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>
diff --git a/src/core/CL/CLKernels.h b/src/core/CL/CLKernels.h
index 63978ce..1302d52 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 100100b..06a73f1 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 @@
/** 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 222a861..e79f6df 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 @@
/** 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.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
deleted file mode 100644
index 479c063..0000000
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
+++ /dev/null
@@ -1,540 +0,0 @@
-/*
- * 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.
- */
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/CL/OpenCL.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "src/core/AccessWindowStatic.h"
-#include "src/core/CL/CLValidate.h"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.h"
-#include "src/core/utils/helpers/float_ops.h"
-#include "support/StringSupport.h"
-
-#include <set>
-#include <string>
-
-namespace arm_compute
-{
-using namespace arm_compute::misc::shape_calculator;
-
-namespace
-{
-using ElementsProcessed = Steps;
-
-inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, 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_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)
- && (!reshape_info.broadcast_bias()),
- "Bias addition only supported with broadcast mode in case the input or output has to be reinterpreted as 3D");
-
- if(!is_interleaved_transposed)
- {
- ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1));
-
- if(input2 != 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);
-
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1);
- 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");
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix");
- }
- }
- }
- else
- {
- GEMMRHSMatrixInfo rhs_info;
- GEMMLHSMatrixInfo lhs_info;
- const auto m = static_cast<unsigned int>(reshape_info.m());
- const auto n = static_cast<unsigned int>(reshape_info.n());
- 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.k0 = 1;
- rhs_info.h0 = mult_transpose1xW_width;
- rhs_info.interleave = false;
- rhs_info.transpose = false;
- lhs_info.m0 = 4;
- lhs_info.k0 = 4;
- lhs_info.v0 = mult_interleave4x4_height;
- lhs_info.interleave = true;
- lhs_info.transpose = true;
-
- TensorShape tensor_shape0{ input0->tensor_shape() };
- tensor_shape0.set(0, k);
- tensor_shape0.set(1, m);
-
- TensorShape tensor_shape1{ input1->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_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));
-
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);
-
- if(input2 != nullptr && !(helpers::float_ops::is_zero(beta)))
- {
- const unsigned int input2_dim0 = input2->dimension(0);
- const unsigned int input2_dim1 = input2->dimension(1);
-
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1);
- 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");
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix");
- }
- }
- }
-
- if(output->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);
- }
-
- return Status{};
-}
-
-inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output,
- float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target,
- ElementsProcessed &num_elements_processed)
-{
- ARM_COMPUTE_UNUSED(beta);
- bool window_changed = false;
- Window win{};
- Window win_out{};
-
- const DataType data_type = input0->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,
- // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
- if(reinterpret_input_as_3d == reinterpret_output_as_3d)
- {
- reinterpret_input_as_3d = false;
- 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)));
-
- TensorInfo tmp_info(*output);
-
- 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,
- // the window needs to be constructed on the 2D collapsed version of the tensor
- TensorShape tmp_shape(output->tensor_shape());
- tmp_shape.collapse(2U, 1U);
- tmp_info.set_tensor_shape(tmp_shape);
- }
-
- if(is_interleaved_transposed)
- {
- // reinterpret_input_as_3d is not supported if is_interleaved_transposed is set
- ARM_COMPUTE_ERROR_ON(reshape_info.reinterpret_input_as_3d());
-
- // Configure kernel window
- num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type);
- 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)
- {
- 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));
-
- window_changed = update_window_and_padding(win, input2_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.
- 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);
-
- // 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;
- }
-
- // 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)
- {
- 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));
-
- 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
- }
- 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
- }
- }
-
- // 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);
- collapsed = win.collapse(win, dimension_to_collapse);
-
- Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
- return std::make_pair(err, collapsed);
-}
-} // 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,
- 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);
-
- // Perform validate step
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr) ? input2->info() : nullptr, output->info(), 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 });
-
- _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();
-
- // In case both input and output 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_input_as_3d = false;
- _reinterpret_output_as_3d = false;
- }
-
- // 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();
-
- _slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0);
-
- const DataType data_type = input0->info()->data_type();
-
- // Get target architecture
- GPUTarget gpu_target = get_target();
-
- 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,
- 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);
-
- 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 m0 = num_elements_processed.y();
- const unsigned int n0 = num_elements_processed.x();
-
- // 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 % m0;
- const unsigned int partial_store_n0 = n % n0;
-
- // 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(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(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)));
-
- const bool is_bifrost = get_arch_from_target(gpu_target) == GPUTarget::BIFROST;
-
- std::string kernel_name;
- if(is_interleaved_transposed)
- {
- const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
- const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
-
- 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("-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));
- build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
-
- if(is_data_type_float(data_type) && is_bifrost)
- {
- kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type)) + "_bifrost";
- }
- else
- {
- kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type));
- if(fp_mixed_precision && data_type == DataType::F16)
- {
- // currently wider accumulator is only supported for fp16 kernels.
- kernel_name += "_acc32";
- }
- }
- }
- 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("-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));
- build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
- build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
-
- // Create kernels according to the architecture, data type and input size.
- if(is_data_type_float(data_type) && is_bifrost)
- {
- kernel_name = "gemm_mm_floating_point";
-
- if(input0->info()->num_dimensions() != 1)
- {
- kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost";
- if(fp_mixed_precision && data_type == DataType::F16)
- {
- // currently wider accumulator is only supported for fp16 kernels.
- kernel_name += "_acc32";
- }
- }
- else if(input1->info()->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.
- // FC6 and FC7 of AlexNet and VGG-16).
- kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost_1000";
- }
-
- // The work-group size equal to the Bifrost quad size has been proved to be optimal for these kernels
- // via exhaustive autotuning over a range of representative layer configurations.
