace arm_compute +{ +namespace opencl +{ +namespace kernels +{ +namespace gemm +{ +using namespace arm_compute::misc::shape_calculator; + +ClGemmDefaultConfigReshapedRhsOnlyBifrost::ClGemmDefaultConfigReshapedRhsOnlyBifrost(GPUTarget gpu) + : IClGemmKernelConfig(gpu) +{ +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) +{ + using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigReshapedRhsOnlyBifrost::*)(unsigned int m, unsigned int n, unsigned int k, + unsigned int b); + + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G51(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f32, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f16, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_u8); + + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G52(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f32, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f16, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_u8); + + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G76(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f32, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f16, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_u8); + + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G7x(&ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f32, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f16, + &ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_u8); + + ConfigurationFunctionExecutorPtr func = nullptr; + + switch(_target) + { + case GPUTarget::G76: + func = configs_G76.get_function(data_type); + break; + case GPUTarget::G51: + func = configs_G51.get_function(data_type); + break; + case GPUTarget::G52: + func = configs_G52.get_function(data_type); + break; + default: + func = configs_G7x.get_function(data_type); + break; + } + + ARM_COMPUTE_ERROR_ON_MSG(func == nullptr, "Data type not support for GEMM"); + return (this->*func)(m, n, k, b); +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + ARM_COMPUTE_UNUSED(k); + ARM_COMPUTE_UNUSED(b); + + if(m == 1) + { + if(n <= 2548) + { + return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 4, false, true, false, true, false); + } + else + { + return configure_lhs_rhs_info(m, n, 1, 4, 16, 1, 8, false, true, false, true, false); + } + } + else + { + return configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 4, false, true, false, true); + } +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + ARM_COMPUTE_UNUSED(k); + ARM_COMPUTE_UNUSED(b); + + GEMMLHSMatrixInfo lhs_info_buf; + GEMMRHSMatrixInfo rhs_info_buf; + GEMMLHSMatrixInfo lhs_info_img; + GEMMRHSMatrixInfo rhs_info_img; + + const bool is_workload_big = ((m * n * b) / 16) >= 2048; + + if(m == 1) + { + if(n >= 8192) + { + const unsigned int h0 = std::max(n / 4, 1U); + return configure_lhs_rhs_info(m, n, 1, 4, 8, 1, h0, false, true, false, true, false); + } + else + { + const unsigned int h0 = std::max(n / 2, 1U); + if(n <= 204) + { + return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, h0, false, true, false, true, false); + } + else + { + return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, h0, false, true, false, true, false); + } + } + } + else + { + const int h0 = std::max(std::min(static_cast<int>(n / 4), static_cast<int>(16)), static_cast<int>(1)); + if(is_workload_big) + { + std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 4, 4, 4, 1, h0, false, true, false, true); + } + else + { + std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 2, 4, 8, 1, h0, false, true, false, true); + } + } + + // Get lhs_info/rhs_info in case of OpenCL image + const int h0 = std::max(std::min(static_cast<int>(n / 4), static_cast<int>(16)), static_cast<int>(1)); + if(is_workload_big) + { + std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 4, 4, 4, 1, h0, false, true, false, false, true); + } + else + { + std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 2, 4, 8, 1, h0, false, true, false, true, true); + } + + const TensorInfo tensor_rhs_info(TensorShape(n, k, b), 1, DataType::F32); + const TensorShape shape = compute_rhs_reshaped_shape(tensor_rhs_info, rhs_info_img); + const TensorInfo tensor_reshaped_info(shape, 1, DataType::F32); + + // In case of vector by matrix or small workloads, we use the OpenCL buffer rather than the OpenCL image2d + const bool use_cl_image2d = ((m == 1) || ((((m * n * b) / 16) < 2048) && n < 128)) ? false : true; + + if(bool(validate_image2d_support_on_rhs(tensor_reshaped_info, rhs_info_img)) && use_cl_image2d) + { + return std::make_pair(lhs_info_img, rhs_info_img); + } + else + { + return std::make_pair(lhs_info_buf, rhs_info_buf); + } +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f; + const float r_nk = static_cast<float>(n) / static_cast<float>(k); + + GEMMLHSMatrixInfo lhs_info_buf; + GEMMRHSMatrixInfo rhs_info_buf; + GEMMLHSMatrixInfo lhs_info_img; + GEMMRHSMatrixInfo rhs_info_img; + + if(m == 1) + { + if(r_nk <= 0.4664f) + { + return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 16, false, true, false, true, false); + } + else + { + std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 1, 4, 8, 1, 16, false, true, false, true, true); + std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 1, 4, 8, 1, 16, false, true, false, true, false); + + return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img), + std::make_pair(lhs_info_buf, rhs_info_buf), + n, k, b, DataType::F32); + } + } + else + { + if(workload <= 274.4000f) + { + return configure_lhs_rhs_info(m, n, 2, 2, 4, 1, 16, false, false, false, true, false); + } + else + { + std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 2, false, false, false, true, true); + std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 2, false, false, false, true, false); + + return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img), + std::make_pair(lhs_info_buf, rhs_info_buf), + n, k, b, DataType::F32); + } + } +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + ARM_COMPUTE_UNUSED(k); + ARM_COMPUTE_UNUSED(b); + + if(m == 1) + { + const unsigned int n0 = n < 1280 ? 2 : 4; + const unsigned int h0 = std::max(n / n0, 1U); + return configure_lhs_rhs_info(m, n, 1, n0, 4, 1, h0, false, true, false, true); + } + else + { + return configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 2, false, true, false, true); + } +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + ARM_COMPUTE_UNUSED(k); + ARM_COMPUTE_UNUSED(b); + + if(m == 1) + { + if(n > 2048) + { + const unsigned int h0 = std::max(n / 4, 1U); + return configure_lhs_rhs_info(m, n, 1, 4, 4, 1, h0, false, true, false, true); + } + else + { + const unsigned int h0 = std::max(n / 2, 1U); + return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, h0, false, true, false, true); + } + } + else + { + return configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 4, false, true, false, true); + } +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G52_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + const float r_mn = static_cast<float>(m) / static_cast<float>(n); + const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f; + const float r_mk = static_cast<float>(m) / static_cast<float>(k); + const float r_nk = static_cast<float>(n) / static_cast<float>(k); + + GEMMLHSMatrixInfo lhs_info_buf; + GEMMRHSMatrixInfo rhs_info_buf; + GEMMLHSMatrixInfo lhs_info_img; + GEMMRHSMatrixInfo rhs_info_img; + + if(m == 1) + { + std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 1, 4, 16, 1, 16, false, true, false, false, false); + + if(r_mk <= 0.0026f) + { + if(r_nk <= 0.4664f) + { + return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 32, false, true, false, true, false); + } + else + { + std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 1, 4, 16, 1, 16, false, true, false, false, true); + return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img), + std::make_pair(lhs_info_buf, rhs_info_buf), + n, k, b, DataType::F16); + } + } + else + { + if(r_mk <= 0.0148f) + { + return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 32, false, true, false, true, false); + } + else + { + std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 1, 4, 16, 1, 16, false, true, false, false, true); + return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img), + std::make_pair(lhs_info_buf, rhs_info_buf), + n, k, b, DataType::F16); + } + } + } + else + { + std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 5, 8, 4, 1, 2, false, false, false, false, false); + + if(workload <= 362.6000f) + { + return configure_lhs_rhs_info(m, n, 2, 2, 8, 1, 16, false, false, false, true, false); + } + else + { + if(r_mn <= 22.6067f) + { + if(workload <= 708.8000f) + { + std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 5, 4, 4, 1, 2, false, false, false, false, true); + return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img), + std::make_pair(lhs_info_buf, rhs_info_buf), + n, k, b, DataType::F16); + } + else + { + return configure_lhs_rhs_info(m, n, 5, 8, 2, 1, 16, false, false, false, false, false); + } + } + else + { + if(r_nk <= 0.0917f) + { + return configure_lhs_rhs_info(m, n, 2, 2, 8, 1, 16, false, false, false, true, false); + } + else + { + std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 5, 4, 4, 1, 2, false, false, false, false, true); + return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img), + std::make_pair(lhs_info_buf, rhs_info_buf), + n, k, b, DataType::F16); + } + } + } + } +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + ARM_COMPUTE_UNUSED(k); + + if(m == 1) + { + return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 32, false, true, false, true, false); + } + else + { + const float r_mn = static_cast<float>(m) / static_cast<float>(n); + const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f; + + if(workload <= 7449.60f) + { + if(workload <= 691.60f) + { + return configure_lhs_rhs_info(m, n, 2, 2, 8, 1, 8, false, false, false, false, false); + } + else + { + if(workload <= 4155.20f) + { + return configure_lhs_rhs_info(m, n, 5, 2, 8, 1, 16, false, false, false, false, false); + } + else + { + return configure_lhs_rhs_info(m, n, 5, 8, 2, 1, 32, false, false, false, false, false); + } + } + } + else + { + if(workload <= 16300.80f) + { + if(r_mn <= 44.56f) + { + GEMMLHSMatrixInfo lhs_info_buf; + GEMMRHSMatrixInfo rhs_info_buf; + GEMMLHSMatrixInfo lhs_info_img; + GEMMRHSMatrixInfo rhs_info_img; + + std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 8, 4, 4, 1, 1, false, true, false, false, true); + std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 5, 2, 8, 1, 16, false, false, false, false, false); + + return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img), + std::make_pair(lhs_info_buf, rhs_info_buf), + n, k, b, DataType::F16); + } + else + { + return configure_lhs_rhs_info(m, n, 5, 2, 8, 1, 16, false, false, false, false, false); + } + } + else + { + GEMMLHSMatrixInfo lhs_info_buf; + GEMMRHSMatrixInfo rhs_info_buf; + GEMMLHSMatrixInfo lhs_info_img; + GEMMRHSMatrixInfo rhs_info_img; + + std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 5, 4, 4, 1, 2, false, true, false, false, true); + std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 5, 2, 8, 1, 16, false, false, false, false, false); + + return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img), + std::make_pair(lhs_info_buf, rhs_info_buf), + n, k, b, DataType::F16); + } + } + } +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + ARM_COMPUTE_UNUSED(k); + ARM_COMPUTE_UNUSED(b); + + if(m == 1) + { + const unsigned int n0 = n < 1280 ? 