| /* |
| * Copyright (c) 2017-2023 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 "tests/validation/Helpers.h" |
| #include "tests/framework/Asserts.h" |
| |
| #include <algorithm> |
| #include <cmath> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <> |
| SimpleTensor<float> convert_from_asymmetric(const SimpleTensor<uint8_t> &src) |
| { |
| const UniformQuantizationInfo &quantization_info = src.quantization_info().uniform(); |
| SimpleTensor<float> dst{ src.shape(), DataType::F32, 1, QuantizationInfo(), src.data_layout() }; |
| #if defined(_OPENMP) |
| #pragma omp parallel for |
| #endif /* _OPENMP */ |
| for(int i = 0; i < src.num_elements(); ++i) |
| { |
| dst[i] = dequantize_qasymm8(src[i], quantization_info); |
| } |
| return dst; |
| } |
| |
| template <> |
| SimpleTensor<float> convert_from_asymmetric(const SimpleTensor<int8_t> &src) |
| { |
| const UniformQuantizationInfo &quantization_info = src.quantization_info().uniform(); |
| SimpleTensor<float> dst{ src.shape(), DataType::F32, 1, QuantizationInfo(), src.data_layout() }; |
| |
| #if defined(_OPENMP) |
| #pragma omp parallel for |
| #endif /* _OPENMP */ |
| for(int i = 0; i < src.num_elements(); ++i) |
| { |
| dst[i] = dequantize_qasymm8_signed(src[i], quantization_info); |
| } |
| return dst; |
| } |
| |
| template <> |
| SimpleTensor<float> convert_from_asymmetric(const SimpleTensor<uint16_t> &src) |
| { |
| const UniformQuantizationInfo &quantization_info = src.quantization_info().uniform(); |
| SimpleTensor<float> dst{ src.shape(), DataType::F32, 1, QuantizationInfo(), src.data_layout() }; |
| |
| #if defined(_OPENMP) |
| #pragma omp parallel for |
| #endif /* _OPENMP */ |
| for(int i = 0; i < src.num_elements(); ++i) |
| { |
| dst[i] = dequantize_qasymm16(src[i], quantization_info); |
| } |
| return dst; |
| } |
| |
| template <> |
| SimpleTensor<uint8_t> convert_to_asymmetric(const SimpleTensor<float> &src, const QuantizationInfo &quantization_info) |
| { |
| SimpleTensor<uint8_t> dst{ src.shape(), DataType::QASYMM8, 1, quantization_info }; |
| const UniformQuantizationInfo &qinfo = quantization_info.uniform(); |
| |
| #if defined(_OPENMP) |
| #pragma omp parallel for |
| #endif /* _OPENMP */ |
| for(int i = 0; i < src.num_elements(); ++i) |
| { |
| dst[i] = quantize_qasymm8(src[i], qinfo); |
| } |
| return dst; |
| } |
| |
| template <> |
| SimpleTensor<int8_t> convert_to_asymmetric(const SimpleTensor<float> &src, const QuantizationInfo &quantization_info) |
| { |
| SimpleTensor<int8_t> dst{ src.shape(), DataType::QASYMM8_SIGNED, 1, quantization_info }; |
| const UniformQuantizationInfo &qinfo = quantization_info.uniform(); |
| |
| #if defined(_OPENMP) |
| #pragma omp parallel for |
| #endif /* _OPENMP */ |
| for(int i = 0; i < src.num_elements(); ++i) |
| { |
| dst[i] = quantize_qasymm8_signed(src[i], qinfo); |
| } |
| return dst; |
| } |
| |
| template <> |
| SimpleTensor<uint16_t> convert_to_asymmetric(const SimpleTensor<float> &src, const QuantizationInfo &quantization_info) |
| { |
| SimpleTensor<uint16_t> dst{ src.shape(), DataType::QASYMM16, 1, quantization_info }; |
| const UniformQuantizationInfo &qinfo = quantization_info.uniform(); |
| |
| #if defined(_OPENMP) |
| #pragma omp parallel for |
| #endif /* _OPENMP */ |
| for(int i = 0; i < src.num_elements(); ++i) |
| { |
| dst[i] = quantize_qasymm16(src[i], qinfo); |
| } |
| return dst; |
| } |
| |
| template <> |
| SimpleTensor<int16_t> convert_to_symmetric(const SimpleTensor<float> &src, const QuantizationInfo &quantization_info) |
| { |
| SimpleTensor<int16_t> dst{ src.shape(), DataType::QSYMM16, 1, quantization_info }; |
| const UniformQuantizationInfo &qinfo = quantization_info.uniform(); |
| |
| #if defined(_OPENMP) |
| #pragma omp parallel for |
