| /* |
| * Copyright (c) 2017-2019 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 "GEMMLowp.h" |
| |
| #include "arm_compute/core/Types.h" |
| #include "tests/validation/reference/UtilsQuantizedAsymm.h" |
| |
| #include <limits> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace reference |
| { |
| namespace |
| { |
| template <typename T> |
| struct DataTypeExtractor |
| { |
| static DataType data_type() |
| { |
| DataType data_type = DataType::UNKNOWN; |
| if(std::is_same<T, int8_t>::value) |
| { |
| data_type = DataType::QASYMM8_SIGNED; |
| } |
| else if(std::is_same<T, uint8_t>::value) |
| { |
| data_type = DataType::QASYMM8; |
| } |
| else if(std::is_same<T, int16_t>::value) |
| { |
| data_type = DataType::QSYMM16; |
| } |
| return data_type; |
| } |
| }; |
| |
| template <typename T> |
| void quantize_down_int32_to_uint8_scale(const SimpleTensor<T> *in, const SimpleTensor<T> *bias, SimpleTensor<uint8_t> *dst, int32_t result_offset, std::vector<int32_t> result_mult_int, |
| std::vector<int32_t> result_shift, int32_t min, int32_t max) |
| { |
| const int cols_in = in->shape().x(); |
| const bool is_per_channel = result_mult_int.size() > 1; |
| |
| for(int i = 0; i < in->num_elements(); ++i) |
| { |
| int32_t result = ((*in)[i] + result_offset); |
| |
| if(bias != nullptr) |
| { |
| result += (*bias)[i % cols_in]; |
| } |
| |
| result *= (is_per_channel) ? result_mult_int[i % cols_in] : result_mult_int[0]; |
| |
| result >>= (is_per_channel) ? result_shift[i % cols_in] : result_shift[0]; |
| |
| // Bounded ReLu |
| if(min != max) |
| { |
| result = std::max(min, std::min(max, result)); |
| } |
| |
| (*dst)[i] = static_cast<uint8_t>(std::max(0, std::min(255, result))); |
| } |
| } |
| |
| template <typename TIn, typename TOut> |
| void quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> *in, const SimpleTensor<TIn> *bias, SimpleTensor<TOut> *dst, std::vector<int32_t> result_fixedpoint_multiplier, |
| std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max) |
| { |
| const int cols_in = in->shape().x(); |
| const bool is_per_channel = result_fixedpoint_multiplier.size() > 1; |
| |
| for(int i = 0; i < in->num_elements(); ++i) |
| { |
| TIn result = (*in)[i]; |
| |
| if(bias != nullptr) |
| { |
| result += (*bias)[i % cols_in]; |
| } |
| |
| // Fixed point multiplication |
| const int32_t multiplier = (is_per_channel) ? result_fixedpoint_multiplier[i % cols_in] : result_fixedpoint_multiplier[0]; |
| const int32_t shift = (is_per_channel) ? result_shift[i % cols_in] : result_shift[0]; |
| |
| if(shift < 0) |
| { |
| result = asymm_int_mult(result * (1 << (-shift)), multiplier); |
| } |
| else |
| { |
| result = asymm_rounding_divide_by_pow2(asymm_int_mult(result, multiplier), shift); |
| } |
| result += result_offset_after_shift; |
| |
| // Bounded ReLu |
| if(min != max) |
| { |
| result = std::max(min, std::min(max, result)); |
| } |
| |
| (*dst)[i] = static_cast<TOut>(std::max<TIn>(std::numeric_limits<TOut>::lowest(), |
| std::min<TIn>(std::numeric_limits<TOut>::max(), result))); |
| } |
| } |
| } // namespace |
| |
| template <typename T_out, typename T_in, typename T_in_1> |
| SimpleTensor<T_out> gemmlowp_matrix_multiply_core(const SimpleTensor<T_in> &a, const SimpleTensor<T_in_1> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset) |
| { |
| static_assert(std::is_same<typename std::decay<T_out>::type, int32_t>::value, "Only int32_t is allowed for the output"); |
| |
| DataType dt = std::is_same<T_out, int32_t>::value ? DataType::S32 : DataType::U32; |
| SimpleTensor<T_out> c(shape_c, dt); |
| |
| const int K = a.shape().x(); |
| const int M = a.shape().y(); |
| const int N = b.shape().x(); |
| const int D = a.shape().z(); // Number of matrices in a batch |
| |
| const int a_stride_z = K * M; |
| // Do not slide the matrix B along the 3rd dimension in case matrix B has less than 3 dimensions |
| const int b_stride_z = b.shape().num_dimensions() > 2 ? N * K : 0; |
| const int c_stride_z = N * M; |
| |
| std::vector<T_out> acc; |
| acc.resize(N); |
| |
| for(int depth = 0; depth < D; ++depth) |
| { |
| const int base_addr_a = depth * a_stride_z; |
| const int base_addr_b = depth * b_stride_z; |
| const int base_addr_c = depth * c_stride_z; |
| |
| for(int i = 0; i < M; ++i) |
| { |
| for(int j = 0; j < N; ++j) |
| { |
| acc[j] = 0; |
| } |
| for(int k = 0; k < K; ++k) |
| { |
| const T_out tmp_a = a_offset + static_cast<T_out>(a[base_addr_a + k + i * K]); |