2 : 4; + const unsigned int h0 = std::max(n / n0, 1U); + return configure_lhs_rhs_info(m, n, 1, n0, 8, 1, h0, false, true, false, true); + } + else + { + return configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 2, false, true, false, true); + } +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G7x_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + ARM_COMPUTE_UNUSED(k); + ARM_COMPUTE_UNUSED(b); + + if(dot8_supported(CLKernelLibrary::get().get_device())) + { + if(m == 1) + { + const unsigned int h0 = std::max(n / 2, 1U); + return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, h0, false, true, false, true); + } + else + { + const unsigned int h0 = std::max(n / 4, 1U); + return configure_lhs_rhs_info(m, n, 4, 4, 16, 1, h0, false, true, false, true); + } + } + else + { + const int h0 = std::max(std::min(static_cast<int>(n / 2), static_cast<int>(128)), static_cast<int>(1)); + if(m == 1) + { + return configure_lhs_rhs_info(m, n, 1, 2, 4, 1, h0, false, true, false, true); + } + else + { + return configure_lhs_rhs_info(m, n, 4, 2, 16, 1, h0, false, true, false, true); + } + } +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G76_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + ARM_COMPUTE_UNUSED(k); + ARM_COMPUTE_UNUSED(b); + + if(m == 1) + { + const unsigned int h0 = std::max(n / 2, 1U); + return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, h0, false, true, false, true); + } + else + { + return configure_lhs_rhs_info(m, n, 4, 4, 16, 1, 2, false, true, false, true); + } +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyBifrost::configure_G51_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + ARM_COMPUTE_UNUSED(k); + ARM_COMPUTE_UNUSED(b); + + if(m == 1) + { + const unsigned int h0 = std::max(n / 2, 1U); + return configure_lhs_rhs_info(m, n, 1, 4, 16, 1, h0, false, true, false, true); + } + else + { + const unsigned int h0 = std::max(n / 2, 1U); + return configure_lhs_rhs_info(m, n, 4, 2, 16, 1, h0, false, true, false, true); + } +} + +} // namespace gemm +} // namespace kernels +} // namespace opencl +} // namespace arm_compute diff --git a/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyValhall.cpp b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyValhall.cpp new file mode 100644 index 0000000000..4c6e633896 --- /dev/null +++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyValhall.cpp @@ -0,0 +1,570 @@ +/* + * Copyright (c) 2020-2021 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. + */ +#include "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/GPUTarget.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" + +#include <utility> + +namespace arm_compute +{ +namespace opencl +{ +namespace kernels +{ +namespace gemm +{ +using namespace arm_compute::misc::shape_calculator; + +ClGemmDefaultConfigReshapedRhsOnlyValhall::ClGemmDefaultConfigReshapedRhsOnlyValhall(GPUTarget gpu) + : IClGemmKernelConfig(gpu) +{ +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type) +{ + using ConfigurationFunctionExecutorPtr = std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> (ClGemmDefaultConfigReshapedRhsOnlyValhall::*)(unsigned int m, unsigned int n, unsigned int k, + unsigned int b); + + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G77(&ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f32, + &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f16, + &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_u8); + + CLGEMMConfigArray<ConfigurationFunctionExecutorPtr> configs_G78(&ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f32, + &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f16, + &ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_u8); + + ConfigurationFunctionExecutorPtr func = nullptr; + + switch(_target) + { + case GPUTarget::G78: + func = configs_G78.get_function(data_type); + break; + case