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Diffstat (limited to 'src/cpu/kernels/CpuElementwiseKernel.cpp')
| -rw-r--r-- | src/cpu/kernels/CpuElementwiseKernel.cpp | 511 |
1 files changed, 511 insertions, 0 deletions
diff --git a/src/cpu/kernels/CpuElementwiseKernel.cpp b/src/cpu/kernels/CpuElementwiseKernel.cpp new file mode 100644 index 0000000000..57a3f39822 --- /dev/null +++ b/src/cpu/kernels/CpuElementwiseKernel.cpp @@ -0,0 +1,511 @@ +/* + * Copyright (c) 2018-2022 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/cpu/kernels/CpuElementwiseKernel.h" + +#include "arm_compute/core/Helpers.h" + +#include "src/core/common/Registrars.h" +#include "src/core/CPP/Validate.h" +#include "src/core/helpers/AutoConfiguration.h" +#include "src/core/helpers/WindowHelpers.h" +#include "src/cpu/kernels/elementwise_binary/list.h" + +#include <arm_neon.h> + +#if defined(ENABLE_FP32_KERNELS) +namespace +{ +static constexpr size_t default_min_max_mws_N1_fp32_neon = 25308; +static constexpr size_t default_min_max_mws_V1_fp32_neon = 34772; +static constexpr size_t default_div_mws_N1_fp32_neon = 19043; +static constexpr size_t default_div_mws_V1_fp32_neon = 25511; +} // namespace +#endif /* ENABLE_FP32_KERNELS */ + +namespace arm_compute +{ +namespace cpu +{ +namespace kernels +{ +namespace +{ +template <ArithmeticOperation op> +const std::vector<CpuElementwiseKernel<CpuArithmeticKernel>::ElementwiseKernel> available_kernels_arithmetic = { + {"sve2_qu8_arithmetic", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::QASYMM8 && data.isa.sve2 && static_cast<ArithmeticOperation>(data.op) == op; }, + REGISTER_QASYMM8_SVE2(sve2_qasymm8_elementwise_binary<op>)}, + {"sve2_qs8_arithmetic", + [](const ElementwiseDataTypeISASelectorData &data) { + return data.dt == DataType::QASYMM8_SIGNED && data.isa.sve2 && static_cast<ArithmeticOperation>(data.op) == op; + }, + REGISTER_QASYMM8_SIGNED_SVE2(sve2_qasymm8_signed_elementwise_binary<op>)}, + {"sve_fp32_arithmetic", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::F32 && data.isa.sve && static_cast<ArithmeticOperation>(data.op) == op; }, + REGISTER_FP32_SVE(sve_fp32_elementwise_binary<op>)}, + {"sve_s32_arithmetic", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::S32 && data.isa.sve && static_cast<ArithmeticOperation>(data.op) == op; }, + REGISTER_INTEGER_SVE(sve_s32_elementwise_binary<op>)}, + {"sve_s16_arithmetic", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::S16 && data.isa.sve && static_cast<ArithmeticOperation>(data.op) == op; }, + REGISTER_INTEGER_SVE(sve_s16_elementwise_binary<op>)}, + {"sve_fp16_arithmetic", + [](const ElementwiseDataTypeISASelectorData &data) + { + return data.dt == DataType::F16 && data.isa.sve && data.isa.fp16 && + static_cast<ArithmeticOperation>(data.op) == op; + }, + REGISTER_FP16_SVE(sve_fp16_elementwise_binary<op>)}, + {"neon_fp32_arithmetic", + + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::F32 && static_cast<ArithmeticOperation>(data.op) == op; }, + REGISTER_FP32_NEON(neon_fp32_elementwise_binary<op>)}, + {"neon_s32_arithmetic", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::S32 && static_cast<ArithmeticOperation>(data.op) == op; }, + REGISTER_INTEGER_NEON(neon_s32_elementwise_binary<op>)}, + {"neon_fp16_arithmetic", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::F16 && data.isa.fp16 && static_cast<ArithmeticOperation>(data.op) == op; }, + REGISTER_FP16_NEON(neon_fp16_elementwise_binary<op>)}, + {"neon_s16_arithmetic", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::S16 && static_cast<ArithmeticOperation>(data.op) == op; }, + REGISTER_INTEGER_NEON(neon_s16_elementwise_binary<op>)}, + {"neon_qu8_arithmetic", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::QASYMM8 && static_cast<ArithmeticOperation>(data.op) == op; }, + REGISTER_QASYMM8_NEON(neon_qasymm8_elementwise_binary<op>)}, + {"neon_qs8_arithmetic", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::QASYMM8_SIGNED && static_cast<ArithmeticOperation>(data.op) == op; }, + REGISTER_QASYMM8_SIGNED_NEON(neon_qasymm8_signed_elementwise_binary<op>)}, +}; +template <ComparisonOperation op> +const std::vector<CpuElementwiseKernel<CpuComparisonKernel>::ElementwiseKernel> available_kernels_comperison = { + {"sve2_qu8_comparison", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::QASYMM8 && data.isa.sve2 && static_cast<ComparisonOperation>(data.op) == op; }, + REGISTER_QASYMM8_SVE2(sve2_qasymm8_comparison_elementwise_binary<op>)}, + {"sve2_qs8_comparison", + [](const ElementwiseDataTypeISASelectorData &data) { + return data.dt == DataType::QASYMM8_SIGNED && data.isa.sve2 && static_cast<ComparisonOperation>(data.op) == op; + }, + REGISTER_QASYMM8_SIGNED_SVE2(sve2_qasymm8_signed_comparison_elementwise_binary<op>)}, + {"sve_u8_comparison", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::U8 && data.isa.sve && static_cast<ComparisonOperation>(data.op) == op; }, + REGISTER_INTEGER_SVE(sve_u8_comparison_elementwise_binary<op>)}, + {"sve_fp32_comparison", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::F32 && data.isa.sve && static_cast<ComparisonOperation>(data.op) == op; }, + REGISTER_FP32_SVE(sve_fp32_comparison_elementwise_binary<op>)}, + {"sve_s16_comparison", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::S16 && data.isa.sve && static_cast<ComparisonOperation>(data.op) == op; }, + REGISTER_INTEGER_SVE(sve_s16_comparison_elementwise_binary<op>)}, + {"sve_s32_comparison", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::S32 && data.isa.sve && static_cast<ComparisonOperation>(data.op) == op; }, + REGISTER_INTEGER_SVE(sve_s32_comparison_elementwise_binary<op>)}, + {"sve_fp16_comparison", + [](const ElementwiseDataTypeISASelectorData &data) + { + return data.dt == DataType::F16 && data.isa.sve && data.isa.fp16 && + static_cast<ComparisonOperation>(data.op) == op; + }, + REGISTER_FP16_SVE(sve_fp16_comparison_elementwise_binary<op>)}, + {"neon_u8_comparison", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::U8 && static_cast<ComparisonOperation>(data.op) == op; }, + REGISTER_INTEGER_NEON(neon_u8_comparison_elementwise_binary<op>)}, + {"neon_fp32_comparison", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::F32 && static_cast<ComparisonOperation>(data.op) == op; }, + REGISTER_FP32_NEON(neon_fp32_comparison_elementwise_binary<op>)}, + {"neon_s16_comparison", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::S16 && static_cast<ComparisonOperation>(data.op) == op; }, + REGISTER_INTEGER_NEON(neon_s16_comparison_elementwise_binary<op>)}, + {"neon_s32_comparison", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::S32 && static_cast<ComparisonOperation>(data.op) == op; }, + REGISTER_INTEGER_NEON(neon_s32_comparison_elementwise_binary<op>)}, + {"neon_qu8_comparison", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::QASYMM8 && static_cast<ComparisonOperation>(data.op) == op; }, + REGISTER_QASYMM8_NEON(neon_qasymm8_comparison_elementwise_binary<op>)}, + {"neon_qs8_comparison", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::QASYMM8_SIGNED && static_cast<ComparisonOperation>(data.op) == op; }, + REGISTER_QASYMM8_SIGNED_NEON(neon_qasymm8_signed_comparison_elementwise_binary<op>)}, + {"neon_fp16_comparison", + [](const ElementwiseDataTypeISASelectorData &data) + { return data.dt == DataType::F16 && data.isa.fp16 && static_cast<ComparisonOperation>(data.op) == op; }, + REGISTER_FP16_NEON(neon_fp16_comparison_elementwise_binary<op>)}, +}; +} // namespace + +const std::vector<CpuElementwiseKernel<CpuArithmeticKernel>::ElementwiseKernel> & +CpuArithmeticKernel::get_available_kernels() +{ + static