| /* |
| * 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/CPP/Validate.h" |
| #include "src/core/common/Registrars.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; |
| } |
| #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 |