GPUTarget::G77: + default: + func = configs_G77.get_function(data_type); + break; + } + + ARM_COMPUTE_ERROR_ON_MSG(func == nullptr, "Data type not support for GEMM"); + return (this->*func)(m, n, k, b); +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + if(m == 1) + { + const float r_mn = static_cast<float>(m) / static_cast<float>(n); + const float r_mk = static_cast<float>(m) / static_cast<float>(k); + + if(r_mk <= 0.0064484127797186375) + { + if(r_mn <= 0.0028273810748942196) + { + GEMMLHSMatrixInfo lhs_info_buf; + GEMMRHSMatrixInfo rhs_info_buf; + GEMMLHSMatrixInfo lhs_info_img; + GEMMRHSMatrixInfo rhs_info_img; + + const unsigned int h0 = std::max(n / 4, 1U); + std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 1, 4, 8, 1, 16, 0, 1, 0, 0, 1); + std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 1, 4, 4, 1, h0, 0, 1, 0, 1, 0); + + return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img), + std::make_pair(lhs_info_buf, rhs_info_buf), + n, k, b, DataType::F32); + } + else + { + return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 8, 0, 1, 0, 0, 0); + } + } + else + { + if(r_mk <= 0.020312500186264515) + { + return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 4, 0, 1, 0, 0, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 1, 4, 16, 1, 16, 0, 1, 0, 1, 0); + } + } + } + else + { + const float r_mn = static_cast<float>(m) / static_cast<float>(n); + const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f; + const float r_mk = static_cast<float>(m) / static_cast<float>(k); + + if(workload <= 1999.2000122070312) + { + if(workload <= 747.1999816894531) + { + return configure_lhs_rhs_info(m, n, 2, 2, 4, 1, 8, 0, 1, 0, 1, 0); + } + else + { + GEMMLHSMatrixInfo lhs_info_buf; + GEMMRHSMatrixInfo rhs_info_buf; + GEMMLHSMatrixInfo lhs_info_img; + GEMMRHSMatrixInfo rhs_info_img; + std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 2, 4, 8, 1, 2, 0, 0, 0, 1, 1); + std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 2, 2, 4, 1, 8, 0, 1, 0, 1, 0); + + return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img), + std::make_pair(lhs_info_buf, rhs_info_buf), + n, k, b, DataType::F32); + } + } + else + { + if(r_mn <= 0.03348214365541935) + { + if(r_mk <= 0.028125000186264515) + { + return configure_lhs_rhs_info(m, n, 2, 2, 4, 1, 8, 0, 1, 0, 1, 0); + } + else + { + GEMMLHSMatrixInfo lhs_info_buf; + GEMMRHSMatrixInfo rhs_info_buf; + GEMMLHSMatrixInfo lhs_info_img; + GEMMRHSMatrixInfo rhs_info_img; + std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 2, 4, 8, 1, 2, 0, 0, 0, 1, 1); + std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 2, 2, 4, 1, 8, 0, 1, 0, 1, 0); + + return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img), + std::make_pair(lhs_info_buf, rhs_info_buf), + n, k, b, DataType::F32); + } + } + else + { + GEMMLHSMatrixInfo lhs_info_buf; + GEMMRHSMatrixInfo rhs_info_buf; + GEMMLHSMatrixInfo lhs_info_img; + GEMMRHSMatrixInfo rhs_info_img; + std::tie(lhs_info_img, rhs_info_img) = configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 2, 0, 1, 0, 0, 1); + std::tie(lhs_info_buf, rhs_info_buf) = configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 16, 0, 1, 0, 1, 0); + + return select_lhs_rhs_info(std::make_pair(lhs_info_img, rhs_info_img), + std::make_pair(lhs_info_buf, rhs_info_buf), + n, k, b, DataType::F32); + } + } + } +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + ARM_COMPUTE_UNUSED(k); + ARM_COMPUTE_UNUSED(b); + + if(m == 1) + { + const unsigned int h0 = std::max(n / 2, 1U); + if(n <= 836.0) + { + return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, h0, 0, 1, 0, 1, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, h0, 0, 1, 0, 1, 0); + } + } + else if(m < 128) + { + const int h0 = std::max(std::min(static_cast<int>(n / 4), static_cast<int>(256)), static_cast<int>(1)); + if(k >= 512) + { + return