| /* |
| * Copyright (c) 2017-2021 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/NEON/kernels/NESoftmaxLayerKernel.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| #include "src/core/CPP/Validate.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| |
| #include "src/core/NEON/kernels/softmax/impl/NEON/list.h" |
| #include "src/core/NEON/kernels/softmax/impl/SVE/list.h" |
| #include "src/core/common/Registrars.h" |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| struct SoftmaxSelectorData |
| { |
| DataType dt; |
| }; |
| using SoftmaxSelectorPtr = std::add_pointer<bool(const SoftmaxSelectorData &data)>::type; |
| using SoftmaxLogits1DMaxKernelPtr = std::add_pointer<void(const ITensor *, ITensor *, const Window &)>::type; |
| using SoftmaxLogits1DKernelPtr = std::add_pointer<void(const ITensor *, const ITensor *, void *const, ITensor *, float, bool, const Window &)>::type; |
| |
| struct SoftmaxLogits1DKernel |
| { |
| const char *name; |
| const SoftmaxSelectorPtr is_selected; |
| SoftmaxLogits1DKernelPtr ukernel; |
| }; |
| |
| struct SoftmaxLogits1DMaxKernel |
| { |
| const char *name; |
| const SoftmaxSelectorPtr is_selected; |
| SoftmaxLogits1DMaxKernelPtr ukernel; |
| }; |
| |
| static const SoftmaxLogits1DKernel available_logits_1d_kernels[] = |
| { |
| #if defined(__ARM_FEATURE_SVE) |
| { |
| "sve_softmax_logits_1d_float", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); }, |
| REGISTER_FP32_SVE(arm_compute::cpu::sve_softmax_logits_1d_float<float>) |
| }, |
| { |
| "sve_softmax_logits_1d_float", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); }, |
| REGISTER_FP16_SVE(arm_compute::cpu::sve_softmax_logits_1d_float<float16_t>) |
| }, |
| #else /* !defined(__ARM_FEATURE_SVE) */ |
| { |
| "neon_softmax_logits_1d_float", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); }, |
| REGISTER_FP32_NEON(arm_compute::cpu::neon_softmax_logits_1d_float<float>) |
| }, |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) |
| { |
| "neon_softmax_logits_1d_float", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); }, |
| REGISTER_FP16_NEON(arm_compute::cpu::neon_softmax_logits_1d_float<float16_t>) |
| }, |
| #endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */ |
| #endif /* defined(__ARM_FEATURE_SVE) */ |
| |
| #if defined(__ARM_FEATURE_SVE2) |
| { |
| "sve_softmax_logits_1d_quantized", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); }, |
| REGISTER_QASYMM8_SVE(arm_compute::cpu::sve_softmax_logits_1d_quantized<qasymm8_t>) |
| }, |
| { |
| "sve_softmax_logits_1d_quantized", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); }, |
| REGISTER_QASYMM8_SIGNED_SVE(arm_compute::cpu::sve_softmax_logits_1d_quantized<qasymm8_signed_t>) |
| }, |
| #else /* !defined(__ARM_FEATURE_SVE2) */ |
| { |
| "neon_softmax_logits_1d_quantized", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); }, |
| REGISTER_QASYMM8_NEON(arm_compute::cpu::neon_softmax_logits_1d_quantized<qasymm8_t>) |
| }, |
| { |
| "neon_softmax_logits_1d_quantized", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); }, |
| REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::neon_softmax_logits_1d_quantized<qasymm8_signed_t>) |
| }, |
| #endif /* defined(__ARM_FEATURE_SVE2) */ |
| |
| }; |
| |
| static const SoftmaxLogits1DMaxKernel available_logits_1d_max_kernels[] = |
| { |
| #if defined(__ARM_FEATURE_SVE) |
| { |
| "sve_logits_1d_max", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); }, |
| REGISTER_FP32_SVE(arm_compute::cpu::sve_logits_1d_max<float>) |
| }, |
| { |
| "sve_logits_1d_max", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); }, |
| REGISTER_FP16_SVE(arm_compute::cpu::sve_logits_1d_max<float16_t>) |
| }, |
| { |
| "sve_logits_1d_max", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); }, |
| REGISTER_QASYMM8_SVE(arm_compute::cpu::sve_logits_1d_max<qasymm8_t>) |
| }, |
| { |
| "sve_logits_1d_max", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); }, |
| REGISTER_QASYMM8_SIGNED_SVE(arm_compute::cpu::sve_logits_1d_max<qasymm8_signed_t>) |
| }, |
| #else /* !defined(__ARM_FEATURE_SVE) */ |
| { |
| "neon_logits_1d_max", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); }, |
