| /* |
| * Copyright (c) 2020-2023 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/CLUtils.h" |
| |
| #include "arm_compute/core/utils/ActivationFunctionUtils.h" |
| #include "arm_compute/core/CL/CLCompileContext.h" |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/StringUtils.h" |
| #include "support/StringSupport.h" |
| |
| #include "src/core/experimental/PostOpUtils.h" |
| |
| namespace arm_compute |
| { |
| cl::Image2D create_image2d_from_tensor(const ICLTensor *tensor, CLImage2DType image_type) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(tensor); |
| |
| const cl::Context &ctx = CLKernelLibrary::get().context(); |
| const cl::Buffer &buffer = tensor->cl_buffer(); |
| const ITensorInfo *info = tensor->info(); |
| ARM_COMPUTE_ERROR_ON_MSG(info->lock_paddings(), |
| "Tensor paddings must not be locked to allow extending paddings to satisfy cl_image pitch alignment requirement"); |
| |
| const size_t image_w{ info->dimension(0) / 4 }; |
| const size_t image_h{ info->tensor_shape().total_size() / info->dimension(0) }; |
| const size_t max_image_w{ CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>() }; |
| const size_t max_image_h{ CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>() }; |
| |
| ARM_COMPUTE_UNUSED(max_image_w, max_image_h); |
| ARM_COMPUTE_ERROR_ON_MSG(image_w > max_image_w, "Image width exceeds maximum width for exporting to cl_image"); |
| ARM_COMPUTE_ERROR_ON_MSG(image_h > max_image_h, "Image height exceeds maximum height for exporting to cl_image"); |
| |
| const TensorShape shape2d(image_w, image_h); |
| const size_t image_row_pitch = info->strides_in_bytes()[1]; |
| |
| return create_image2d_from_buffer(ctx, buffer, shape2d, info->data_type(), image_row_pitch, image_type); |
| } |
| |
| cl::Image2D create_image2d_from_buffer(const cl::Context &ctx, const cl::Buffer &buffer, const TensorShape &shape2d, DataType data_type, size_t image_row_pitch, CLImage2DType image_type) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(!image2d_from_buffer_supported(CLKernelLibrary::get().get_device()), |
| "The extension cl_khr_image2d_from_buffer is not supported on the target platform"); |
| ARM_COMPUTE_ERROR_ON_MSG(get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()) == 0, |
| "Impossible to retrieve the cl_image pitch alignment"); |
| ARM_COMPUTE_ERROR_ON_MSG(buffer.get() == nullptr, |
| "Cannot create cl_image from empty cl_buffer"); |
| |
| cl_channel_type cl_data_type; |
| |
| switch(data_type) |
| { |
| case DataType::F32: |
| cl_data_type = CL_FLOAT; |
| break; |
| case DataType::F16: |
| cl_data_type = CL_HALF_FLOAT; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Data type not support with OpenCL image2d"); |
| } |
| |
| cl_mem cl_image; |
| cl_int err = CL_SUCCESS; |
| |
| const cl_image_format format = { CL_RGBA, cl_data_type }; |
| |
| cl_image_desc desc; |
| memset(&desc, 0, sizeof(desc)); |
| desc.image_type = CL_MEM_OBJECT_IMAGE2D; |
| desc.mem_object = buffer(); |
| desc.image_row_pitch = image_row_pitch; |
| desc.image_width = shape2d[0]; |
| desc.image_height = shape2d[1]; |
| |
| switch(image_type) |
| { |
| case CLImage2DType::ReadOnly: |
| cl_image = clCreateImage(ctx(), CL_MEM_READ_ONLY, &format, &desc, nullptr, &err); |
| break; |
| case CLImage2DType::WriteOnly: |
| cl_image = clCreateImage(ctx(), CL_MEM_WRITE_ONLY, &format, &desc, nullptr, &err); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Unsupported CLImage2DType"); |
| } |
| |
| ARM_COMPUTE_UNUSED(err); |
| ARM_COMPUTE_ERROR_ON_MSG(err != CL_SUCCESS, "Error during the creation of CL image from buffer"); |
| |
| return cl::Image2D(cl_image); |
| } |
| |
| namespace experimental |
| { |
| PostOpCLKernelUtils::PostOpCLKernelUtils(const Config &supported_config) |
| : _supported_config(supported_config) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(supported_config.empty(), "Empty PostOp CL kernel support configuration is not allowed"); |
| for(auto it = _supported_config.begin(); it != _supported_config.end(); ++it) |
| { |
| auto post_op_sequence = it->first; |
| auto post_op_slots = std::get<1>(it->second); |
| ARM_COMPUTE_ERROR_ON_MSG(post_op_sequence.size() != post_op_slots.size(), "The number of PostOps must be the same as that of the assigned slots"); |
| } |
| } |
| |
| bool PostOpCLKernelUtils::are_post_op_shapes_compliant(const ITensorInfo *dst, const experimental::PostOpList<ITensorInfo *> &post_ops) |
| { |