std::vector<CpuElementwiseKernel<CpuArithmeticKernel>::ElementwiseKernel> available_kernels; + std::move(available_kernels_arithmetic<ArithmeticOperation::ADD>.begin(), + available_kernels_arithmetic<ArithmeticOperation::ADD>.end(), std::back_inserter(available_kernels)); + std::move(available_kernels_arithmetic<ArithmeticOperation::SUB>.begin(), + available_kernels_arithmetic<ArithmeticOperation::SUB>.end(), std::back_inserter(available_kernels)); + std::move(available_kernels_arithmetic<ArithmeticOperation::DIV>.begin(), + available_kernels_arithmetic<ArithmeticOperation::DIV>.end(), std::back_inserter(available_kernels)); + std::move(available_kernels_arithmetic<ArithmeticOperation::MIN>.begin(), + available_kernels_arithmetic<ArithmeticOperation::MIN>.end(), std::back_inserter(available_kernels)); + std::move(available_kernels_arithmetic<ArithmeticOperation::MAX>.begin(), + available_kernels_arithmetic<ArithmeticOperation::MAX>.end(), std::back_inserter(available_kernels)); + std::move(available_kernels_arithmetic<ArithmeticOperation::SQUARED_DIFF>.begin(), + available_kernels_arithmetic<ArithmeticOperation::SQUARED_DIFF>.end(), + std::back_inserter(available_kernels)); + std::move(available_kernels_arithmetic<ArithmeticOperation::POWER>.begin(), + available_kernels_arithmetic<ArithmeticOperation::POWER>.end(), std::back_inserter(available_kernels)); + std::move(available_kernels_arithmetic<ArithmeticOperation::PRELU>.begin(), + available_kernels_arithmetic<ArithmeticOperation::PRELU>.end(), std::back_inserter(available_kernels)); + + return available_kernels; +} + +const std::vector<CpuElementwiseKernel<CpuComparisonKernel>::ElementwiseKernel> & +CpuComparisonKernel::get_available_kernels() +{ + static std::vector<CpuElementwiseKernel<CpuComparisonKernel>::ElementwiseKernel> available_kernels; + std::move(available_kernels_comperison<ComparisonOperation::Equal>.begin(), + available_kernels_comperison<ComparisonOperation::Equal>.end(), std::back_inserter(available_kernels)); + std::move(available_kernels_comperison<ComparisonOperation::NotEqual>.begin(), + available_kernels_comperison<ComparisonOperation::NotEqual>.end(), std::back_inserter(available_kernels)); + std::move(available_kernels_comperison<ComparisonOperation::Greater>.begin(), + available_kernels_comperison<ComparisonOperation::Greater>.end(), std::back_inserter(available_kernels)); + std::move(available_kernels_comperison<ComparisonOperation::GreaterEqual>.begin(), + available_kernels_comperison<ComparisonOperation::GreaterEqual>.end(), + std::back_inserter(available_kernels)); + std::move(available_kernels_comperison<ComparisonOperation::Less>.begin(), + available_kernels_comperison<ComparisonOperation::Less>.end(), std::back_inserter(available_kernels)); + std::move(available_kernels_comperison<ComparisonOperation::LessEqual>.begin(), + available_kernels_comperison<ComparisonOperation::LessEqual>.end(), + std::back_inserter(available_kernels)); + + return available_kernels; +} + +template <class Derived> +Status CpuElementwiseKernel<Derived>::validate_arguments_common(const ITensorInfo &src0, + const ITensorInfo &src1, + const ITensorInfo &dst) +{ + ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&src0); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src0, &src1); + + const TensorShape out_shape = TensorShape::broadcast_shape(src0.tensor_shape(), src1.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_MSG(detail::have_different_dimensions(out_shape, dst.tensor_shape(), 0), + "Wrong shape for output"); + } + + return Status{}; +} + +void CpuArithmeticKernel::configure_common(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); + + const auto *uk = CpuArithmeticKernel::get_implementation( + ElementwiseDataTypeISASelectorData{src0->data_type(), CPUInfo::get().get_isa(), static_cast<int>(_op)}); + + ARM_COMPUTE_ERROR_ON(uk == nullptr || uk->ukernel == nullptr); + + _run_method = uk->ukernel; + _name = std::string("CpuArithmeticKernel").append("/").append(uk->name); + + // If any of shapes is dynamic, expect a configured window and dst at run-time. + if (src0->is_dynamic() || src1->is_dynamic()) + { + return; + } + + auto shape_and_window = compute_output_shape_and_window(src0->tensor_shape(), src1->tensor_shape()); + auto_init_if_empty(*dst, shape_and_window.first, 1, src0->data_type()); + ICpuKernel::configure(shape_and_window.second); +} + +void