| REGISTER_FP32_NEON(arm_compute::cpu::neon_logits_1d_max<float>) |
| }, |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) |
| { |
| "neon_logits_1d_max", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); }, |
| REGISTER_FP16_NEON(arm_compute::cpu::neon_logits_1d_max<float16_t>) |
| }, |
| #endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */ |
| { |
| "neon_logits_1d_max", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); }, |
| REGISTER_QASYMM8_NEON(arm_compute::cpu::neon_logits_1d_max<qasymm8_t>) |
| }, |
| { |
| "neon_logits_1d_max", |
| [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); }, |
| REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::neon_logits_1d_max<qasymm8_signed_t>) |
| }, |
| #endif /* defined(__ARM_FEATURE_SVE) */ |
| }; |
| |
| const SoftmaxLogits1DKernel *get_implementation_logits(const SoftmaxSelectorData &data) |
| { |
| for(const auto &uk : available_logits_1d_kernels) |
| { |
| if(uk.is_selected({ data.dt })) |
| { |
| return &uk; |
| } |
| } |
| return nullptr; |
| } |
| |
| const SoftmaxLogits1DMaxKernel *get_implementation_logits_max(const SoftmaxSelectorData &data) |
| { |
| for(const auto &uk : available_logits_1d_max_kernels) |
| { |
| if(uk.is_selected({ data.dt })) |
| { |
| return &uk; |
| } |
| } |
| return nullptr; |
| } |
| |
| Status validate_arguments_logits_1d_max(const ITensorInfo &input, const ITensorInfo &output) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); |
| |
| // Validate in case of configured output |
| if(output.total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&input, &output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output.tensor_shape(), TensorShape(input.tensor_shape()).set(0, 1)); |
| } |
| |
| return Status{}; |
| } |
| |
| } // namespace |
| |
| NELogits1DMaxKernel::NELogits1DMaxKernel() |
| : _border_size() |
| { |
| } |
| |
| BorderSize NELogits1DMaxKernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void NELogits1DMaxKernel::configure(const ITensor *input, ITensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input->info(), output->info()); |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_logits_1d_max(*input->info(), *output->info())); |
| // Configure kernel window |
| |
| // Softmax across the x dimension |
| const TensorShape output_shape = TensorShape(input->info()->tensor_shape()).set(0, 1); |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->quantization_info()); |
| |
| Window win = calculate_max_window(*input->info(), Steps()); |
| Coordinates coord; |
| coord.set_num_dimensions(output->info()->num_dimensions()); |
| output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); |
| |
| _input = input; |
| _output = output; |
| |
| const int input_width = input->info()->valid_region().shape.x(); |
| const int num_elems_processed_per_iteration = 16U / data_size_from_type(input->info()->data_type()); |
| const int num_elems_read_per_iteration = ceil_to_multiple(input_width, num_elems_processed_per_iteration); |
| |
| _border_size = BorderSize(0, num_elems_read_per_iteration - input_width, 0, 0); |
| |
| INEKernel::configure(win); |
| } |
| |
| Status NELogits1DMaxKernel::validate(const ITensorInfo *input, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_logits_1d_max(*input, *output)); |
| |
| return Status{}; |
| } |
| |
| void NELogits1DMaxKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| |
| const auto *uk = get_implementation_logits_max(SoftmaxSelectorData{ _input->info()->data_type() }); |
| uk->ukernel(_input, _output, window); |
| } |
| |
| namespace |
| { |
| Status validate_arguments_logits_softmax(const ITensorInfo &input, const ITensorInfo &max, |
| const ITensorInfo &output, const float beta, const ITensorInfo &tmp, bool is_log) |
| { |
| ARM_COMPUTE_UNUSED(beta); |
| // Check input |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); |
| |
| const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(input.data_type()); |
| |
| // Check max |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &max); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(TensorShape(input.tensor_shape()).set(0, 1), max.tensor_shape()); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&input, &max); |
| |
| // Check output if configured |
| if(output.total_size() != 0) |
| { |
| const QuantizationInfo output_quantization = is_quantized_asymmetric ? arm_compute::get_softmax_output_quantization_info(input.data_type(), is_log) : output.quantization_info(); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&input, &output); |
| ARM_COMPUTE_RETURN_ERROR_ON(output.quantization_info() != output_quantization); |
| } |
| |
| // Check tmp if configured |
| if(tmp.total_size() != 0) |
| { |
| const DataType tmp_data_type = is_quantized_asymmetric ? DataType::F32 : input.data_type(); |
| ARM_COMPUTE_RETURN_ERROR_ON(tmp.data_type() != tmp_data_type); |
| // We could potentially reduce tmp memory if we could predict or make an assumption |
| // on the maximum number of threads that will run in parallel. |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&input, &tmp); |
| } |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| template <bool IS_LOG> |
| NELogits1DSoftmaxKernel<IS_LOG>::NELogits1DSoftmaxKernel() |
| : _input(nullptr), _max(nullptr), _output(nullptr), _beta(1.0f), _tmp(nullptr) |
| { |
| } |
| |
| template <bool IS_LOG> |
| void NELogits1DSoftmaxKernel<IS_LOG>::configure(const ITensor *input, const ITensor *max, ITensor *output, const float beta, ITensor *tmp) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, max, output, tmp); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input->info(), max->info(), output->info(), tmp->info()); |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_logits_softmax(*input->info(), *max->info(), *output->info(), beta, *tmp->info(), IS_LOG)); |
| |
| // Configure kernel window |
| const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(input->info()->data_type()); |
| |
| // Output auto initialization if not yet initialized |
| const QuantizationInfo output_quantization = is_quantized_asymmetric ? arm_compute::get_softmax_output_quantization_info(input->info()->data_type(), IS_LOG) : output->info()->quantization_info(); |
| auto_init_if_empty(*output->info(), TensorInfo(*input->info()).set_quantization_info(output_quantization).reset_padding()); |
| |
| // Tmp auto initialization if not yet initialized |
| const DataType tmp_data_type = is_quantized_asymmetric ? DataType::F32 : input->info()->data_type(); |
| auto_init_if_empty(*tmp->info(), TensorInfo(*input->info()).set_data_type(tmp_data_type).reset_padding()); |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*max->info(), Steps()); |
| Coordinates coord; |
| coord.set_num_dimensions(output->info()->num_dimensions()); |
| output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); |
| |
| _input = input; |
| _max = max; |
| _output = output; |
| _beta = beta; |
| _tmp = tmp; |
| |
| INEKernel::configure(win); |
| } |
| |
| template <bool IS_LOG> |
| Status NELogits1DSoftmaxKernel<IS_LOG>::validate(const ITensorInfo *input, const ITensorInfo *max, |
| const ITensorInfo *output, const float beta, const ITensorInfo *tmp) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, max, output, tmp); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_logits_softmax(*input, *max, *output, beta, *tmp, IS_LOG)); |
| |
| return Status{}; |
| } |
| |
| template <bool IS_LOG> |
| void NELogits1DSoftmaxKernel<IS_LOG>::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| |
| const unsigned int num_elems_processed_per_iteration = _input->info()->valid_region().shape.x(); |
| const unsigned int tmp_size_for_thread = _tmp->info()->element_size() * num_elems_processed_per_iteration; |
| |
| ARM_COMPUTE_ERROR_ON(_tmp->info()->total_size() < (info.num_threads * tmp_size_for_thread)); |
| |
| void *tmp_for_thread = _tmp->buffer() + (info.thread_id * tmp_size_for_thread); |
| |
| const auto *uk = get_implementation_logits(SoftmaxSelectorData{ _input->info()->data_type() }); |
| uk->ukernel(_input, _max, tmp_for_thread, _output, _beta, IS_LOG, window); |
| } |
| |
| template class NELogits1DSoftmaxKernel<true>; |
| template class NELogits1DSoftmaxKernel<false>; |
| |
| } // namespace arm_compute |