Move CPU/GPU files from Core/Runtime to the respective backend folders
Legacy structure contained two libraries core/runtime with two backends
in each.
We reduce the core/runtime libraries to a single library thus merging
the backend files
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: I69545765fe7a730368105cdbd067d3135ec7a174
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6155
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp b/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp
new file mode 100644
index 0000000..448d353
--- /dev/null
+++ b/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp
@@ -0,0 +1,416 @@
+/*
+ * 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/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
+
+ClGemmMatrixMultiplyNativeKernel::ClGemmMatrixMultiplyNativeKernel()
+{
+ _type = CLKernelType::GEMM;
+}
+
+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