CpuComparisonKernel::configure_common(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); + + const auto *uk = CpuComparisonKernel::get_implementation( + ElementwiseDataTypeISASelectorData{src0->data_type(), CPUInfo::get().get_isa(), static_cast<int>(_op)}); + + ARM_COMPUTE_ERROR_ON(uk == nullptr || uk->ukernel == nullptr); + + _run_method = uk->ukernel; + _name = std::string("CpuComparisonKernel").append("/").append(uk->name); + + // If any of shapes is dynamic, expect a configured window and dst at run-time. + if (src0->is_dynamic() || src1->is_dynamic()) + { + return; + } + + auto shape_and_window = compute_output_shape_and_window(src0->tensor_shape(), src1->tensor_shape()); + auto_init_if_empty(*dst, shape_and_window.first, 1, src0->data_type()); + ICpuKernel::configure(shape_and_window.second); +} + +template <class Derived> +void CpuElementwiseKernel<Derived>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_UNUSED(info); + ARM_COMPUTE_ERROR_ON(_run_method == nullptr); + + auto src0 = tensors.get_const_tensor(TensorType::ACL_SRC_0); + auto src1 = tensors.get_const_tensor(TensorType::ACL_SRC_1); + auto dst = tensors.get_tensor(TensorType::ACL_DST); + + _run_method(src0, src1, dst, window); +} +template void +CpuElementwiseKernel<CpuArithmeticKernel>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info); +template void +CpuElementwiseKernel<CpuComparisonKernel>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info); + +template <class Derived> +const char *CpuElementwiseKernel<Derived>::name() const +{ + return _name.c_str(); +} +template const char *CpuElementwiseKernel<CpuArithmeticKernel>::name() const; +template const char *CpuElementwiseKernel<CpuComparisonKernel>::name() const; + +/** Arithmetic operators (min, max, squared_diff) */ +void CpuArithmeticKernel::configure(ArithmeticOperation op, + const ITensorInfo *src0, + const ITensorInfo *src1, + ITensorInfo *dst) +{ + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*src0, *src1, *dst)); + _op = op; + CpuArithmeticKernel::configure_common(src0, src1, dst); +} + +Status CpuArithmeticKernel::validate_arguments(const ITensorInfo &src0, const ITensorInfo &src1, const ITensorInfo &dst) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&src0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, + DataType::S16, DataType::F16, DataType::S32, DataType::F32); + // Validate in case of configured dst + if (dst.total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src0, &dst); + } + return validate_arguments_common(src0, src1, dst); +} + +Status CpuArithmeticKernel::validate(ArithmeticOperation op, + const ITensorInfo *src0, + const ITensorInfo *src1, + const ITensorInfo *dst) +{ + ARM_COMPUTE_UNUSED(op); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*src0, *src1, *dst)); + return Status{}; +} + +size_t CpuArithmeticKernel::get_mws(const CPUInfo &platform, size_t thread_count) const +{ + ARM_COMPUTE_UNUSED(thread_count); + +#if defined(ENABLE_FP32_KERNELS) + if (this->_run_method == &neon_fp32_elementwise_binary<ArithmeticOperation::MIN> || + this->_run_method == &neon_fp32_elementwise_binary<ArithmeticOperation::MAX>) + { + size_t mws = ICPPKernel::default_mws; + if (platform.get_cpu_model() == CPUModel::N1) + { + mws = default_min_max_mws_N1_fp32_neon; + } + else if (platform.get_cpu_model() == CPUModel::V1) + { + mws = default_min_max_mws_V1_fp32_neon; + } + else + { + return ICPPKernel::default_mws; + } + + // tensor is 1D or was re-interpreted as 1D + if (this->window().shape().num_dimensions() == 1) + { + return mws; + } + else + { + // scale mws down by the number of elements along all the dimensions (x, z, w, etc) except the one + // that we parallelize along (the y dimension). This allows for parallelization when the Y_SIZE is small + // but the other sizes are large, which boosts performance. + mws = static_cast<size_t>(mws / (this->window().num_iterations_total() / this->window().num_iterations(1))); + return