- set_lws_hint(cl::NDRange(4));
- }
- else // (MIDGARD and F32) or (F16)
- {
- kernel_name = "gemm_mm_floating_point";
- }
- }
- // Create kernel
- _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
-
- // Set config_id for enabling LWS tuning
- _config_id = "gemm_";
- _config_id += (is_interleaved_transposed ? "reshaped_" : "");
- _config_id += (_add_bias ? "add_bias_" : "");
- _config_id += (_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 += "_";
- _config_id += support::cpp11::to_string(output->info()->dimension(1));
- _config_id += "_";
- _config_id += support::cpp11::to_string(output->info()->dimension(0));
- _config_id += "_";
- _config_id += support::cpp11::to_string(output->info()->dimension(2));
- _config_id += "_";
- _config_id += support::cpp11::to_string(output->info()->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)));
-
- 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,
- 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(),
- beta,
- is_interleaved_transposed,
- reshape_info,
- gpu_target,
- num_elements_processed)
- .first);
-
- return Status{};
-}
-
-void CLGEMMMatrixMultiplyKernel::run(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)
- {
- // 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);
- }
-
- Window slice = window.first_slice_window_3D();
- Window slice_matrix_b = slice;
-
- 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 num_arguments_bias = _add_bias ? num_arguments_per_2D_tensor() + 1 : 0;
-
- if(_reinterpret_input_as_3d)
- {
- // 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;
- _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
- 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;
- _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
- }
-
- do
- {
- Window slice_b = slice;
- // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
- // This scenario can happen when the matrix multiplication is used to perform a convolution operation
- if(!_slide_matrix_b)
- {
- slice_b = slice_matrix_b;
- }
-
- unsigned int idx = 0;
- add_2D_tensor_argument(idx, _input0, slice);
- add_2D_tensor_argument(idx, _input1, slice_b);
- if(_add_bias)
- {
- add_2D_tensor_argument(idx, _input2, 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]));
- 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>(_output->info()->strides_in_bytes()[2]));
- enqueue(queue, *this, slice, lws_hint());
- }
- while(window.slide_window_slice_3D(slice));
-}
-} // namespace arm_compute
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h
deleted file mode 100644
index 71d223b..0000000
--- 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.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp
deleted file mode 100644
index 1fe298c..0000000
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp
+++ /dev/null
@@ -1,420 +0,0 @@
-/*
- * 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/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/CL/OpenCL.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "src/core/AccessWindowStatic.h"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.h"
-#include "src/core/utils/helpers/float_ops.h"
-#include "support/StringSupport.h"
-
-#include <cstddef>
-#include <cstdint>
-#include <tuple>
-
-using namespace arm_compute::misc::shape_calculator;
-
-namespace arm_compute
-{
-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)
-{
- 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_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)
- && (!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, "Mixed precision not supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_info.export_to_cl_image, "Export to CLImage not supported for GEMM native");
-
- const unsigned int m = gemm_info.m;
- const unsigned int n = gemm_info.n;
- const unsigned int k = gemm_info.k;
-
- ARM_COMPUTE_UNUSED(m);
- 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);
- if(gemm_info.reinterpret_input_as_3d)
- {
- ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != m);
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != m);
- }
-
- if(input2 != nullptr && !(helpers::float_ops::is_zero(beta)))
- {
- const unsigned int input2_dim0 = input2->dimension(0);
- const unsigned int input2_dim1 = input2->dimension(1);
-
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1);
- 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");
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix");
- }
- }
-
- if(output->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);
- }
-
- 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)
-{
- 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 = gemm_info.reinterpret_input_as_3d;
- bool reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0;
-
- Window win{};
- Window win_out{};
- bool window_changed = false;
-
- // In case both input and output 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)));
-
- TensorInfo tmp_info(*output);
-
- 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,
- // the window needs to be constructed on the 2D collapsed version of the tensor
- TensorShape tmp_shape(output->tensor_shape());
- tmp_shape.collapse(2U, 1U);
- tmp_info.set_tensor_shape(tmp_shape);
- }
-
- // Configure kernel window
- num_elems_processed_per_iteration_x = rhs_info.n0;
- 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)
- {
- 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));
-
- 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
- }
- 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
- }
-
- // 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);
- collapsed = win.collapse(win, dimension_to_collapse);
-
- Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
- return std::make_pair(err, collapsed);
-}
-} // 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,
- 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_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr ? input2->info() : nullptr), output->info(), 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;
- _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;
-
- // In case both input and output 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_input_as_3d = false;
- _reinterpret_output_as_3d = false;
- }
-
- // 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);
-
- 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);
- ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
- 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);
-
- 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);
-
- // 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;
- const unsigned int partial_store_n0 = gemm_info.n % rhs_info.n0;
-
- // Shrink M0 to be always <= M (internal_m) to prevent out-of-bounds reads.
- // NOTE: This might have implications on heuristics and performance
- const unsigned int internal_m0 = std::min(internal_m, lhs_info.m0);
-
- // 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_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(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(_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));
- build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k));
- build_opts.add_option("-DM0=" + support::cpp11::to_string(internal_m0));
- build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));
- build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0));
- build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
- build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
- build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation())));
- build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a()));
- build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b()));
-
- std::string kernel_name("gemm_mm_native");
-
- // Create kernel
- _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
-
- // Set config_id for enabling LWS tuning
- _config_id = kernel_name;
- _config_id += "_";
- _config_id += (_add_bias ? "add_bias_" : "");
- _config_id += (_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 += "_";
- _config_id += support::cpp11::to_string(output->info()->dimension(1));
- _config_id += "_";
- _config_id += support::cpp11::to_string(output->info()->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 += "_";
- _config_id += support::cpp11::to_string(lhs_info.m0);
- _config_id += "_";
- _config_id += support::cpp11::to_string(rhs_info.n0);
- _config_id += "_";
- _config_id += support::cpp11::to_string(rhs_info.k0);
-
- 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,
- 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(),
- lhs_info,
- rhs_info,
- gemm_info,
- num_elements_processed)
- .first);
-
- return Status{};
-}
-
-void CLGEMMMatrixMultiplyNativeKernel::run(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)
- {
- // 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);
- }
-
- Window slice = window.first_slice_window_3D();
- Window slice_matrix_b = slice;
-
- slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
- slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
-
- if(_reinterpret_input_as_3d)
- {
- // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
- unsigned int idx0;
- if(_add_bias)
- {
- idx0 = 4 * num_arguments_per_2D_tensor() + 4;
- }
- else
- {
- idx0 = 3 * num_arguments_per_2D_tensor() + 3;
- }
- const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->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
- unsigned int idx0;
- if(_add_bias)
- {
- idx0 = 4 * num_arguments_per_2D_tensor() + 4 + (_reinterpret_input_as_3d ? 1 : 0);
- }
- else
- {
- 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;
- _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
- }
-
- do
- {
- Window slice_b = slice;
- // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
- // This scenario can happen when the matrix multiplication is used to perform a convolution operation
- if(!_slide_matrix_b)
- {
- slice_b = slice_matrix_b;
- }
-
- unsigned int idx = 0;
- add_2D_tensor_argument(idx, _input0, slice);
- add_2D_tensor_argument(idx, _input1, slice_b);
- if(_add_bias)
- {
- add_2D_tensor_argument(idx, _input2, 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]));
- 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>(_output->info()->strides_in_bytes()[2]));
- enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
- }
- while(window.slide_window_slice_3D(slice));
-}
-} // namespace arm_compute
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h b/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h
deleted file mode 100644
index 6b6004b..0000000
--- 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.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp
deleted file mode 100644
index d270f92..0000000
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp
+++ /dev/null
@@ -1,425 +0,0 @@
-/*
- * 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.