| #endif /* _OPENMP */ |
| for(int i = 0; i < src.num_elements(); ++i) |
| { |
| dst[i] = quantize_qsymm16(src[i], qinfo); |
| } |
| return dst; |
| } |
| |
| template <> |
| SimpleTensor<float> convert_from_symmetric(const SimpleTensor<int16_t> &src) |
| { |
| const UniformQuantizationInfo &quantization_info = src.quantization_info().uniform(); |
| SimpleTensor<float> dst{ src.shape(), DataType::F32, 1, QuantizationInfo(), src.data_layout() }; |
| |
| #if defined(_OPENMP) |
| #pragma omp parallel for |
| #endif /* _OPENMP */ |
| for(int i = 0; i < src.num_elements(); ++i) |
| { |
| dst[i] = dequantize_qsymm16(src[i], quantization_info); |
| } |
| return dst; |
| } |
| |
| template <typename T> |
| void matrix_multiply(const SimpleTensor<T> &a, const SimpleTensor<T> &b, SimpleTensor<T> &out) |
| { |
| ARM_COMPUTE_ERROR_ON(a.shape()[0] != b.shape()[1]); |
| ARM_COMPUTE_ERROR_ON(a.shape()[1] != out.shape()[1]); |
| ARM_COMPUTE_ERROR_ON(b.shape()[0] != out.shape()[0]); |
| |
| const int M = a.shape()[1]; // Rows |
| const int N = b.shape()[0]; // Cols |
| const int K = b.shape()[1]; |
| |
| #if defined(_OPENMP) |
| #pragma omp parallel for collapse(2) |
| #endif /* _OPENMP */ |
| for(int y = 0; y < M; ++y) |
| { |
| for(int x = 0; x < N; ++x) |
| { |
| float acc = 0.0f; |
| for(int k = 0; k < K; ++k) |
| { |
| acc += a[y * K + k] * b[x + k * N]; |
| } |
| |
| out[x + y * N] = acc; |
| } |
| } |
| } |
| |
| template <typename T> |
| void transpose_matrix(const SimpleTensor<T> &in, SimpleTensor<T> &out) |
| { |
| ARM_COMPUTE_ERROR_ON((in.shape()[0] != out.shape()[1]) || (in.shape()[1] != out.shape()[0])); |
| |
| const int width = in.shape()[0]; |
| const int height = in.shape()[1]; |
| |
| #if defined(_OPENMP) |
| #pragma omp parallel for collapse(2) |
| #endif /* _OPENMP */ |
| for(int y = 0; y < height; ++y) |
| { |
| for(int x = 0; x < width; ++x) |
| { |
| const T val = in[x + y * width]; |
| |
| out[x * height + y] = val; |
| } |
| } |
| } |
| |
| template <typename T> |
| void get_tile(const SimpleTensor<T> &in, SimpleTensor<T> &tile, const Coordinates &coord) |
| { |
| ARM_COMPUTE_ERROR_ON(tile.shape().num_dimensions() > 2); |
| |
| const int w_tile = tile.shape()[0]; |
| const int h_tile = tile.shape()[1]; |
| |
| // Fill the tile with zeros |
| std::fill(tile.data() + 0, (tile.data() + (w_tile * h_tile)), static_cast<T>(0)); |
| |
| // Check if with the dimensions greater than 2 we could have out-of-bound reads |
| for(size_t d = 2; d < Coordinates::num_max_dimensions; ++d) |
| { |
| if(coord[d] < 0 || coord[d] >= static_cast<int>(in.shape()[d])) |
| { |
| ARM_COMPUTE_ERROR("coord[d] < 0 || coord[d] >= in.shape()[d] with d >= 2"); |
| } |
| } |
| |
| // Since we could have out-of-bound reads along the X and Y dimensions, |
| // we start calculating the input address with x = 0 and y = 0 |
| Coordinates start_coord = coord; |
| start_coord[0] = 0; |
| start_coord[1] = 0; |
| |
| // Get input and roi pointers |
| auto in_ptr = static_cast<const T *>(in(start_coord)); |
| auto roi_ptr = static_cast<T *>(tile.data()); |
| |
| const int x_in_start = std::max(0, coord[0]); |
| const int y_in_start = std::max(0, coord[1]); |
| const int x_in_end = std::min(static_cast<int>(in.shape()[0]), coord[0] + w_tile); |
| const int y_in_end = std::min(static_cast<int>(in.shape()[1]), coord[1] + h_tile); |
| |
| // Number of elements to copy per row |
| const int n = x_in_end - x_in_start; |
| |
| // Starting coordinates for the ROI |
| const int x_tile_start = coord[0] > 0 ? 0 : std::abs(coord[0]); |
| const int y_tile_start = coord[1] > 0 ? 0 : std::abs(coord[1]); |
| |
| // Update input pointer |
| in_ptr += x_in_start; |
| in_ptr += (y_in_start * in.shape()[0]); |
| |
| // Update ROI pointer |
| roi_ptr += x_tile_start; |
| roi_ptr += (y_tile_start * tile.shape()[0]); |
| |
| for(int y = y_in_start; y < y_in_end; ++y) |
| { |
| // Copy per row |
| std::copy(in_ptr, in_ptr + n, roi_ptr); |
| |
| in_ptr += in.shape()[0]; |
| roi_ptr += tile.shape()[0]; |
| } |
| } |
| |
| template <typename T> |
| void zeros(SimpleTensor<T> &in, const Coordinates &anchor, const TensorShape &shape) |
| { |
| ARM_COMPUTE_ERROR_ON(anchor.num_dimensions() != shape.num_dimensions()); |
| ARM_COMPUTE_ERROR_ON(in.shape().num_dimensions() > 2); |
| ARM_COMPUTE_ERROR_ON(shape.num_dimensions() > 2); |
| |
| // Check if with the dimensions greater than 2 we could have out-of-bound reads |
| for(size_t d = 0; d < Coordinates::num_max_dimensions; ++d) |
| { |
| if(anchor[d] < 0 || ((anchor[d] + shape[d]) > in.shape()[d])) |
| { |
| ARM_COMPUTE_ERROR("anchor[d] < 0 || (anchor[d] + shape[d]) > in.shape()[d]"); |
| } |
| } |
| |
| // Get input pointer |
| auto in_ptr = static_cast<T *>(in(anchor[0] + anchor[1] * in.shape()[0])); |
| |
| const unsigned int n = in.shape()[0]; |
| |
| for(unsigned int y = 0; y < shape[1]; ++y) |
| { |
| std::fill(in_ptr, in_ptr + shape[0], 0); |
| in_ptr += n; |
| } |
| } |
| |
| std::pair<int, int> get_quantized_bounds(const QuantizationInfo &quant_info, float min, float max) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(min > max, "min must be lower equal than max"); |
| |
| const int min_bound = quantize_qasymm8(min, quant_info.uniform()); |
| const int max_bound = quantize_qasymm8(max, quant_info.uniform()); |
| return std::pair<int, int> { min_bound, max_bound }; |
| } |
| |
| std::pair<int, int> get_quantized_qasymm8_signed_bounds(const QuantizationInfo &quant_info, float min, float max) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(min > max, "min must be lower equal than max"); |
| |
| const int min_bound = quantize_qasymm8_signed(min, quant_info.uniform()); |
| const int max_bound = quantize_qasymm8_signed(max, quant_info.uniform()); |
| return std::pair<int, int> { min_bound, max_bound }; |
| } |
| |
| std::pair<int, int> get_symm_quantized_per_channel_bounds(const QuantizationInfo &quant_info, float min, float max, size_t channel_id) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(min > max, "min must be lower equal than max"); |
| |
| const int min_bound = quantize_qsymm8_per_channel(min, quant_info, channel_id); |
| const int max_bound = quantize_qsymm8_per_channel(max, quant_info, channel_id); |
| return std::pair<int, int> { min_bound, max_bound }; |
| } |
| |
| void add_padding_x(std::initializer_list<ITensor *> tensors, const DataLayout &data_layout, bool only_right_pad) |
| { |
| if(data_layout == DataLayout::NHWC) |
| { |
| constexpr unsigned int lower = 1U; |
| constexpr unsigned int upper = 16U; |
| |
| std::uniform_int_distribution<unsigned int> distribution(lower, upper); |
| size_t seed_offset = 0; |
| |
| for(ITensor *tensor : tensors) |
| { |
| ARM_COMPUTE_ERROR_ON(!tensor->info()->is_resizable()); |
| |
| std::mt19937 gen(library->seed() + seed_offset++); |
| |
| const unsigned int right = distribution(gen); |
| const unsigned int left = only_right_pad ? 0 : distribution(gen); |
| |
| tensor->info()->extend_padding(PaddingSize(0U, right, 0U, left)); |
| } |
| } |
| } |
| |
| void add_padding_y(std::initializer_list<ITensor *> tensors, const DataLayout &data_layout) |
| { |
| if(data_layout == DataLayout::NHWC) |
| { |
| constexpr unsigned int lower = 1U; |
| constexpr unsigned int upper = 4U; |
| |
| std::uniform_int_distribution<unsigned int> distribution(lower, upper); |
| size_t seed_offset = 0; |
| |
| for(ITensor *tensor : tensors) |
| { |
| ARM_COMPUTE_ERROR_ON(!tensor->info()->is_resizable()); |
| |
| std::mt19937 gen(library->seed() + seed_offset++); |
