| /* |
| * Copyright (c) 2016-2021 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/ClMulKernel.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/TensorInfo.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 *src1, const ITensorInfo *src2, const ITensorInfo *dst, float scale, |
| ConvertPolicy overflow_policy, RoundingPolicy rounding_policy, const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_UNUSED(overflow_policy); |
| ARM_COMPUTE_UNUSED(rounding_policy); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src1, src2, dst); |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src1); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src1, |
| 1, |
| DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, |
| DataType::S16, DataType::QSYMM16, DataType::F16, DataType::S32, |
| DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src2, |
| 1, |
| DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, |
| DataType::S16, DataType::QSYMM16, DataType::F16, DataType::S32, |
| DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(scale < 0, "Scale cannot be negative."); |
| ARM_COMPUTE_RETURN_ERROR_ON(act_info.enabled() && !is_data_type_float(dst->data_type())); |
| |
| // Check whether it is in_place calculation |
| const bool in_place = (src1 == dst) || (src2 == dst); |
| const bool src1_in_place = in_place && (src1 == dst); |
| |
| const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape()); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); |
| |
| // Validate in case of configured dst |
| if(dst->total_size() > 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, |
| 1, |
| DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, |
| DataType::S16, DataType::QSYMM16, DataType::F16, |
| DataType::S32, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->data_type() == DataType::U8 && (src1->data_type() != DataType::U8 || src2->data_type() != DataType::U8), |
| "Dst can only be U8 if both src are U8"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->data_type() == DataType::QASYMM8 && (src1->data_type() != DataType::QASYMM8 || src2->data_type() != DataType::QASYMM8), |
| "Dst can only be QASYMM8 if both src are QASYMM8"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->data_type() == DataType::QASYMM8_SIGNED && (src1->data_type() != DataType::QASYMM8_SIGNED || src2->data_type() != DataType::QASYMM8_SIGNED), |
| "Dst can only be QASYMM8_SIGNED if both src are QASYMM8_SIGNED"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->data_type() == DataType::QSYMM16 && (src1->data_type() != DataType::QSYMM16 || src2->data_type() != DataType::QSYMM16), |
| "Dst can only be QSYMM16 if both src are QSYMM16"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((src1->data_type() == DataType::S32 || src2->data_type() == DataType::S32) && (dst->data_type() != DataType::S32), |
| "Dst must be S32 if source tensors are S32"); |
| if(in_place) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, src1_in_place ? src1->tensor_shape() : src2->tensor_shape(), 0), |
| "Wrong shape for dst, cannot do in_place calculation"); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0), |
| "Wrong shape for dst"); |
| } |
| } |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| ClMulKernel::ClMulKernel() |
| { |
| _type = CLKernelType::ELEMENTWISE; |
| } |
| |
| void ClMulKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float scale, |
| ConvertPolicy overflow_policy, RoundingPolicy rounding_policy, const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src1, src2, dst, |
| scale, overflow_policy, rounding_policy, act_info)); |
| |
| auto padding_info = get_padding_info({ src1, src2, dst }); |
| |
| const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape()); |
| auto_init_if_empty(*dst, src1->clone()->set_tensor_shape(out_shape)); |
| |
| int scale_int = -1; |
| // Extract sign, exponent and mantissa |
| int exponent = 0; |
| float normalized_mantissa = std::frexp(scale, &exponent); |
| // Use int scaling if factor is equal to 1/2^n for 0 <= n <= 15 |
| // frexp returns 0.5 as mantissa which means that the exponent will be in the range of -1 <= e <= 14 |
| // Moreover, it will be negative as we deal with 1/2^n |
| if((normalized_mantissa == 0.5f) && (-14 <= exponent) && (exponent <= 1)) |
| { |
| // Store the positive exponent. We know that we compute 1/2^n |
| // Additionally we need to subtract 1 to compensate that frexp used a mantissa of 0.5 |
| scale_int = std::abs(exponent - 1); |
| } |
| |
| std::string acc_type; |
| // Check if it has float src and dst |
| if(is_data_type_float(src1->data_type()) || is_data_type_float(src2->data_type())) |
| { |
| scale_int = -1; |
| acc_type = (src1->data_type() == DataType::F32 || src2->data_type() == DataType::F32) ? "float" : "half"; |
| } |
| else |
| { |
| if(src1->element_size() == 4 || src2->element_size() == 4) |
| { |
| // use 64 bit accumulator for 32-bit input |
| acc_type = "long"; |
| } |
| else if(src1->element_size() == 2 || src2->element_size() == 2) |
| { |
| // Use 32-bit accumulator for 16-bit input |
| acc_type = "int"; |
| } |
| else |
| { |
| // Use 16-bit accumulator for 8-bit input |