std::max(static_cast<size_t>(1), mws); + } + } +#else /* ENABLE_FP32_KERNELS */ + ARM_COMPUTE_UNUSED(platform); +#endif /* ENABLE_FP32_KERNELS */ + return ICPPKernel::default_mws; +} + +/** The division operator */ + +void CpuDivisionKernel::configure(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst) +{ + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*src0, *src1, *dst)); + _op = ArithmeticOperation::DIV; + CpuArithmeticKernel::configure_common(src0, src1, dst); +} + +size_t CpuDivisionKernel::get_mws(const CPUInfo &platform, size_t thread_count) const +{ + ARM_COMPUTE_UNUSED(thread_count); + +#if defined(ENABLE_FP32_KERNELS) + if (this->_run_method == &neon_fp32_elementwise_binary<ArithmeticOperation::DIV>) + { + size_t mws = ICPPKernel::default_mws; + if (platform.get_cpu_model() == CPUModel::N1) + { + mws = default_div_mws_N1_fp32_neon; + } + else if (platform.get_cpu_model() == CPUModel::V1) + { + mws = default_div_mws_V1_fp32_neon; + } + else + { + return ICPPKernel::default_mws; + } + + // tensor is 1D or was re-interpreted as 1D + if (this->window().shape().num_dimensions() == 1) + { + return mws; + } + else + { + // scale mws down by the number of elements along all the dimensions (x, z, w, etc) except the one + // that we parallelize along (the y dimension). This allows for parallelization when the Y_SIZE is small + // but the other sizes are large, which boosts performance. + mws = static_cast<size_t>(mws / (this->window().num_iterations_total() / this->window().num_iterations(1))); + return std::max(static_cast<size_t>(1), mws); + } + } +#else /* ENABLE_FP32_KERNELS */ + ARM_COMPUTE_UNUSED(platform); +#endif /* ENABLE_FP32_KERNELS */ + return ICPPKernel::default_mws; +} + +Status CpuDivisionKernel::validate_arguments(const ITensorInfo &src0, const ITensorInfo &src1, const ITensorInfo &dst) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&src0, 1, DataType::S32, DataType::F16, DataType::F32); + return CpuArithmeticKernel::validate_arguments(src0, src1, dst); +} + +Status CpuDivisionKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*src0, *src1, *dst)); + return Status{}; +} + +/** The power operator */ +void CpuPowerKernel::configure(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst) +{ + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*src0, *src1, *dst)); + _op = ArithmeticOperation::POWER; + CpuArithmeticKernel::configure_common(src0, src1, dst); +} + +Status CpuPowerKernel::validate_arguments(const ITensorInfo &src0, const ITensorInfo &src1, const ITensorInfo &dst) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&src0, 1, DataType::F16, DataType::F32); + return CpuArithmeticKernel::validate_arguments(src0, src1, dst); +} + +Status CpuPowerKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*src0, *src1, *dst)); + return Status{}; +} + +/** Comparison operators (equal, not equal, less than, greater than, less than or equal, greater than or equal) */ +void CpuComparisonKernel::configure(ComparisonOperation op, + const ITensorInfo *src0, + const ITensorInfo *src1, + ITensorInfo *dst) +{ + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*src0, *src1, *dst)); + _op = op; + CpuComparisonKernel::configure_common(src0, src1, dst); +} + +Status CpuComparisonKernel::validate_arguments(const ITensorInfo &src0, const ITensorInfo &src1, const ITensorInfo &dst) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&src0, 1, DataType::U8, DataType::QASYMM8, + DataType::QASYMM8_SIGNED, DataType::S16, DataType::F16, + DataType::S32, DataType::F32); + // 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); + } + return validate_arguments_common(src0, src1, dst); +} + +Status CpuComparisonKernel::validate(ComparisonOperation op, + const ITensorInfo *src0, + const ITensorInfo *src1, + const ITensorInfo *dst) +{ + ARM_COMPUTE_UNUSED(op); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*src0, *src1, *dst)); + return Status{}; +} +} // namespace kernels +} // namespace cpu +} // namespace arm_compute |