src/core/CL/CLUtils.cpp - ml/ComputeLibrary - Gitiles

 /*
  * 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 "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 "support/StringSupport.h"

 #include "src/core/CL/CLUtils.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
	/*
	* 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 "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 "support/StringSupport.h"

	#include "src/core/CL/CLUtils.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