configure_lhs_rhs_info(m, n, 2, 4, 16, 1, h0, 0, 1, 0, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 2, 4, 8, 1, h0, 0, 1, 0, 0); + } + } + else + { + const int h0 = std::max(std::min(static_cast<int>(n / 4), static_cast<int>(256)), static_cast<int>(1)); + if(n >= 64) + { + return configure_lhs_rhs_info(m, n, 4, 8, 4, 1, h0, 0, 1, 0, 0); + } + else + { + if(k >= 512) + { + return configure_lhs_rhs_info(m, n, 2, 4, 16, 1, h0, 0, 1, 0, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 2, 4, 8, 1, h0, 0, 1, 0, 0); + } + } + } +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G77_u8(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + ARM_COMPUTE_UNUSED(k); + ARM_COMPUTE_UNUSED(b); + + if(m == 1) + { + const unsigned int h0 = std::max(n / 2, 1U); + return configure_lhs_rhs_info(m, n, 1, 4, 16, 1, h0, 0, 1, 0, 1); + } + else + { + const int h0 = std::max(std::min(static_cast<int>(n / 4), static_cast<int>(256)), static_cast<int>(1)); + if(m >= 28) + { + return configure_lhs_rhs_info(m, n, 4, 4, 16, 1, h0, 0, 1, 0, 1); + } + else + { + return configure_lhs_rhs_info(m, n, 2, 4, 16, 1, h0, 0, 1, 0, 1); + } + } +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f32(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + const float r_mn = static_cast<float>(m) / static_cast<float>(n); + const float r_mk = static_cast<float>(m) / static_cast<float>(k); + const float r_nk = static_cast<float>(n) / static_cast<float>(k); + const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f; + + if(m == 1) + { + if(workload <= 278.7000f) + { + if(workload <= 7.5000f) + { + return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, 2, 0, 1, 1, 0, 0); + } + else + { + if(r_mn <= 0.0031f) + { + if(workload <= 256.6000f) + { + if(workload <= 16.7500f) + { + if(r_nk <= 1.6671f) + { + return configure_lhs_rhs_info(m, n, 1, 2, 2, 1, 32, 0, 0, 0, 1, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, 2, 0, 1, 1, 0, 0); + } + } + else + { + return configure_lhs_rhs_info(m, n, 1, 2, 2, 1, 32, 0, 0, 0, 1, 0); + } + } + else + { + return configure_lhs_rhs_info(m, n, 1, 2, 2, 1, 32, 0, 0, 0, 1, 0); + } + } + else + { + if(r_mk <= 0.0027f) + { + if(r_mk <= 0.0014f) + { + return configure_lhs_rhs_info(m, n, 1, 2, 2, 1, 32, 0, 0, 0, 1, 0); + } + else + { + if(workload <= 8.9500f) + { + return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, 2, 0, 1, 1, 0, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 1, 2, 2, 1, 32, 0, 0, 0, 1, 0); + } + } + } + else + { + if(workload <= 14.1500f) + { + return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, 2, 0, 1, 1, 0, 0); + } + else + { + if(r_mk <= 0.0041f) + { + return configure_lhs_rhs_info(m, n, 1, 2, 2, 1, 32, 0, 0, 0, 1, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 1, 2, 8, 1, 2, 0, 1, 1, 0, 0); + } + } + } + } + } + } + else + { + if(workload <= 363.7000f) + { + if(r_mk <= 0.0031f) + { + return configure_lhs_rhs_info(m, n, 1, 4, 2, 1, 32, 0, 1, 0, 1, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 1, 4, 4, 1, 32, 0, 1, 0, 1, 0); + } + } + else + { + return configure_lhs_rhs_info(m, n, 1, 4, 2, 1, 32, 0, 1, 0, 1, 0); + } + } + } + else + { + if(workload <= 1384.8000f) + { + if(workload <= 704.0000f) + { + return configure_lhs_rhs_info(m, n, 2, 2, 4, 1, 32, 0, 1, 0, 1, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 2, 4, 8, 1, 4, 0, 0, 0, 1, 1); + } + } + else + { + if(workload <= 16761.6006f) + { + if(r_mn <= 187.1250f) + { + return configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 16, 0, 0, 0, 1, 1); + } + else + { + return configure_lhs_rhs_info(m, n, 2, 4, 8, 1, 4, 0, 0, 0, 1, 1); + } + } + else + { + if(r_mk <= 432.4630f) + { + return configure_lhs_rhs_info(m, n, 5, 4, 4, 1, 16, 0, 0, 0, 1, 1); + } + else + { + return configure_lhs_rhs_info(m, n, 2, 4, 4, 1, 16, 0, 1, 0, 1, 