- */
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/CL/OpenCL.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#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/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.h"
-#include "src/core/utils/helpers/float_ops.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
-{
-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)
-{
- 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(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");
- 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_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)
- && (!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));
-
- 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() };
- tensor_shape0.set(0, k);
- tensor_shape0.set(1, m);
-
- TensorShape tensor_shape1{ input1->tensor_shape() };
- tensor_shape1.set(0, n);
- tensor_shape1.set(1, k);
-
- if(input2 != nullptr && !(helpers::float_ops::is_zero(beta)))
- {
- const unsigned int input2_dim0 = input2->dimension(0);
- const unsigned int input2_dim1 = input2->dimension(1);
-
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1);
- 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");
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_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_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));
-
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);
-
- if(output->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);
- }
-
- 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)
-{
- 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_output_as_3d = gemm_info.depth_output_gemm3d != 0;
-
- Window win{};
- 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)));
-
- TensorInfo tmp_info(*output);
-
- 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,
- // the window needs to be constructed on the 2D collapsed version of the tensor
- TensorShape tmp_shape(output->tensor_shape());
- tmp_shape.collapse(2U, 1U);
- tmp_info.set_tensor_shape(tmp_shape);
- }
-
- // Configure kernel window
- num_elems_processed_per_iteration_x = rhs_info.n0;
- 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));
-
- if(input2 != 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));
-
- window_changed = update_window_and_padding(win, input2_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);
- collapsed = win.collapse(win, dimension_to_collapse);
-
- Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
- return std::make_pair(err, collapsed);
-}
-} // 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)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
-
- 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));
-
- auto padding_info = get_padding_info({ input0, output });
- _input0 = input0;
- _input1 = input1;
- _input2 = helpers::float_ops::is_zero(beta) ? nullptr : input2;
- _output = output;
- _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;
- _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);
-
- 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);
- 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();
-
- // 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 partial_store_m0 = internal_m % lhs_info.m0;
- const unsigned int partial_store_n0 = gemm_info.n % rhs_info.n0;
-
- // 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(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(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(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");
- build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS");
- build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation())));
- build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a()));
- 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("-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));
- build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n));
- build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k));
- build_opts.add_option("-DM0=" + support::cpp11::to_string(lhs_info.m0));
- build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));
- build_opts.add_option("-DK0=" + support::cpp11::to_string(lhs_info.k0));
- build_opts.add_option("-DV0=" + support::cpp11::to_string(lhs_info.v0));
- build_opts.add_option("-DH0=" + support::cpp11::to_string(rhs_info.h0));
- build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
- build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
-
- std::string kernel_name("gemm_mm_reshaped_");
- kernel_name += lhs_info.transpose ? "lhs_t_" : "lhs_nt_";
- kernel_name += rhs_info.transpose ? "rhs_t" : "rhs_nt";
- kernel_name += rhs_info.export_to_cl_image ? "_texture" : "";
-
- // Create kernel
- _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
-
- // Set config_id for enabling LWS tuning
- _config_id = kernel_name;
- _config_id += "_";
- _config_id += (_add_bias ? "add_bias_" : "");
- _config_id += (_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 += "_";
- _config_id += (enable_mixed_precision ? "mixed_precision_" : "");
- _config_id += support::cpp11::to_string(output->info()->dimension(1));
- _config_id += "_";
- _config_id += support::cpp11::to_string(output->info()->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 += "_";
- _config_id += support::cpp11::to_string(lhs_info.m0);
- _config_id += "_";
- _config_id += support::cpp11::to_string(rhs_info.n0);
- _config_id += "_";
- _config_id += support::cpp11::to_string(lhs_info.k0);
- _config_id += "_";
- _config_id += support::cpp11::to_string(lhs_info.v0);
- _config_id += "_";
- _config_id += support::cpp11::to_string(rhs_info.h0);
- _config_id += "_";
- _config_id += support::cpp11::to_string(lhs_info.interleave);
- _config_id += "_";
- _config_id += support::cpp11::to_string(rhs_info.interleave);
-
- 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,
- 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(),
- lhs_info,
- rhs_info,
- gemm_info,
- num_elements_processed)
- .first);
-
- return Status{};
-}
-
-void CLGEMMMatrixMultiplyReshapedKernel::run(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)
- {
- // 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);
- }
-
- Window slice = window.first_slice_window_3D();
- Window slice_matrix_b = slice;
-
- 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;
-
- cl::Image2D input1_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];
-
- input1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), _input1->cl_buffer(), shape2d, _input1->info()->data_type(), image_row_pitch);
- }
-
- do
- {
- Window slice_b = slice;
- // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
- // This scenario can happen when the matrix multiplication is used to perform a convolution operation
- if(!_slide_matrix_b)
- {
- slice_b = slice_matrix_b;
- }
-
- unsigned int idx = 0;
-
- // LHS buffer
- add_2D_tensor_argument(idx, _input0, slice);
-
- // RHS buffer or RHS OpenCL image (_export_to_cl_image == true)
- if(_export_to_cl_image)
- {
- _kernel.setArg(idx++, input1_image2d);
- }
- else
- {
- add_2D_tensor_argument(idx, _input1, slice_b);
- }
-
- // Bias buffer (_add_bias == true)
- add_2D_tensor_argument_if(_add_bias, idx, _input2, slice);
-
- // Output buffer
- add_2D_tensor_argument(idx, _output, 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]));
-
- // 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]));
-
- // 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]));
- }
-
- // Output stride_z
- _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2]));
-
- // Cross-plan padding (if _reinterpret_output_as_3d = true)
- if(_reinterpret_output_as_3d)
- {
- _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(total_cross_plane_pad));
- }
-
- // Dispatch kernel
- enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
- }
- while(window.slide_window_slice_3D(slice));
-}
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h b/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h
deleted file mode 100644
index 2ffc322..0000000
--- 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.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.cpp
deleted file mode 100644
index 3dee4f2..0000000
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.cpp
+++ /dev/null
@@ -1,449 +0,0 @@
-/*
- * 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/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
-
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#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/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.h"
-#include "src/core/utils/helpers/float_ops.h"
-#include "support/StringSupport.h"
-
-#include <tuple>
-
-using namespace arm_compute::misc::shape_calculator;
-
-namespace arm_compute
-{
-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)
-{
- 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_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)
- && (!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, "Mixed precision not supported");
- ARM_COMPUTE_RETURN_ON_ERROR(cl_gemm::validate_image2d_support_on_rhs(*input1, 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() };
- tensor_shape1.set(0, n);
- tensor_shape1.set(1, k);
-
- if(input2 != nullptr && !(helpers::float_ops::is_zero(beta)))
- {
- const unsigned int input2_dim0 = input2->dimension(0);
- const unsigned int input2_dim1 = input2->dimension(1);
-
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input0);
- 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");
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix");
- }
- }
-
- const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1);
-
- const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info));
-
- ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != k);
- if(gemm_info.reinterpret_input_as_3d)
- {
- ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != m);
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != m);
- }
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);
-
- if(output->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);
- }
-
- 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)
-{
- 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 = gemm_info.reinterpret_input_as_3d;
- bool reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0;
-
- Window win{};
- Window win_out{};
- bool window_changed = false;
-
- // In case both input and output 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
- 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)));
-
- TensorInfo tmp_info(*output);
-
- 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,
- // the window needs to be constructed on the 2D collapsed version of the tensor
- TensorShape tmp_shape(output->tensor_shape());
- tmp_shape.collapse(2U, 1U);
- tmp_info.set_tensor_shape(tmp_shape);
- }
-
- // Configure kernel window
- num_elems_processed_per_iteration_x = rhs_info.n0;
- 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));
-
- if(input2 != 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));
-
- window_changed = update_window_and_padding(win, input2_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);
- collapsed = win.collapse(win, dimension_to_collapse);
-
- Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
- return std::make_pair(err, collapsed);
-}
-} // 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)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
-
- 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));
-
- _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;
- _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 });
-
- // In case both input and output 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)
- {
- _reinterpret_input_as_3d = false;
- _reinterpret_output_as_3d = false;
- }
-
- // 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);
-
- 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);
- ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
- 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);
-
- // 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);
-
- // Shrink M0 to be always <= M (internal_m) to prevent out-of-bounds reads.