| for(int j = 0; j < N; ++j) |
| { |
| const T_out tmp_b = b_offset + static_cast<T_out>(b[base_addr_b + j + k * N]); |
| const T_out mult_as_int = tmp_a * tmp_b; |
| acc[j] += mult_as_int; |
| } |
| } |
| for(int j = 0; j < N; ++j) |
| { |
| c[base_addr_c + j + i * N] = acc[j]; |
| } |
| } |
| } |
| |
| return c; |
| } |
| |
| // used to validate assembly kernels which don't know anything about offsets |
| template <typename T1, typename T2, typename T3> |
| SimpleTensor<T1> gemmlowp(const SimpleTensor<T2> &a, const SimpleTensor<T3> &b, TensorShape shape_c) |
| { |
| return gemmlowp_matrix_multiply_core<T1, T2, T3>(a, b, shape_c, 0, 0); |
| } |
| |
| template <typename T> |
| SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, int32_t result_offset, std::vector<int32_t> result_mult_int, std::vector<int32_t> result_shift, |
| int32_t min, int32_t max) |
| { |
| SimpleTensor<uint8_t> dst(in.shape(), DataType::QASYMM8); |
| |
| quantize_down_int32_to_uint8_scale<T>(&in, nullptr, &dst, result_offset, result_mult_int, result_shift, min, max); |
| |
| return dst; |
| } |
| |
| template <typename T> |
| SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, int32_t result_offset, std::vector<int32_t> result_mult_int, |
| std::vector<int32_t> result_shift, int32_t min, int32_t max) |
| { |
| SimpleTensor<uint8_t> dst(in.shape(), DataType::QASYMM8); |
| |
| quantize_down_int32_to_uint8_scale<T>(&in, &bias, &dst, result_offset, result_mult_int, result_shift, min, max); |
| |
| return dst; |
| } |
| |
| template <typename TIn, typename TOut> |
| SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, std::vector<int32_t> result_fixedpoint_multiplier, std::vector<int32_t> result_shift, |
| int32_t result_offset_after_shift, int32_t min, int32_t max) |
| { |
| SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type()); |
| |
| quantize_down_scale_by_fixedpoint<TIn, TOut>(&in, nullptr, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max); |
| |
| return dst; |
| } |
| |
| template <typename TIn, typename TOut> |
| SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias, std::vector<int32_t> result_fixedpoint_multiplier, |
| std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max) |
| { |
| SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type()); |
| |
| quantize_down_scale_by_fixedpoint<TIn, TOut>(&in, &bias, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max); |
| |
| return dst; |
| } |
| |
| template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier, |
| std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); |
| template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, |
| std::vector<int32_t> result_fixedpoint_multiplier, |
| std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); |
| template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier, |
| std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); |
| template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, |
| std::vector<int32_t> result_fixedpoint_multiplier, |
| std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); |
| template SimpleTensor<int16_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier, |
| std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); |
| template SimpleTensor<int16_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, |
| std::vector<int32_t> result_fixedpoint_multiplier, |
| std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); |
| template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<int32_t> &a, int32_t result_offset, std::vector<int32_t> result_mult_int, |
| std::vector<int32_t> result_shift, int32_t min, int32_t max); |
| template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, int32_t result_offset, std::vector<int32_t> result_mult_int, |
| std::vector<int32_t> result_shift, int32_t min, int32_t max); |
| template SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset); |
| template SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset); |
| template SimpleTensor<int32_t> gemmlowp<int32_t, int8_t, int8_t>(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c); |
| template SimpleTensor<int32_t> gemmlowp<int32_t, uint8_t, uint8_t>(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, TensorShape shape_c); |
| template SimpleTensor<int32_t> gemmlowp<int32_t, uint8_t, int8_t>(const SimpleTensor<uint8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c); |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |