| /* |
| * Copyright (c) 2020-2021 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/NEQLSTMLayerNormalizationKernel.h" |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| #include "src/core/CPP/Validate.h" |
| #include "src/core/NEON/NEFixedPoint.h" |
| #include "src/core/NEON/NEMath.h" |
| #include "src/core/NEON/NESymm.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| |
| #include "src/core/NEON/kernels/detail/NEActivationFunctionDetail.h" |
| |
| #include <map> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| inline std::pair<int64_t, int64_t> compute_mean_variance(int64_t sum, int64_t sum_sq, uint32_t num_input) |
| { |
| const auto temp = static_cast<int64_t>(0x100000) / num_input; |
| const auto mean = sum * 1024 / static_cast<int64_t>(num_input); |
| const int64_t variance = ((sum_sq * temp) - (mean * mean)) / 0x100000; |
| |
| return std::make_pair(mean, variance); |
| } |
| |
| inline int64x2x2_t mul_add(const int32x4_t &a, const int32x4_t &b, const int32x4_t &bias) |
| { |
| using namespace wrapper; |
| const int64x2_t a_low = vmovl(vgetlow(a)); |
| const int64x2_t a_high = vmovl(vgethigh(a)); |
| const int64x2_t b_low = vmovl(vgetlow(b)); |
| const int64x2_t b_high = vmovl(vgethigh(b)); |
| |
| const int64_t a_0 = vgetlane(a_low, 0); |
| const int64_t a_1 = vgetlane(a_low, 1); |
| const int64_t a_2 = vgetlane(a_high, 0); |
| const int64_t a_3 = vgetlane(a_high, 1); |
| |
| const int64_t b_0 = vgetlane(b_low, 0); |
| const int64_t b_1 = vgetlane(b_low, 1); |
| const int64_t b_2 = vgetlane(b_high, 0); |
| const int64_t b_3 = vgetlane(b_high, 1); |
| |
| int64x2x2_t result; |
| const int64x2_t result_0{ a_0 * b_0, a_1 * b_1 }; |
| const int64x2_t result_1{ a_2 * b_2, a_3 * b_3 }; |
| result.val[0] = vadd(vmovl(vgetlow(bias)), result_0); |
| result.val[1] = vadd(vmovl(vgethigh(bias)), result_1); |
| |
| return result; |
| } |
| } // namespace |
| |
| void NEQLSTMLayerNormalizationKernel::configure(const ITensor *input, ITensor *output, const ITensor *weight, const ITensor *bias) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weight, bias, output); |
| ARM_COMPUTE_ERROR_ON(input == output); |
| ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), output->info(), weight->info(), bias->info())); |
| |
| static const std::map<DataType, ComputeFuncType> fn_map = |
| { |
| { DataType::QSYMM16, std::mem_fn(&NEQLSTMLayerNormalizationKernel::compute_qsymm16) }, |
| }; |
| |
| _input = input; |
| _output = output; |
| _weight = weight; |
| _bias = bias; |
| _fn = fn_map.at(_input->info()->data_type()); |
| |
| auto_init_if_empty(*_output->info(), *_input->info()); |
| _output->info()->set_quantization_info(compute_output_qinfo()); |
| |
| const UniformQuantizationInfo wq_info = _weight->info()->quantization_info().uniform(); |
| const Status s = quantization::calculate_quantized_multiplier(wq_info.scale, &_output_multiplier, &_output_shift); |
| _output_shift *= -1; |
| |
| if(!bool(s)) |
| { |
| _output_multiplier = 0; |
| _output_shift = 0; |
| } |
| |
| Window win = configure_window(output); |
| INEKernel::configure(win); |
| } |
| |
| Window NEQLSTMLayerNormalizationKernel::configure_window(ITensor *target) |
| { |
| Window window = calculate_max_window(*target->info(), Steps()); |
| |
| _window_start_x = static_cast<int32_t>(window.x().start()); |
| _window_end_x = static_cast<int32_t>(window.x().end()); |
| _window_step_x = static_cast<int32_t>(vector_size_byte) / _output->info()->element_size(); |
| |
| // input and output windows will iterator over y-axis, while execute_window will handler x-axis. |
| _inout_window = window; |
| _inout_window.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| // weight and bias cannot iterator along y-axis since they are 1D. |
| _weight_window = _inout_window; |
| _weight_window.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| |
| return window; |
| } |
| |
| Status NEQLSTMLayerNormalizationKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *weight, const ITensorInfo *bias) |
| { |
| ARM_COMPUTE_UNUSED(output, bias, weight, input); |
| |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weight, bias, output); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QSYMM16); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weight, 1, DataType::QSYMM16); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > max_input_dimension); |
| ARM_COMPUTE_RETURN_ERROR_ON(weight->num_dimensions() > max_weight_dimension); |
| ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > max_bias_dimension); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape().x() != weight->tensor_shape().x()); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(weight, bias); |
| |
| if(output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); |
| } |
| |
| return Status{}; |
| } |
| |
| void NEQLSTMLayerNormalizationKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(window, info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON_MSG(!_fn, "internal function is not defined for computation"); |
| |
| _fn(*this); |
| } |
| |
| inline QuantizationInfo NEQLSTMLayerNormalizationKernel::compute_output_qinfo() |
| { |
| return QuantizationInfo(1.f / 4096); |
| } |
| |
| inline std::pair<int64_t, int64_t> NEQLSTMLayerNormalizationKernel::sum_qsymm16(const int16_t *input_ptr) |
| { |
| ARM_COMPUTE_ERROR_ON(!input_ptr); |
| |
| using AccType = int64_t; |
| using InputDataType = int16_t; |
| |
| AccType sum{ 0 }; |
| AccType sum_sq{ 0 }; |
| |
| int32_t x = _window_start_x; |
| for(; x <= _window_end_x && _window_step_x <= (_window_end_x - x); x += _window_step_x) |
| { |
| using namespace wrapper; |
| const int16x8_t val = vloadq(input_ptr + x); |
| const int32x4_t val_low = vmovl(vgetlow(val)); |
| const int32x4_t val_high = vmovl(vgethigh(val)); |
| |
| #if defined(__aarch64__) |
| sum += static_cast<AccType>(vaddv(val_low)); |
| sum += static_cast<AccType>(vaddv(val_high)); |
| |
| sum_sq += static_cast<AccType>(vaddv(vmul(val_low, val_low))); |
| sum_sq += static_cast<AccType>(vaddv(vmul(val_high, val_high))); |
| #else // __aarch64__ |
| // only AArch64 supports vaddv |
| const int64x2_t pair_sum_low = vpaddl(val_low); |
| const int64x2_t pair_sum_high = vpaddl(val_high); |
| const int64x2_t pair_sum = vadd(pair_sum_low, pair_sum_high); |
| sum += vgetlane(pair_sum, 0) + vgetlane(pair_sum, 1); |
| |
| const int32x4_t square_low = vmul(val_low, val_low); |
| const int32x4_t square_high = vmul(val_high, val_high); |
| const int64x2_t pair_sum_sq_low = vpaddl(square_low); |
| const int64x2_t pair_sum_sq_high = vpaddl(square_high); |
| const int64x2_t pair_sum_sq = vadd(pair_sum_sq_low, pair_sum_sq_high); |
| sum_sq += vgetlane(pair_sum_sq, 0) + vgetlane(pair_sum_sq, 1); |
| #endif // __aarch64__ |
| } |
| |
| for(; x < _window_end_x; ++x) |
| { |
| const InputDataType val = input_ptr[x]; |
| sum += static_cast<AccType>(val); |
| sum_sq += static_cast<AccType>(val * val); |
| } |
| |
| return std::make_pair(sum, sum_sq); |
| } |
| |
| inline void NEQLSTMLayerNormalizationKernel::normalize_qasymm16(const int16_t *input_ptr, |
| int16_t *output_ptr, |
| const int16_t *weight_ptr, |
| const int32_t *bias_ptr, |
| int32_t mean, int32_t inv_std_mul, int32_t inv_std_shift) |
| { |
| using OutputDataType = int16_t; |
| |
| using namespace wrapper; |
| const int32x4_t mean_vec = vdup_n(mean, wrapper::traits::vector_128_tag{}); |
| |