| acc_type = "ushort"; |
| } |
| } |
| |
| const bool is_quantized = is_data_type_quantized(src1->data_type()); |
| const unsigned int vec_size = adjust_vec_size(16 / dst->element_size(), dst->dimension(0)); |
| const unsigned int vec_size_leftover = dst->dimension(0) % vec_size; |
| |
| // Set kernel build options |
| std::string kernel_name = "pixelwise_mul"; |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(src1->data_type())); |
| build_opts.add_option("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(src2->data_type())); |
| build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(dst->data_type())); |
| build_opts.add_option("-DVEC_SIZE_IN1=" + ((dst->dimension(0) != 1 && src1->dimension(0) == 1) ? "1" : support::cpp11::to_string(vec_size))); |
| build_opts.add_option("-DVEC_SIZE_IN2=" + ((dst->dimension(0) != 1 && src2->dimension(0) == 1) ? "1" : support::cpp11::to_string(vec_size))); |
| build_opts.add_option("-DVEC_SIZE_OUT=" + support::cpp11::to_string(vec_size)); |
| build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(vec_size_leftover)); |
| if(is_quantized && (dst->data_type() != DataType::S32)) |
| { |
| const UniformQuantizationInfo iq1_info = src1->quantization_info().uniform(); |
| const UniformQuantizationInfo iq2_info = src2->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = dst->quantization_info().uniform(); |
| |
| build_opts.add_option_if(is_data_type_quantized_asymmetric(src1->data_type()), |
| "-DOFFSET_IN1=" + support::cpp11::to_string(iq1_info.offset)); |
| build_opts.add_option_if(is_data_type_quantized_asymmetric(src2->data_type()), |
| "-DOFFSET_IN2=" + support::cpp11::to_string(iq2_info.offset)); |
| build_opts.add_option_if(is_data_type_quantized_asymmetric(dst->data_type()), |
| "-DOFFSET_OUT=" + support::cpp11::to_string(oq_info.offset)); |
| build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(iq1_info.scale)); |
| build_opts.add_option("-DSCALE_IN2=" + float_to_string_with_full_precision(iq2_info.scale)); |
| build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oq_info.scale)); |
| kernel_name += "_quantized"; |
| } |
| else |
| { |
| kernel_name += (scale_int >= 0) ? "_int" : "_float"; |
| build_opts.add_option_if_else(overflow_policy == ConvertPolicy::WRAP || is_data_type_float(dst->data_type()), "-DWRAP", "-DSATURATE"); |
| build_opts.add_option_if_else(rounding_policy == RoundingPolicy::TO_ZERO, "-DROUND=_rtz", "-DROUND=_rte"); |
| build_opts.add_option("-DACC_DATA_TYPE=" + acc_type); |
| if(act_info.enabled()) |
| { |
| build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation()))); |
| build_opts.add_option("-DA_VAL=" + float_to_string_with_full_precision(act_info.a())); |
| build_opts.add_option("-DB_VAL=" + float_to_string_with_full_precision(act_info.b())); |
| } |
| } |
| |
| // Check whether it is in_place calculation |
| const bool in_place = (src1 == dst) || (src2 == dst); |
| const bool src1_in_place = in_place && (src1 == dst); |
| build_opts.add_option_if(in_place, "-DIN_PLACE"); |
| build_opts.add_option_if(src1_in_place, "-DSRC1_IN_PLACE"); |
| |
| // Create kernel |
| _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); |
| |
| // Set scale argument |
| unsigned int idx = (in_place ? 2 : 3) * num_arguments_per_3D_tensor(); // Skip the src and dst parameters |
| |
| if(scale_int >= 0 && !is_quantized) |
| { |
| _kernel.setArg(idx++, scale_int); |
| } |
| else |
| { |
| _kernel.setArg(idx++, scale); |
| } |
| |
| Window win = calculate_max_window(*dst, Steps(vec_size)); |
| ICLKernel::configure_internal(win); |
| |
| ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); |
| |
| // Set config_id for enabling LWS tuning |
| _config_id = kernel_name; |
| _config_id += "_"; |
| _config_id += lower_string(string_from_data_type(dst->data_type())); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(src1->dimension(0)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(src1->dimension(1)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(src1->dimension(2)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(src2->dimension(0)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(src2->dimension(1)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(src2->dimension(2)); |
| _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)); |
| } |
| |
| Status ClMulKernel::validate(const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float scale, |
| ConvertPolicy overflow_policy, RoundingPolicy rounding_policy, const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src1, src2, dst, scale, overflow_policy, rounding_policy, act_info)); |
| |
| return Status{}; |
| } |
| |
| void ClMulKernel::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_0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0)); |
| const auto src_1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); |
| auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); |
| |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src_0, src_1, dst); |
| |
| const TensorShape &in_shape1 = src_0->info()->tensor_shape(); |
| const TensorShape &in_shape2 = src_1->info()->tensor_shape(); |
| const TensorShape &out_shape = dst->info()->tensor_shape(); |
| |
| bool can_collapse = true; |
| if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1) |
| { |
| can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); |
| for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); ++d) |
| { |
| can_collapse = (in_shape1[d] == in_shape2[d]); |
| } |
| } |
| |
| bool has_collapsed = false; |
| Window collapsed = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window; |
| |
| const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; |
| const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; |
| |
| Window slice = collapsed.first_slice_window_3D(); |
| Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); |
| Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); |
| |
| // Check whether it is in_place calculation |
| const bool in_place = (src_0 == dst) || (src_1 == dst); |
| do |
| { |
| unsigned int idx = 0; |
| add_3D_tensor_argument(idx, src_0, slice_input1); |
| add_3D_tensor_argument(idx, src_1, slice_input2); |
| if(!in_place) |
| { |
| add_3D_tensor_argument(idx, dst, slice); |
| } |
| enqueue(queue, *this, slice, lws_hint()); |
| |
| ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1)); |
| ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2)); |
| } |
| while(collapsed.slide_window_slice_3D(slice)); |
| } |
| |
| namespace |
| { |
| constexpr unsigned int vec_size_complex = 1; |
| |
| Status validate_arguments_complex(const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src1, 2, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src2, 2, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src1, src2); |
| |
| const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape()); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); |
| ARM_COMPUTE_RETURN_ERROR_ON(act_info.enabled() && !is_data_type_float(dst->data_type())); |
| |
| // Validate in case of configured dst |
| if(dst->total_size() > 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 2, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src1, dst); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0), "Wrong shape for dst"); |
| } |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| ClComplexMulKernel::ClComplexMulKernel() |
| { |
| _type = CLKernelType::ELEMENTWISE; |
| } |
| |
| void ClComplexMulKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_complex(src1, src2, dst, act_info)); |
| |
| auto padding_info = get_padding_info({ src1, src2, dst }); |
| |
| const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape()); |
| auto_init_if_empty(*dst, src1->clone()->set_tensor_shape(out_shape)); |
| |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(dst->data_type())); |
| if(act_info.enabled()) |
| { |
| build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation()))); |
| build_opts.add_option("-DA_VAL=" + float_to_string_with_full_precision(act_info.a())); |
| build_opts.add_option("-DB_VAL=" + float_to_string_with_full_precision(act_info.b())); |
| } |
| |
| // Create kernel |
| _kernel = create_kernel(compile_context, "pixelwise_mul_complex", build_opts.options()); |
| |
| Window win = calculate_max_window(*dst, Steps(vec_size_complex)); |
| ICLKernel::configure_internal(win); |
| |
| ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); |
| } |
| |
| Status ClComplexMulKernel::validate(const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_complex(src1, src2, dst, act_info)); |
| |
| return Status{}; |
| } |
| |
| void ClComplexMulKernel::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_0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0)); |
| const auto src_1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); |
| auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); |
| |
| const TensorShape &in_shape1 = src_0->info()->tensor_shape(); |
| const TensorShape &in_shape2 = src_1->info()->tensor_shape(); |
| const TensorShape &out_shape = dst->info()->tensor_shape(); |
| |
| bool can_collapse = true; |
| if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1) |
| { |
| can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); |
| for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); ++d) |
| { |
| can_collapse = (in_shape1[d] == in_shape2[d]); |
| } |
| } |
| |
| bool has_collapsed = false; |
| Window collapsed = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window; |
| |
| const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; |
| const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; |
| |
| Window slice = collapsed.first_slice_window_3D(); |
| Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); |
| Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); |
| |
| do |
| { |
| unsigned int idx = 0; |
| add_3D_tensor_argument(idx, src_0, slice_input1); |
| add_3D_tensor_argument(idx, src_1, slice_input2); |
| add_3D_tensor_argument(idx, dst, slice); |
| enqueue(queue, *this, slice, lws_hint()); |
| |
| ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1)); |
| ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2)); |
| } |
| while(collapsed.slide_window_slice_3D(slice)); |
| } |
| } // namespace kernels |
| } // namespace opencl |
| } // namespace arm_compute |
