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/*
* Copyright (c) 2022-2024 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 "ClComponentElementwiseBinary.h"
#include "arm_compute/core/Validate.h"
#include "src/core/CL/CLValidate.h"
#include "src/dynamic_fusion/sketch/gpu/ckw_driver/components/GpuCkwElementwiseBinary.h"
namespace arm_compute
{
namespace experimental
{
namespace dynamic_fusion
{
namespace
{
std::set<ElementwiseBinaryCommonAttributes::ElementwiseOp> supported_ops{
ElementwiseBinaryCommonAttributes::ElementwiseOp::Add, ElementwiseBinaryCommonAttributes::ElementwiseOp::Sub,
ElementwiseBinaryCommonAttributes::ElementwiseOp::Mul};
}
Status ClComponentElementwiseBinary::validate(const ArgumentPack<ITensorInfo> &tensors,
const ElementwiseBinaryCommonAttributes &attributes)
{
const auto lhs = tensors.get_const_tensor(TensorType::ACL_SRC_0);
const auto rhs = tensors.get_const_tensor(TensorType::ACL_SRC_1);
const auto dst = tensors.get_const_tensor(TensorType::ACL_DST_0);
// Check operator type
ARM_COMPUTE_RETURN_ERROR_ON_MSG(supported_ops.find(attributes.operation()) == supported_ops.end(),
"Provided Elementwise operation not supported.");
// Check validity
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);
//Check data type for different elementwise operators
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16, DataType::S32,
DataType::S16, DataType::U8);
// dst shape is correct
const TensorShape out_shape = TensorShape::broadcast_shape(lhs->tensor_shape(), rhs->tensor_shape());
ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0),
"Wrong shape for dst.");
const auto &lhs_shape = lhs->tensor_shape();
const auto &rhs_shape = rhs->tensor_shape();
const auto &dst_shape = dst->tensor_shape();
ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(lhs_shape, dst_shape, 0) &&
detail::have_different_dimensions(rhs_shape, dst_shape, 0),
"Only LHS or RHS can be broadcasting, not both.");
// Dimension Y and Z are collapsed together in the current kernel implementation,
// hence they cannot be independently broadcast or non-broadcast.
// See: ClTemplateElementwiseBinary::get_window
ARM_COMPUTE_RETURN_ERROR_ON_MSG((lhs_shape[1] != dst_shape[1] || rhs_shape[1] != dst_shape[1]) !=
(lhs_shape[2] != dst_shape[2] || rhs_shape[2] != dst_shape[2]),
"Dimension Y and Z must both be either broadcast or non-broadcast.");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(lhs_shape, dst_shape, 3),
"LHS broadcast in dimension 3 or higher is not supported.");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(rhs_shape, dst_shape, 3),
"RHS broadcast in dimension 3 or higher is not supported.");
// Matching data type
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
// Matching data layout
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(lhs, rhs);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(lhs, dst);
// All tensor infos are initialized
ARM_COMPUTE_RETURN_ERROR_ON(lhs->tensor_shape().total_size() == 0);
ARM_COMPUTE_RETURN_ERROR_ON(rhs->tensor_shape().total_size() == 0);
ARM_COMPUTE_RETURN_ERROR_ON(dst->tensor_shape().total_size() == 0);
// Device requirements are met
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(lhs);
return Status{};
}
ClComponentElementwiseBinary::~ClComponentElementwiseBinary()
{
}
ClComponentElementwiseBinary::ClComponentElementwiseBinary(ComponentId id,
const Properties &properties,
const ArgumentPack<ITensorInfo> &tensors,
const Attributes &attributes)
: IGpuKernelComponent{id, properties, tensors},
_component_writer{std::make_unique<GpuCkwElementwiseBinary>(id, tensors, attributes)}
{
}
const IGpuCkwComponentDriver *ClComponentElementwiseBinary::ckw_component_driver() const
{
return _component_writer.get();
}
} // namespace dynamic_fusion
} // namespace experimental
} // namespace arm_compute
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