| int32_t x = _window_start_x; |
| for(; x <= _window_end_x && _window_step_x <= (_window_end_x - x); x += _window_step_x) |
| { |
| const int16x8_t val = vloadq(input_ptr + x); |
| int32x4x2_t shifted; |
| shifted.val[0] = vsub(vshlq_n_s32(vmovl(vgetlow(val)), 10), mean_vec); |
| shifted.val[1] = vsub(vshlq_n_s32(vmovl(vgethigh(val)), 10), mean_vec); |
| |
| int32x4x2_t rescaled = multiply_by_quantized_multiplier_2row(shifted, inv_std_mul, inv_std_shift); |
| |
| const int16x8_t weight_val = vloadq(weight_ptr + x); |
| const int32x4_t weight_low = vmovl(vgetlow(weight_val)); |
| const int32x4_t weight_high = vmovl(vgethigh(weight_val)); |
| |
| const int32x4_t bias_low = vloadq(bias_ptr + x); |
| const int32x4_t bias_high = vloadq(bias_ptr + 4 + x); |
| |
| int64x2x2_t result_0 = mul_add(rescaled.val[0], weight_low, bias_low); |
| int64x2x2_t result_1 = mul_add(rescaled.val[1], weight_high, bias_high); |
| |
| int32x4x2_t combined; |
| combined.val[0] = vcombine(vmovn(vrshrq_n_s64(result_0.val[0], 10)), vmovn(vrshrq_n_s64(result_0.val[1], 10))); |
| combined.val[1] = vcombine(vmovn(vrshrq_n_s64(result_1.val[0], 10)), vmovn(vrshrq_n_s64(result_1.val[1], 10))); |
| |
| int32x4x2_t out_val = multiply_by_quantized_multiplier_2row(combined, _output_multiplier, _output_shift + 12); |
| |
| vstore(output_ptr + x, vqmovn(out_val.val[0])); |
| vstore(output_ptr + x + 4, vqmovn(out_val.val[1])); |
| } |
| |
| for(; x < _window_end_x; ++x) |
| { |
| const auto val = static_cast<int32_t>(input_ptr[x]); |
| const int32_t shifted = (val << 10) - mean; |
| const int32_t rescaled = quantization::multiply_by_quantized_multiplier(shifted, inv_std_mul, inv_std_shift); |
| const int64_t weighted = rescaled * weight_ptr[x] + bias_ptr[x]; |
| const auto reverse_shifted = static_cast<int32_t>((weighted + 512) >> 10); |
| int32_t out_val = quantization::multiply_by_quantized_multiplier(reverse_shifted, _output_multiplier, _output_shift + 12); |
| out_val = utility::clamp<decltype(out_val), OutputDataType>(out_val, std::numeric_limits<OutputDataType>::min()); |
| output_ptr[x] = static_cast<OutputDataType>(out_val); |
| } |
| } |
| |
| void NEQLSTMLayerNormalizationKernel::compute_qsymm16() |
| { |
| using InputDataType = int16_t; |
| using OutputDataType = int16_t; |
| using BiasDataType = int32_t; |
| using AccType = int64_t; |
| |
| Iterator input_iterator{ _input, _inout_window }; |
| Iterator output_iterator{ _output, _inout_window }; |
| Iterator weight_iterator{ _weight, _weight_window }; |
| Iterator bias_iterator{ _bias, _weight_window }; |
| |
| const auto weight_ptr = reinterpret_cast<const InputDataType *>(weight_iterator.ptr()); |
| const auto bias_ptr = reinterpret_cast<const BiasDataType *>(bias_iterator.ptr()); |
| |
| const uint32_t column_size = _input->info()->tensor_shape()[0]; |
| |
| execute_window_loop(_inout_window, [ &, this](const Coordinates &) |
| { |
| const auto in_ptr = reinterpret_cast<const InputDataType *>(input_iterator.ptr()); |
| auto out_ptr = reinterpret_cast<OutputDataType *>(output_iterator.ptr()); |
| |
| AccType sum{ 0 }; |
| AccType sum_sq{ 0 }; |
| std::tie(sum, sum_sq) = sum_qsymm16(in_ptr); |
| |
| AccType mean{ 0 }; |
| AccType variance{ 0 }; |
| std::tie(mean, variance) = compute_mean_variance(sum, sum_sq, column_size); |
| |
| int32_t stddev_invsqrt_mul{}; |
| int32_t stddev_invsqrt_shift{}; |
| quantization::get_invsqrt_quantized_multiplier_exp(static_cast<int32_t>(variance), -1, stddev_invsqrt_mul, stddev_invsqrt_shift); |
| |
| normalize_qasymm16(in_ptr, out_ptr, weight_ptr, bias_ptr, mean, stddev_invsqrt_mul, stddev_invsqrt_shift); |
| }, |
| input_iterator, output_iterator); |
| } |
| } // namespace arm_compute |