| for(const auto &op : post_ops.get_list()) |
| { |
| for(const auto &tensor : op->arguments()) |
| { |
| const TensorShape &out_shape = TensorShape::broadcast_shape(dst->tensor_shape(), (*tensor)->tensor_shape()); |
| // All post ops must be elementwise and must not alter the shape of the original dst tensor after broadcasting |
| if(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0)) |
| { |
| return false; |
| } |
| // NOTE: Kernel limitation: currently only the following broadcasting types are supported: |
| // 1. Post op arg is scalar, broadcast in both first and second dims |
| // 2. Post op arg is of shape: second dim=1, first dim=N, broadcast only in second dim |
| // This means this case: Post op arg is of shape: second dim=M, first dim=1, broadcast only in first dim, is NOT supported |
| if(dst->dimension(0) > 1 && dst->dimension(1) > 1 && (*tensor)->dimension(0) == 1 && (*tensor)->dimension(1) > 1) |
| { |
| return false; |
| } |
| } |
| } |
| return true; |
| } |
| |
| bool PostOpCLKernelUtils::is_post_op_sequence_supported(const PostOpList<ITensorInfo *> &post_ops) const |
| { |
| if(post_ops.size() == 0) |
| { |
| return true; // Always support cases where no post op is specified |
| } |
| const auto post_op_sequence = get_post_op_sequence(post_ops); |
| |
| return _supported_config.find(post_op_sequence) != _supported_config.end(); |
| } |
| |
| void PostOpCLKernelUtils::set_post_ops_cl_build_options(CLBuildOptions &build_opts, const PostOpList<ITensorInfo *> &post_ops) const |
| { |
| const auto post_op_sequence = get_post_op_sequence(post_ops); |
| const auto slots = std::get<1>(_supported_config.at(post_op_sequence)); |
| for(size_t post_op_id = 0; post_op_id < post_ops.size(); ++post_op_id) |
| { |
| const auto &post_op = post_ops.get_list().at(post_op_id); |
| const auto slot_prefix = "-DP" + support::cpp11::to_string(slots[post_op_id]); |
| if(post_op->type() == experimental::PostOpType::Activation) |
| { |
| const auto _post_op = utils::cast::polymorphic_downcast<const experimental::PostOpAct<ITensorInfo *> *>(post_op.get()); |
| const auto act_type = slot_prefix + "_ACTIVATION_TYPE=" + lower_string(string_from_activation_func(_post_op->_act_info.activation())); |
| const auto act_a_val = slot_prefix + "_ACTIVATION_A_VAL=" + float_to_string_with_full_precision(_post_op->_act_info.a()); |
| const auto act_b_val = slot_prefix + "_ACTIVATION_B_VAL=" + float_to_string_with_full_precision(_post_op->_act_info.b()); |
| build_opts.add_option(act_type); |
| build_opts.add_option(act_a_val); |
| build_opts.add_option(act_b_val); |
| } |
| else if(post_op->type() == experimental::PostOpType::Eltwise_Add) |
| { |
| size_t arg_id = 1; |
| const auto eltwise_op = slot_prefix + "_ELTWISE_OP=ADD" + "_X_POS_" + support::cpp11::to_string(post_op->prev_dst_pos()); |
| build_opts.add_option(eltwise_op); |
| for(const auto &tensor : post_op->arguments()) |
| { |
| const auto height = slot_prefix + "_ELTWISE_ARG" + support::cpp11::to_string(arg_id) + "_HEIGHT=" + support::cpp11::to_string((*tensor)->dimension(1)); |
| const auto width = slot_prefix + "_ELTWISE_ARG" + support::cpp11::to_string(arg_id) + "_WIDTH=" + support::cpp11::to_string((*tensor)->dimension(0)); |
| build_opts.add_option(height); |
| build_opts.add_option(width); |
| ++arg_id; |
| } |
| } |
| else if(post_op->type() == experimental::PostOpType::Eltwise_PRelu) |
| { |
| size_t arg_id = 1; |
| const auto eltwise_op = slot_prefix + "_ELTWISE_OP=PRELU" + "_X_POS_" + support::cpp11::to_string(post_op->prev_dst_pos()); |
| build_opts.add_option(eltwise_op); |
| for(const auto &tensor : post_op->arguments()) |
| { |
| const auto height = slot_prefix + "_ELTWISE_ARG" + support::cpp11::to_string(arg_id) + "_HEIGHT=" + support::cpp11::to_string((*tensor)->dimension(1)); |
| const auto width = slot_prefix + "_ELTWISE_ARG" + support::cpp11::to_string(arg_id) + "_WIDTH=" + support::cpp11::to_string((*tensor)->dimension(0)); |
| build_opts.add_option(height); |
| build_opts.add_option(width); |
| ++arg_id; |
| } |
| } |
| } |
| } |
| |
| void PostOpCLKernelUtils::set_post_ops_cl_kernel_name(std::string &kernel_name, const PostOpList<ITensorInfo *> &post_ops) const |
| { |
| const auto post_op_sequence = get_post_op_sequence(post_ops); |
| const auto postfix = std::get<0>(_supported_config.at(post_op_sequence)); |
| kernel_name += postfix; |
| } |
| } // namespace experimental |
| |
| } // namespace arm_compute |