- // NOTE: This might have implications on heuristics and performance
- const unsigned int internal_m0 = std::min(internal_m, lhs_info.m0);
-
- // 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 % internal_m0;
- const unsigned int partial_store_n0 = gemm_info.n % rhs_info.n0;
-
- // 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_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(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(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("-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));
- build_opts.add_option("-DM0=" + support::cpp11::to_string(internal_m0));
- build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));
- build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0));
- build_opts.add_option("-DH0=" + support::cpp11::to_string(rhs_info.h0));
- build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
- build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
- build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation())));
- build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a()));
- build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b()));
- if(_has_pad_y)
- {
- 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));
- }
-
- std::string kernel_name("gemm_mm_reshaped_only_rhs_");
- kernel_name += rhs_info.transpose ? "t" : "nt";
- kernel_name += rhs_info.export_to_cl_image ? "_texture" : "";
-
- // Create kernel
- _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
-
- // Set config_id for enabling LWS tuning
- _config_id = kernel_name;
- _config_id += "_";
- _config_id += (_has_pad_y ? "" : "no_pad_y_");
- _config_id += (_add_bias ? "add_bias_" : "");
- _config_id += (_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 += "_";
- _config_id += support::cpp11::to_string(output->info()->dimension(1));
- _config_id += "_";
- _config_id += support::cpp11::to_string(output->info()->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 += "_";
- _config_id += support::cpp11::to_string(lhs_info.m0);
- _config_id += "_";
- _config_id += support::cpp11::to_string(rhs_info.n0);
- _config_id += "_";
- _config_id += support::cpp11::to_string(rhs_info.k0);
- _config_id += "_";
- _config_id += support::cpp11::to_string(rhs_info.h0);
- _config_id += "_";
- _config_id += support::cpp11::to_string(rhs_info.interleave);
-
- 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,
- 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(),
- lhs_info,
- rhs_info,
- gemm_info,
- num_elements_processed)
- .first);
-
- return Status{};
-}
-
-void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::run(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)
- {
- // 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);
- }
-
- const size_t lhs_idx_batch_size = _reinterpret_input_as_3d && !_has_pad_y ? 3u : 2u;
- const size_t rhs_idx_batch_size = 2u;
- const size_t bia_idx_batch_size = 2u;
- const size_t out_idx_batch_size = _reinterpret_output_as_3d && !_has_pad_y ? 3u : 2u;
-
- Window slice = window.first_slice_window_3D();
- Window slice_matrix_b = slice;
-
- slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
- 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;
-
- // 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;
-
- 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];
-
- input1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), _input1->cl_buffer(), shape2d, _input1->info()->data_type(), image_row_pitch);
- }
-
- do
- {
- Window slice_b = slice;
- // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
- // This scenario can happen when the matrix multiplication is used to perform a convolution operation
- if(!_slide_matrix_b)
- {
- slice_b = slice_matrix_b;
- }
-
- unsigned int idx = 0;
-
- // LHS buffer
- add_2D_tensor_argument(idx, _input0, slice);
-
- // RHS buffer or RHS OpenCL image (_export_to_cl_image == true)
- if(_export_to_cl_image)
- {
- _kernel.setArg(idx++, input1_image2d);
- }
- else
- {
- add_2D_tensor_argument(idx, _input1, slice_b);
- }
-
- // Bias buffer (_add_bias == true)
- add_2D_tensor_argument_if(_add_bias, idx, _input2, slice);
-
- // Output buffer
- add_2D_tensor_argument(idx, _output, slice);
-
- // LHS stride_z
- _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->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]));
-
- // 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]));
- }
-
- // Output stride_z
- _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->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)
- {
- _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(total_cross_plane_pad_lhs));
- }
-
- // 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));
- }
-
- enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
- }
- while(window.slide_window_slice_3D(slice));
-}
-} // namespace arm_compute
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h b/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h
deleted file mode 100644
index 5b96679..0000000
--- 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.cpp b/src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.cpp
deleted file mode 100644
index cc95315..0000000
--- a/src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.cpp
+++ /dev/null
@@ -1,220 +0,0 @@
-/*
- * 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.
- */
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/CL/OpenCL.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "src/core/AccessWindowStatic.h"
-#include "src/core/CL/CLValidate.h"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.h"
-#include "support/StringSupport.h"
-
-namespace arm_compute
-{
-using namespace arm_compute::misc::shape_calculator;
-
-namespace
-{
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
- 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);
- 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");
- 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);
-
- if(output->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);
- }
-
- return Status{};
-}
-
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, 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);
-
- 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,
- // the window needs to be constructed on the 2D collapsed version of the tensor
- TensorShape tmp_shape(input->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)));
-
- // 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));
-
- AccessWindowStatic input_access(input, 0, 0,
- input->dimension(0),
- input->dimension(1));
- AccessWindowStatic output_access(output, 0, 0, output->dimension(0), output->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
-
- // Collapse along the Z direction
- // This collapse needs to be here in order to tune the Z dimension of LWS
- Window collapsed = win.collapse(win, Window::DimZ);
-
- Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
- return std::make_pair(err, collapsed);
-}
-} // namespace
-
-CLGEMMReshapeLHSMatrixKernel::CLGEMMReshapeLHSMatrixKernel()
- : _input(nullptr), _output(nullptr), _reinterpret_input_as_3d(false)
-{
-}
-
-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);
-
- // Perform validate step
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), lhs_info, reinterpret_input_as_3d));
-
- auto padding_info = get_padding_info({ input });
-
- _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 partial_load_m0 = src_h % lhs_info.m0;
- const unsigned int partial_load_k0 = src_w % lhs_info.k0;
-
- // Create build options
- CLBuildOptions build_opts;
- build_opts.add_option("-DM0=" + support::cpp11::to_string(lhs_info.m0));
- build_opts.add_option("-DK0=" + support::cpp11::to_string(lhs_info.k0));
- build_opts.add_option("-DV0=" + support::cpp11::to_string(lhs_info.v0));
- build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src_w));
- 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("-DPARTIAL_LOAD_M0=" + support::cpp11::to_string(partial_load_m0));
- build_opts.add_option("-DPARTIAL_LOAD_K0=" + support::cpp11::to_string(partial_load_k0));
-
- std::string kernel_name("gemm_reshape_lhs_matrix_");
- kernel_name += lhs_info.transpose ? "t" : "nt";
-
- // Create kernel
- _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);
- 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 += "_";
- _config_id += support::cpp11::to_string(output->info()->dimension(0));
- _config_id += "_";
- _config_id += support::cpp11::to_string(output->info()->dimension(1));
- _config_id += "_";
- _config_id += support::cpp11::to_string(output->info()->dimension(2));
- _config_id += "_";
- _config_id += support::cpp11::to_string(lhs_info.m0);
- _config_id += "_";
- _config_id += support::cpp11::to_string(lhs_info.k0);
- _config_id += "_";
- _config_id += support::cpp11::to_string(lhs_info.v0);
- _config_id += "_";
- _config_id += support::cpp11::to_string(lhs_info.interleave);
- _config_id += "_";
- _config_id += support::cpp11::to_string(lhs_info.transpose);
-
- 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)
-{
- 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);
-
- return Status{};
-}
-
-void CLGEMMReshapeLHSMatrixKernel::run(const Window &window, cl::CommandQueue &queue)
-{
- ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
- ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
-
- 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
- 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;
- _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);
- enqueue(queue, *this, slice, lws_hint());
- }
- while(window.slide_window_slice_3D(slice));
-}
-} // namespace arm_compute
diff --git a/src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h b/src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h
deleted file mode 100644
index 92202a2..0000000
--- 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 911484e..0000000
--- 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 546f669..9eaeece 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 18d648d..0a5101f 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 @@
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 @@
// 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/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp
new file mode 100644
index 0000000..817a105
--- /dev/null
+++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp
@@ -0,0 +1,533 @@
+/*
+ * 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.
+ */
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "src/core/AccessWindowStatic.h"
+#include "src/core/CL/CLValidate.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"
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+namespace
+{
+using ElementsProcessed = Steps;
+
+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(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(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 dst has to be reinterpreted as 3D");
+
+ if(!is_interleaved_transposed)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != src1->dimension(1));
+
+ if(src2 != nullptr && !(helpers::float_ops::is_zero(beta)))
+ {
+ 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(src2, src1);
+ if(reshape_info.broadcast_bias())
+ {
+ 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((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix");
+ }
+ }
+ }
+ else
+ {
+ GEMMRHSMatrixInfo rhs_info;
+ GEMMLHSMatrixInfo lhs_info;
+ const auto m = static_cast<unsigned int>(reshape_info.m());
+ const auto n = static_cast<unsigned int>(reshape_info.n());
+ 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 / src1->element_size();
+ rhs_info.k0 = 1;
+ rhs_info.h0 = mult_transpose1xW_width;
+ rhs_info.interleave = false;
+ rhs_info.transpose = false;
+ lhs_info.m0 = 4;
+ lhs_info.k0 = 4;
+ lhs_info.v0 = mult_interleave4x4_height;
+ lhs_info.interleave = true;
+ lhs_info.transpose = true;
+
+ TensorShape tensor_shape0{ src0->tensor_shape() };
+ tensor_shape0.set(0, k);
+ tensor_shape0.set(1, m);
+
+ TensorShape tensor_shape1{ src1->tensor_shape() };
+ tensor_shape1.set(0, n);
+ tensor_shape1.set(1, k);
+
+ 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 = 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(src0, &tensor_info_reshaped0);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1);
+
+ if(src2 != nullptr && !(helpers::float_ops::is_zero(beta)))
+ {
+ const unsigned int src2_dim0 = src2->dimension(0);
+ const unsigned int src2_dim1 = src2->dimension(1);
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1);
+ if(reshape_info.broadcast_bias())
+ {
+ 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((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix");
+ }
+ }
+ }
+
+ if(dst->total_size() != 0)
+ {
+ 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 *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst,
+ float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target,
+ ElementsProcessed &num_elements_processed)
+{
+ ARM_COMPUTE_UNUSED(beta);
+ bool window_changed = false;
+ Window win{};
+ Window win_out{};
+
+ 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 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_input_as_3d = false;
+ reinterpret_output_as_3d = false;
+ }
+
+ // 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(*dst);
+
+ if(reinterpret_output_as_3d)
+ {
+ // 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(dst->tensor_shape());
+ tmp_shape.collapse(2U, 1U);
+ tmp_info.set_tensor_shape(tmp_shape);
+ }
+
+ if(is_interleaved_transposed)
+ {
+ // reinterpret_input_as_3d is not supported if is_interleaved_transposed is set
+ ARM_COMPUTE_ERROR_ON(reshape_info.reinterpret_input_as_3d());
+
+ // Configure kernel window
+ num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type);
+ 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(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 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, 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 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>(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 = (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(*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 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, 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, 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>(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{};
+ return std::make_pair(err, collapsed);
+}
+} // namespace
+
+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(src0, src1, dst);
+
+ // Perform validate step
+ 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({ src0, src1, dst }) : get_padding_info({ src0, dst });
+
+ _reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d();
+ _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0);
+ _add_bias = src2 != nullptr;
+
+ // 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_input_as_3d = false;
+ _reinterpret_output_as_3d = false;
+ }
+
+ // Check if we need to slide the matrix B
+ const unsigned int num_dimensions_src0 = _reinterpret_input_as_3d ? src0->num_dimensions() - 1 : src0->num_dimensions();
+
+ _slide_matrix_b = (src1->num_dimensions() >= num_dimensions_src0);
+
+ const DataType data_type = src0->data_type();
+
+ // Get target architecture
+ GPUTarget gpu_target = get_target();
+
+ ElementsProcessed num_elements_processed{};
+
+ // Configure kernel window
+ 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 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 ? 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();
+
+ // 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 % m0;
+ const unsigned int partial_store_n0 = n % n0;
+
+ // 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(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(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(src1->dimension(0)));
+
+ const bool is_bifrost = get_arch_from_target(gpu_target) == GPUTarget::BIFROST;
+
+ std::string kernel_name;
+ if(is_interleaved_transposed)
+ {
+ const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
+ const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
+
+ 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(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));
+ build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
+
+ if(is_data_type_float(data_type) && is_bifrost)
+ {
+ kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type)) + "_bifrost";
+ }
+ else
+ {
+ kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type));
+ if(fp_mixed_precision && data_type == DataType::F16)
+ {
+ // currently wider accumulator is only supported for fp16 kernels.
+ kernel_name += "_acc32";
+ }
+ }
+ }
+ 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(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));
+ build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
+ build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
+
+ // Create kernels according to the architecture, data type and input size.
+ if(is_data_type_float(data_type) && is_bifrost)
+ {
+ kernel_name = "gemm_mm_floating_point";
+
+ if(src0->num_dimensions() != 1)
+ {
+ kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost";
+ if(fp_mixed_precision && data_type == DataType::F16)
+ {
+ // currently wider accumulator is only supported for fp16 kernels.
+ kernel_name += "_acc32";
+ }
+ }
+ else if(src1->dimension(0) <= 1000 && data_type == DataType::F32)
+ {
+ // 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";
+ }
+
+ // The work-group size equal to the Bifrost quad size has been proved to be optimal for these kernels
+ // via exhaustive autotuning over a range of representative layer configurations.
+ set_lws_hint(cl::NDRange(4));
+ }
+ else // (MIDGARD and F32) or (F16)
+ {
+ kernel_name = "gemm_mm_floating_point";
+ }
+ }
+ // Create kernel
+ _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
+
+ // Set config_id for enabling LWS tuning
+ _config_id = "gemm_";
+ _config_id += (is_interleaved_transposed ? "reshaped_" : "");
+ _config_id += (_add_bias ? "add_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(src0->data_type()));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(dst->dimension(1));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(dst->dimension(0));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(dst->dimension(2));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(dst->dimension(3));
+ _config_id += "_";
+ _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 *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(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,
+ gpu_target,
+ num_elements_processed)
+ .first);
+
+ return Status{};
+}
+
+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);
+
+ 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(src1->info()->strides_in_bytes()[3] != 0);
+ }
+
+ Window slice = window.first_slice_window_3D();
+ Window slice_matrix_b = slice;
+
+ 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 num_arguments_bias = _add_bias ? num_arguments_per_2D_tensor() + 1 : 0;
+
+ if(_reinterpret_input_as_3d)
+ {
+ // 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 = 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 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 = dst->info()->padding().top + dst->info()->padding().bottom;
+ _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
+ }
+
+ do
+ {
+ Window slice_b = slice;
+ // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
+ // This scenario can happen when the matrix multiplication is used to perform a convolution operation
+ if(!_slide_matrix_b)
+ {
+ slice_b = slice_matrix_b;
+ }
+
+ unsigned int idx = 0;
+ add_2D_tensor_argument(idx, src0, slice);
+ add_2D_tensor_argument(idx, src1, slice_b);
+ if(_add_bias)
+ {
+ add_2D_tensor_argument(idx, src2, slice);
+ }
+ 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>(src2->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 0000000..c160133
--- /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/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp
new file mode 100644
index 0000000..97d64c4
--- /dev/null
+++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp
@@ -0,0 +1,411 @@
+/*
+ * 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/ClGemmMatrixMultiplyNativeKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "src/core/AccessWindowStatic.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"
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+namespace
+{
+using ElementsProcessed = Steps;
+
+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(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) && (src2 != nullptr)
+ && (!gemm_info.broadcast_bias),
+ "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");
+
+ const unsigned int m = gemm_info.m;
+ const unsigned int n = gemm_info.n;
+ const unsigned int k = gemm_info.k;
+
+ ARM_COMPUTE_UNUSED(m);
+ ARM_COMPUTE_UNUSED(n);
+ ARM_COMPUTE_UNUSED(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(src0->dimension(1) * src0->dimension(2) != m);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) != m);
+ }
+
+ if(src2 != nullptr && !(helpers::float_ops::is_zero(beta)))
+ {
+ const unsigned int src2_dim0 = src2->dimension(0);
+ const unsigned int src2_dim1 = src2->dimension(1);
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1);
+ if(gemm_info.broadcast_bias)
+ {
+ 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((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix");
+ }
+ }
+
+ if(dst->total_size() != 0)
+ {
+ 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 *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];
+ bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d;
+ bool reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0;
+
+ Window win{};
+ Window win_out{};
+ bool window_changed = false;
+
+ // 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;
+ }
+
+ // 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(*dst);
+
+ if(reinterpret_output_as_3d)
+ {
+ // 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(dst->tensor_shape());
+ tmp_shape.collapse(2U, 1U);
+ tmp_info.set_tensor_shape(tmp_shape);
+ }
+
+ // Configure kernel window
+ num_elems_processed_per_iteration_x = rhs_info.n0;
+ 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(*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 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, 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, 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>(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{};
+ return std::make_pair(err, collapsed);
+}
+} // namespace
+
+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(src0, src1, dst);
+
+ 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({ 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 = src2 != nullptr;
+
+ // 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_input_as_3d = false;
+ _reinterpret_output_as_3d = false;
+ }
+
+ // Check if we need to slide the matrix B
+ 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(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);
+
+ // 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 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 ? 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;
+ const unsigned int partial_store_n0 = gemm_info.n % rhs_info.n0;
+
+ // Shrink M0 to be always <= M (internal_m) to prevent out-of-bounds reads.
+ // NOTE: This might have implications on heuristics and performance
+ const unsigned int internal_m0 = std::min(internal_m, lhs_info.m0);
+
+ // Create build options
+ CLBuildOptions build_opts;
+ 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(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(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));
+ build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k));
+ build_opts.add_option("-DM0=" + support::cpp11::to_string(internal_m0));
+ build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));
+ build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0));
+ build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
+ build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
+ build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation())));
+ build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a()));
+ build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b()));
+
+ std::string kernel_name("gemm_mm_native");
+
+ // Create kernel
+ _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
+
+ // Set config_id for enabling LWS tuning
+ _config_id = kernel_name;
+ _config_id += "_";
+ _config_id += (_add_bias ? "add_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(src0->data_type()));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(dst->dimension(1));
+ _config_id += "_";
+ _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(dst->dimension(2));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(lhs_info.m0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(rhs_info.n0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(rhs_info.k0);
+
+ ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
+}
+
+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(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,
+ num_elements_processed)
+ .first);
+
+ return Status{};
+}
+
+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);
+
+ 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(src1->info()->strides_in_bytes()[3] != 0);
+ }
+
+ Window slice = window.first_slice_window_3D();
+ Window slice_matrix_b = slice;
+
+ slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
+ slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+ if(_reinterpret_input_as_3d)
+ {
+ // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
+ unsigned int idx0;
+ if(_add_bias)
+ {
+ idx0 = 4 * num_arguments_per_2D_tensor() + 4;
+ }
+ else
+ {
+ idx0 = 3 * num_arguments_per_2D_tensor() + 3;
+ }
+ 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 dst has to be reinterpreted as 3D tensor
+ unsigned int idx0;
+ if(_add_bias)
+ {
+ idx0 = 4 * num_arguments_per_2D_tensor() + 4 + (_reinterpret_input_as_3d ? 1 : 0);
+ }
+ else
+ {
+ idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0);
+ }
+ 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));
+ }
+
+ do
+ {
+ Window slice_b = slice;
+ // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
+ // This scenario can happen when the matrix multiplication is used to perform a convolution operation
+ if(!_slide_matrix_b)
+ {
+ slice_b = slice_matrix_b;
+ }
+
+ unsigned int idx = 0;
+ add_2D_tensor_argument(idx, src0, slice);
+ add_2D_tensor_argument(idx, src1, slice_b);
+ if(_add_bias)
+ {
+ add_2D_tensor_argument(idx, src2, slice);
+ }
+ 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>(src2->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 0000000..4770b18
--- /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/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.cpp b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.cpp
new file mode 100644
index 0000000..27409b6
--- /dev/null
+++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.cpp
@@ -0,0 +1,416 @@
+/*
+ * 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.
+ */
+#include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "src/core/AccessWindowStatic.h"
+#include "src/core/CL/CLUtils.h"
+#include "src/core/CL/CLValidate.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>
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+namespace
+{
+using ElementsProcessed = Steps;
+
+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(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");
+ 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_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) && (src2 != nullptr)
+ && (!gemm_info.broadcast_bias),
+ "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{ src0->tensor_shape() };
+ tensor_shape0.set(0, k);
+ tensor_shape0.set(1, m);
+
+ TensorShape tensor_shape1{ src1->tensor_shape() };
+ tensor_shape1.set(0, n);
+ tensor_shape1.set(1, k);
+
+ if(src2 != nullptr && !(helpers::float_ops::is_zero(beta)))
+ {
+ const unsigned int src2_dim0 = src2->dimension(0);
+ const unsigned int src2_dim1 = src2->dimension(1);
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1);
+ if(gemm_info.broadcast_bias)
+ {
+ 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((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix");
+ }
+ }
+
+ 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 = 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(src0, &tensor_info_reshaped0);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1);
+
+ if(dst->total_size() != 0)
+ {
+ 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 *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];
+ bool reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0;
+
+ Window win{};
+ Window win_out{};
+ bool window_changed = false;
+
+ // 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(*dst);
+
+ if(reinterpret_output_as_3d)
+ {
+ // 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(dst->tensor_shape());
+ tmp_shape.collapse(2U, 1U);
+ tmp_info.set_tensor_shape(tmp_shape);
+ }
+
+ // Configure kernel window
+ num_elems_processed_per_iteration_x = rhs_info.n0;
+ 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(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+ 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 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, 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>(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{};
+ return std::make_pair(err, collapsed);
+}
+} // namespace
+
+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(src0, src1, dst);
+
+ 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({ 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 = 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_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(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 = 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 : 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;
+
+ // 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(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(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(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");
+ build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS");
+ build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation())));
+ build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a()));
+ 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(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));
+ build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n));
+ build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k));
+ build_opts.add_option("-DM0=" + support::cpp11::to_string(lhs_info.m0));
+ build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));
+ build_opts.add_option("-DK0=" + support::cpp11::to_string(lhs_info.k0));
+ build_opts.add_option("-DV0=" + support::cpp11::to_string(lhs_info.v0));
+ build_opts.add_option("-DH0=" + support::cpp11::to_string(rhs_info.h0));
+ build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
+ build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
+
+ std::string kernel_name("gemm_mm_reshaped_");
+ kernel_name += lhs_info.transpose ? "lhs_t_" : "lhs_nt_";
+ kernel_name += rhs_info.transpose ? "rhs_t" : "rhs_nt";
+ kernel_name += rhs_info.export_to_cl_image ? "_texture" : "";
+
+ // Create kernel
+ _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
+
+ // Set config_id for enabling LWS tuning
+ _config_id = kernel_name;
+ _config_id += "_";
+ _config_id += (_add_bias ? "add_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(src0->data_type()));
+ _config_id += "_";
+ _config_id += (enable_mixed_precision ? "mixed_precision_" : "");
+ _config_id += support::cpp11::to_string(dst->dimension(1));
+ _config_id += "_";
+ _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(dst->dimension(2));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(lhs_info.m0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(rhs_info.n0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(lhs_info.k0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(lhs_info.v0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(rhs_info.h0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(lhs_info.interleave);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(rhs_info.interleave);
+
+ ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
+}
+
+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(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,
+ num_elements_processed)
+ .first);
+
+ return Status{};
+}
+
+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);
+
+ 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(src1->info()->strides_in_bytes()[3] != 0);
+ }
+
+ Window slice = window.first_slice_window_3D();
+ Window slice_matrix_b = slice;
+
+ 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 = dst->info()->padding().top + dst->info()->padding().bottom;
+
+ cl::Image2D src1_image2d;
+
+ if(_export_to_cl_image)
+ {
+ 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];
+
+ src1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), src1->cl_buffer(), shape2d, src1->info()->data_type(), image_row_pitch);
+ }
+
+ do
+ {
+ Window slice_b = slice;
+ // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
+ // This scenario can happen when the matrix multiplication is used to perform a convolution operation
+ if(!_slide_matrix_b)
+ {
+ slice_b = slice_matrix_b;
+ }
+
+ unsigned int idx = 0;
+
+ // LHS buffer
+ 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++, src1_image2d);
+ }
+ else
+ {
+ add_2D_tensor_argument(idx, src1, slice_b);
+ }
+
+ // Bias buffer (_add_bias == true)
+ add_2D_tensor_argument_if(_add_bias, idx, src2, 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>(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>(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>(src2->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)
+ {
+ _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(total_cross_plane_pad));
+ }
+
+ // Dispatch kernel
+ enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
+ }
+ 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 0000000..ab648f1
--- /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/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.cpp b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.cpp
new file mode 100644
index 0000000..4eea2c6
--- /dev/null
+++ b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.cpp
@@ -0,0 +1,438 @@
+/*
+ * 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/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h"
+
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "src/core/AccessWindowStatic.h"
+#include "src/core/CL/CLUtils.h"
+#include "src/core/CL/CLValidate.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"
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+namespace
+{
+using ElementsProcessed = Steps;
+
+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(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) && (src2 != nullptr)
+ && (!gemm_info.broadcast_bias),
+ "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(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{ src1->tensor_shape() };
+ tensor_shape1.set(0, n);
+ tensor_shape1.set(1, k);
+
+ if(src2 != nullptr && !(helpers::float_ops::is_zero(beta)))
+ {
+ const unsigned int src2_dim0 = src2->dimension(0);
+ const unsigned int src2_dim1 = src2->dimension(1);
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src0);
+ if(gemm_info.broadcast_bias)
+ {
+ 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((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix");
+ }
+ }
+
+ const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1);
+
+ 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(src0->dimension(0) != k);
+ if(gemm_info.reinterpret_input_as_3d)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) * src0->dimension(2) != m);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) != m);
+ }
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1);
+
+ if(dst->total_size() != 0)
+ {
+ 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 *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];
+ bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d;
+ bool reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0;
+
+ Window win{};
+ Window win_out{};
+ bool window_changed = false;
+
+ // 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/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;
+ }
+
+ // 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(*dst);
+
+ if(reinterpret_output_as_3d)
+ {
+ // 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(dst->tensor_shape());
+ tmp_shape.collapse(2U, 1U);
+ tmp_info.set_tensor_shape(tmp_shape);
+ }
+
+ // Configure kernel window
+ num_elems_processed_per_iteration_x = rhs_info.n0;
+ 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(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+ if(src2 != nullptr)
+ {
+ const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x;
+
+ 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, 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>(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{};
+ return std::make_pair(err, collapsed);
+}
+} // namespace
+
+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(src0, src1, dst);
+
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info));
+
+ _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 = 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({ src0, src1, dst });
+
+ // 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)
+ {
+ _reinterpret_input_as_3d = false;
+ _reinterpret_output_as_3d = false;
+ }
+
+ // Check if we need to slide the matrix B
+ 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(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,
+ // 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 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 ? 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
+ const unsigned int internal_m0 = std::min(internal_m, lhs_info.m0);
+
+ // 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 % internal_m0;
+ const unsigned int partial_store_n0 = gemm_info.n % rhs_info.n0;
+
+ // Create build options
+ CLBuildOptions build_opts;
+ 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(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(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(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));
+ build_opts.add_option("-DM0=" + support::cpp11::to_string(internal_m0));
+ build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));
+ build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0));
+ build_opts.add_option("-DH0=" + support::cpp11::to_string(rhs_info.h0));
+ build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
+ build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
+ build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation())));
+ build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a()));
+ build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b()));
+ if(_has_pad_y)
+ {
+ 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));
+ }
+
+ std::string kernel_name("gemm_mm_reshaped_only_rhs_");
+ kernel_name += rhs_info.transpose ? "t" : "nt";
+ kernel_name += rhs_info.export_to_cl_image ? "_texture" : "";
+
+ // Create kernel
+ _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
+
+ // Set config_id for enabling LWS tuning
+ _config_id = kernel_name;
+ _config_id += "_";
+ _config_id += (_has_pad_y ? "" : "no_pad_y_");
+ _config_id += (_add_bias ? "add_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(src0->data_type()));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(dst->dimension(1));
+ _config_id += "_";
+ _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(dst->dimension(2));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(lhs_info.m0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(rhs_info.n0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(rhs_info.k0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(rhs_info.h0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(rhs_info.interleave);
+
+ ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
+}
+
+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(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,
+ num_elements_processed)
+ .first);
+
+ return Status{};
+}
+
+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);
+
+ 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(src1->info()->strides_in_bytes()[3] != 0);
+ }
+
+ const size_t lhs_idx_batch_size = _reinterpret_input_as_3d && !_has_pad_y ? 3u : 2u;
+ const size_t rhs_idx_batch_size = 2u;
+ const size_t bia_idx_batch_size = 2u;
+ const size_t out_idx_batch_size = _reinterpret_output_as_3d && !_has_pad_y ? 3u : 2u;
+
+ Window slice = window.first_slice_window_3D();
+ Window slice_matrix_b = slice;
+
+ slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
+ slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+ // Get cross plane pads
+ 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 src1_image2d;
+
+ if(_export_to_cl_image)
+ {
+ 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];
+
+ src1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), src1->cl_buffer(), shape2d, src1->info()->data_type(), image_row_pitch);
+ }
+
+ do
+ {
+ Window slice_b = slice;
+ // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
+ // This scenario can happen when the matrix multiplication is used to perform a convolution operation
+ if(!_slide_matrix_b)
+ {
+ slice_b = slice_matrix_b;
+ }
+
+ unsigned int idx = 0;
+
+ // LHS buffer
+ 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++, src1_image2d);
+ }
+ else
+ {
+ add_2D_tensor_argument(idx, src1, slice_b);
+ }
+
+ // Bias buffer (_add_bias == true)
+ add_2D_tensor_argument_if(_add_bias, idx, src2, slice);
+
+ // dst buffer
+ add_2D_tensor_argument(idx, dst, slice);
+
+ // LHS stride_z
+ _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>(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>(src2->info()->strides_in_bytes()[bia_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)
+ {
+ _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(total_cross_plane_pad_lhs));
+ }
+
+ // 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));
+ }
+
+ 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/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h b/src/core/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h
new file mode 100644
index 0000000..ff6c391
--- /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/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.cpp b/src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.cpp
new file mode 100644
index 0000000..98161ed
--- /dev/null
+++ b/src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.cpp
@@ -0,0 +1,219 @@
+/*
+ * 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.