1); + } + } + } + } +} + +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigReshapedRhsOnlyValhall::configure_G78_f16(unsigned int m, unsigned int n, unsigned int k, unsigned int b) +{ + const float r_mn = static_cast<float>(m) / static_cast<float>(n); + const float r_mk = static_cast<float>(m) / static_cast<float>(k); + const float r_nk = static_cast<float>(n) / static_cast<float>(k); + const float workload = (static_cast<float>(m) * static_cast<float>(n) * static_cast<float>(b)) / 20.0f; + + if(m == 1) + { + if(r_mn <= 0.0038f) + { + if(workload <= 353.9000f) + { + if(workload <= 278.7000f) + { + return configure_lhs_rhs_info(m, n, 1, 2, 4, 1, 32, 0, 0, 1, 0, 0); + } + else + { + if(r_mk <= 0.0004f) + { + return configure_lhs_rhs_info(m, n, 1, 2, 4, 1, 32, 0, 0, 1, 0, 0); + } + else + { + if(r_mk <= 0.0030f) + { + return configure_lhs_rhs_info(m, n, 1, 8, 4, 1, 8, 0, 1, 1, 0, 1); + } + else + { + return configure_lhs_rhs_info(m, n, 1, 2, 4, 1, 32, 0, 0, 1, 0, 0); + } + } + } + } + else + { + if(r_nk <= 1.9384f) + { + return configure_lhs_rhs_info(m, n, 1, 2, 4, 1, 32, 0, 0, 1, 0, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 1, 8, 4, 1, 8, 0, 1, 1, 0, 1); + } + } + } + else + { + if(r_nk <= 1.0368f) + { + return configure_lhs_rhs_info(m, n, 1, 2, 16, 1, 32, 0, 0, 1, 0, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 1, 2, 4, 1, 32, 0, 0, 1, 0, 0); + } + } + } + else + { + if(workload <= 1422.4000f) + { + if(workload <= 704.0000f) + { + return configure_lhs_rhs_info(m, n, 2, 2, 8, 1, 32, 0, 0, 1, 0, 0); + } + else + { + if(workload <= 1197.6000f) + { + return configure_lhs_rhs_info(m, n, 2, 4, 8, 1, 8, 0, 1, 1, 0, 1); + } + else + { + if(workload <= 1241.6000f) + { + return configure_lhs_rhs_info(m, n, 2, 8, 8, 1, 16, 0, 1, 1, 0, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 2, 4, 8, 1, 8, 0, 1, 1, 0, 1); + } + } + } + } + else + { + if(workload <= 2769.6000f) + { + if(workload <= 1846.4000f) + { + if(r_mn <= 2.4927f) + { + return configure_lhs_rhs_info(m, n, 2, 8, 8, 1, 16, 0, 1, 1, 0, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 4, 4, 8, 1, 32, 0, 1, 1, 0, 0); + } + } + else + { + if(r_mn <= 0.6261f) + { + return configure_lhs_rhs_info(m, n, 4, 4, 8, 1, 32, 0, 1, 1, 0, 0); + } + else + { + if(r_mk <= 3.4453f) + { + if(r_mn <= 1.4135f) + { + return configure_lhs_rhs_info(m, n, 2, 8, 8, 1, 16, 0, 1, 1, 0, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 4, 4, 8, 1, 32, 0, 1, 1, 0, 0); + } + } + else + { + return configure_lhs_rhs_info(m, n, 2, 8, 8, 1, 16, 0, 1, 1, 0, 0); + } + } + } + } + else + { + if(r_nk <= 0.0302f) + { + return configure_lhs_rhs_info(m, n, 2, 4, 8, 1, 8, 0, 1, 1, 0, 1); + } + else + { + if(r_mk <= 181.3750f) + { + return configure_lhs_rhs_info(m, n, 4, 4, 8, 1, 32, 0, 1, 1, 0, 0); + } + else + { + if(workload <= 28035.2002f) + { + return configure_lhs_rhs_info(m, n, 2, 8, 8, 1, 16, 0, 1, 1, 0, 0); + } + else + { + if(r_mk <= 808.6667f) + { + return configure_lhs_rhs_info(m, n, 4, 4, 8, 1, 32, 0, 1, 1, 0, 0); + } + else + { + return configure_lhs_rhs_info(m, n, 2, 8, 8, 1, 16, 0, 1, 1, 0, 0); + } + } + } + } + } + } + } +} +} // namespace gemm +} // namespace kernels +} // namespace opencl +} // namespace arm_compute diff --git a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMReshapedOnlyRHSKernelConfiguration.h b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmReshapedOnlyRhsKernelConfig.h index 001b98dca8..8fd71276a0 100644 --- a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMReshapedOnlyRHSKernelConfiguration.h +++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmReshapedOnlyRhsKernelConfig.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2020 Arm Limited. + * Copyright (c) 2019-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,21 +21,25 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_CLGEMMRESHAPEDONLYRHSKERNELCONFIGURATION_H -#define ARM_COMPUTE_CLGEMMRESHAPEDONLYRHSKERNELCONFIGURATION_H +#ifndef ARM_COMPUTE_CL_GEMM_RESHAPED_ONLY_RHS_KERNEL_CONFIGURATION_H +#define ARM_COMPUTE_CL_GEMM_RESHAPED_ONLY_RHS_KERNEL_CONFIGURATION_H -#include "src/core/CL/ICLGEMMKernelConfiguration.h" -#include "src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.h" -#include "src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.h" +#include "src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h" +#include "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.h" +#include "src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.h" #include <memory> namespace arm_compute { -namespace cl_gemm +namespace opencl +{ +namespace kernels +{ +namespace gemm { /** CLGEMMReshapedOnlyRHS factory class */ -class CLGEMMReshapedOnlyRHSKernelConfigurationFactory final +class ClGemmReshapedOnlyRhsKernelConfigurationFactory final { public: /** Static method to call the CLGEMMReshapedOnlyRHS kernel configuration class accordingly with the GPU target @@ -44,20 +48,22 @@ public: * * @return CLGEMMReshapedOnlyRHS kernel configuration class */ - static std::unique_ptr<ICLGEMMKernelConfiguration> create(GPUTarget gpu) + static std::unique_ptr<IClGemmKernelConfig> create(GPUTarget gpu) { switch(get_arch_from_target(gpu)) { case GPUTarget::MIDGARD: case GPUTarget::BIFROST: - return std::make_unique<CLGEMMDefaultConfigReshapedRHSOnlyBifrost>(gpu); + return std::make_unique<ClGemmDefaultConfigReshapedRhsOnlyBifrost>(gpu); case GPUTarget::VALHALL: - return std::make_unique<CLGEMMDefaultConfigReshapedRHSOnlyValhall>(gpu); + return std::make_unique<ClGemmDefaultConfigReshapedRhsOnlyValhall>(gpu); default: ARM_COMPUTE_ERROR("Not supported GPU target"); } } }; -} // namespace cl_gemm +} // namespace gemm +} // namespace kernels +} // namespace opencl } // namespace arm_compute -#endif /*ARM_COMPUTE_CLGEMMRESHAPEDONLYRHSKERNELCONFIGURATION_H */ +#endif /* ARM_COMPUTE_CL_GEMM_RESHAPED_ONLY_RHS_KERNEL_CONFIGURATION_H */ diff --git a/src/core/helpers/MemoryHelpers.h b/src/core/helpers/MemoryHelpers.h new file mode 100644 index 0000000000..6756a90c25 --- /dev/null +++ b/src/core/helpers/MemoryHelpers.h @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2021 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 SRC_COMMON_MEMORY_HELPERS_H +#define SRC_COMMON_MEMORY_HELPERS_H + +#include "arm_compute/core/ITensorPack.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/experimental/Types.h" +#include "arm_compute/runtime/MemoryGroup.h" + +#include <memory> +#include <utility> +#include <vector> + +namespace arm_compute +{ +inline int offset_int_vec(int offset) +{ + return ACL_INT_VEC + offset; +} + +template <typename TensorType> +using WorkspaceData = std::vector<std::pair<int, std::unique_ptr<TensorType>>>; + +template <typename TensorType> +WorkspaceData<TensorType> manage_workspace(const experimental::MemoryRequirements &mem_reqs, + MemoryGroup &mgroup, + ITensorPack &run_pack, ITensorPack &prep_pack) +{ + WorkspaceData<TensorType> workspace_memory; + for(const auto &req : mem_reqs) + { + if(req.size == 0) + { + continue; + } + + const auto aux_info = TensorInfo{ TensorShape(req.size), 1, DataType::U8 }; + workspace_memory.emplace_back(req.slot, std::make_unique<TensorType>()); + + auto aux_tensor = workspace_memory.back().second.get(); + ARM_COMPUTE_ERROR_ON_NULLPTR(aux_tensor); + aux_tensor->allocator()->init(aux_info); + + if(req.lifetime == experimental::MemoryLifetime::Temporary) + { + mgroup.manage(aux_tensor); + } + else + { + prep_pack.add_tensor(req.slot, aux_tensor); + } + run_pack.add_tensor(req.slot, aux_tensor); + } + + for(auto &mem : workspace_memory) + { + auto tensor = mem.second.get(); + tensor->allocator()->allocate(); + } + + return workspace_memory; +} +} // namespace arm_compute +#endif /* SRC_COMMON_MEMORY_HELPERS_H */ |