+ */
+#include "src/core/gpu/cl/kernels/ClGemmReshapeLhsMatrixKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "src/core/AccessWindowStatic.h"
+#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
+{
+namespace opencl
+{
+namespace kernels
+{
+namespace
+{
+Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d)
+{
+ 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);
+ 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");
+ 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(src);
+ ARM_COMPUTE_RETURN_ERROR_ON(src->data_type() == DataType::UNKNOWN);
+
+ if(dst->total_size() != 0)
+ {
+ 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 *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(*src);
+
+ if(reinterpret_input_as_3d)
+ {
+ // 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(src->tensor_shape());
+ tmp_shape.collapse(2U, 1U);
+ tmp_info.set_tensor_shape(tmp_shape);
+ }
+
+ // 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(*src, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+ 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, 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
+ Window collapsed = win.collapse(win, Window::DimZ);
+
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, collapsed);
+}
+} // namespace
+
+void ClGemmReshapeLhsMatrixKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+
+ // Perform validate step
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, lhs_info, reinterpret_input_as_3d));
+
+ auto padding_info = get_padding_info({ src });
+
+ _reinterpret_input_as_3d = reinterpret_input_as_3d;
+
+ 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;
+
+ // Create build options
+ CLBuildOptions build_opts;
+ build_opts.add_option("-DM0=" + support::cpp11::to_string(lhs_info.m0));
+ build_opts.add_option("-DK0=" + support::cpp11::to_string(lhs_info.k0));
+ build_opts.add_option("-DV0=" + support::cpp11::to_string(lhs_info.v0));
+ build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src_w));
+ 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(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));
+
+ std::string kernel_name("gemm_reshape_lhs_matrix_");
+ kernel_name += lhs_info.transpose ? "t" : "nt";
+
+ // Create kernel
+ _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
+
+ // Configure kernel window
+ 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(src->data_type()));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(dst->dimension(0));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(dst->dimension(1));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(dst->dimension(2));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(lhs_info.m0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(lhs_info.k0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(lhs_info.v0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(lhs_info.interleave);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(lhs_info.transpose);
+
+ ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
+}
+
+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(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_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 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 = 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, 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 0000000..b830ba0
--- /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
similarity index 61%
rename from src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.cpp
rename to src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.cpp
index 1c4092c..e1ef7c6 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 @@
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 @@
}
} // namespace
-CLGEMMReshapeRHSMatrixKernel::CLGEMMReshapeRHSMatrixKernel()
- : _input(nullptr), _output(nullptr)
+void ClGemmReshapeRhsMatrixKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const GEMMRHSMatrixInfo &rhs_info)
{
-}
-
-void CLGEMMReshapeRHSMatrixKernel::configure(const ICLTensor *input, ICLTensor *output, 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 @@
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 @@
_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 0000000..e877d87
--- /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
similarity index 92%
rename from src/core/CL/gemm/CLGEMMHelpers.cpp
rename to src/core/gpu/cl/kernels/gemm/ClGemmHelpers.cpp
index 61aa962..0a8ba97 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
{
-using namespace arm_compute::misc::shape_calculator;
-
+namespace kernels
+{
+namespace gemm
+{
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 @@
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 @@
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 @@
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
similarity index 94%
rename from src/core/CL/gemm/CLGEMMHelpers.h
rename to src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h
index 5762467..3fce8c9 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 @@
* @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
similarity index 79%
rename from src/core/CL/ICLGEMMKernelConfiguration.h
rename to src/core/gpu/cl/kernels/gemm/IClGemmKernelConfig.h
index 886905e..a49836c 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 @@
};
/** 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 @@
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
similarity index 83%
rename from src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.cpp
rename to src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.cpp
index 52023dd..9d11006 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 @@
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::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::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::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::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 @@
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
similarity index 81%
rename from src/core/CL/gemm/native/CLGEMMDefaultConfigNativeBifrost.h
rename to src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.h
index 78d47a8..385b96e 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 @@
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
similarity index 80%
rename from src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.cpp
rename to src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.cpp
index cf9bb18..e3c129e 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 @@
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
similarity index 75%
rename from src/core/CL/gemm/native/CLGEMMDefaultConfigNativeMidgard.h
rename to src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeMidgard.h
index 40c91d4..0ff5471 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 @@
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
similarity index 85%
rename from src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.cpp
rename to src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.cpp
index 3b55be7..92767ac 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::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::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 @@
}
}
}
-} // 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
similarity index 77%
rename from src/core/CL/gemm/native/CLGEMMDefaultConfigNativeValhall.h
rename to src/core/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeValhall.h
index 08d2d57..17e4c9d 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 @@
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
similarity index 64%
rename from src/core/CL/gemm/native/CLGEMMNativeKernelConfiguration.h
rename to src/core/gpu/cl/kernels/gemm/native/ClGemmNativeKernelConfig.h
index 39a534e..ff6a012 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 @@
*
* @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
similarity index 89%
rename from src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.cpp
rename to src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.cpp
index 5877ab9..b030913 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 @@
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> 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> 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> 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> 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> 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> 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> 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 @@
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
similarity index 82%
rename from src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedBifrost.h
rename to src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedBifrost.h
index 814b831..52e6ce3 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 @@
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
similarity index 93%
rename from src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.cpp
rename to src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.cpp
index b07092a..57e42c9 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 @@
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> 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> 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> 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> 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 @@
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
similarity index 81%
rename from src/core/CL/gemm/reshaped/CLGEMMDefaultConfigReshapedValhall.h
rename to src/core/gpu/cl/kernels/gemm/reshaped/ClGemmDefaultConfigReshapedValhall.h
index 52b83b0..588cd64 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 @@
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
similarity index 67%
rename from src/core/CL/gemm/reshaped/CLGEMMReshapedKernelConfiguration.h
rename to src/core/gpu/cl/kernels/gemm/reshaped/ClGemmReshapedKernelConfig.h
index de60698..c990c89 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 @@
*
* @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
similarity index 85%
rename from src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.cpp
rename to src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.cpp
index 3645a0e..7ed6b39 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 @@
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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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 @@
}
}
-} // 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
similarity index 83%
rename from src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.h
rename to src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyBifrost.h
index db89d83..7b1a1fb 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 @@
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
similarity index 91%
rename from src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.cpp
rename to src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.cpp
index a3f0509..4c6e633 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 @@
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> 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> 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> 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> 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 @@
}
}
}
-} // 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
similarity index 79%
rename from src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.h
rename to src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultConfigReshapedRhsOnlyValhall.h
index a3b556c..6a11ddb 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 @@
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/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.cpp b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyBifrost.cpp
similarity index 85%
copy from src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.cpp
copy to src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyBifrost.cpp
index 3645a0e..7ed6b39 100644
--- a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyBifrost.cpp
+++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyBifrost.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 @@
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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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 @@
}
}
-} // namespace cl_gemm
+} // namespace gemm
+} // namespace kernels
+} // namespace opencl
} // namespace arm_compute
diff --git a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.cpp b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyValhall.cpp
similarity index 91%
copy from src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.cpp
copy to src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyValhall.cpp
index a3f0509..4c6e633 100644
--- a/src/core/CL/gemm/reshaped_only_rhs/CLGEMMDefaultConfigReshapedRHSOnlyValhall.cpp
+++ b/src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmDefaultReshapedRhsOnlyValhall.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 @@
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> 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> 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> 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> 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 @@
}
}
}
-} // namespace cl_gemm
+} // 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
similarity index 64%
rename from src/core/CL/gemm/reshaped_only_rhs/CLGEMMReshapedOnlyRHSKernelConfiguration.h
rename to src/core/gpu/cl/kernels/gemm/reshaped_only_rhs/ClGemmReshapedOnlyRhsKernelConfig.h
index 001b98d..8fd7127 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 @@
*
* @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 0000000..6756a90
--- /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 */