| |
| const unsigned int top = distribution(gen); |
| const unsigned int bottom = distribution(gen); |
| |
| tensor->info()->extend_padding(PaddingSize(top, 0U, bottom, 0U)); |
| } |
| } |
| } |
| |
| QuantizationInfo calculate_mat_mul_dst_q_info(const QuantizationInfo &a_q_info, const QuantizationInfo &b_q_info, int m, int n, int k, DataType data_type) |
| { |
| ARM_COMPUTE_UNUSED(m, n); |
| QuantizationInfo c_q_info; |
| |
| ARM_COMPUTE_ASSERT(data_type == DataType::QASYMM8 || data_type == DataType::QASYMM8_SIGNED); |
| |
| const int32_t t_max = static_cast<int32_t>(data_type == DataType::QASYMM8 ? std::numeric_limits<uint8_t>::max() : std::numeric_limits<int8_t>::max()); |
| const int32_t t_min = static_cast<int32_t>(data_type == DataType::QASYMM8 ? std::numeric_limits<uint8_t>::min() : std::numeric_limits<int8_t>::min()); |
| |
| /** Quantization Setup of matrix multiplication |
| * |
| * We have a matrix multiplication of the form C = A * B |
| * where A is (M X K), B is (K x N) and C is therefore (M x N). |
| * |
| * If we have some distributions statistics of A and B, i.e. mean and variance, |
| * we can estimate the mean and variance of a single value in C matrix and |
| * pick good scale and offset values for the output and have non-saturated tests. |
| * |
| * Each element in the output matrix can be calculated as follows: |
| * C_ij = sum_k(A_ik * B_kj) |
| * |
| * All values are float above. |
| * |
| * Note: All possible A_ik, B_kj random variables are assumed mutually independent. |
| * |
| * Terminology: |
| * E[X]: Mean of the random variable X (sometimes referred as mu_x) |
| * var(X): Variance of the random variable X (someimes referred as sigma^2_x) |
| * std(X): sqrt(var(X)), standard deviation of X |
| * |
| * 1) Calculate the mean: |
| * E[C_ij] = sum_k( E[A_ik] * E[B_kj] ) = K * mean_a * mean_b |
| * |
| * Since elements of A and B are uniformly distributed random variables, we have |
| * mean_a = (max_a + min_a) / 2, mean_b = (max_b + min_b ) / 2 |
| * max_a and min_a can be calculated with the scale_a/b and offset_a/b |
| * by replacing data type minimum and maximums in the equations |
| * |
| * 2) Calculate the variance: |
| * var(C_ij) = sum_k( var(A_ik * B_kj) ) |
| * = sum_k ( E[A_ik^2 * B_kj^2] - E[A_ik]^2E[B_kj^2] ) |
| * = ... |
| * = K * (var_a * var_b + var_a * mean^2_b + var_b * mean^2_a) |
| * |
| * Similarly, due to uniform random variable properties, we have |
| * var_a = (max_a - min_a)^2 / 12 |
| * var_b = (max_b - min_b)^2 / 12 |
| * |
| * |
| * 3) Now, we have an idea of what would an average C_ij will like and how much deviation |
| * is present around it. The exact distribution of C is not easy to come up with dependent on K. |
| * But, as K increases, due to Central Limit Theorem, it'll look more like a bell shaped figure, |
| * approaching normal distribution. |
| * |
| * This is useful because, in normal distribution, we know that values +- 2 std_deviation around |
| * the mean constitute 95% of the values. Therefore, setting a plausible range for us: |
| * C_range = [C_min, C_max] = [mean_c - 2 * std_c, mean_c + 2 * std_c] |
| * |
| * 4) |
| * If we map this [C_min, C_max] to [0, 255] or [-128, 127] depending on the signedness of the |
| * data type, we can find a suitable scale and offset for the output. On average, it's expected |
| * that 5% of the output values will saturate and 95% will remain in the range. |
| * |
| * The equations to be solved for offset_c and scale_c are: |
| * C_min = scale_c * (type_min - offset_c) |
| * C_max = scale_c * (type_max - offset_c) |
| */ |
| |
| const int32_t a_offset = a_q_info.uniform().offset; |
| const float a_scale = a_q_info.uniform().scale; |
| const int32_t b_offset = b_q_info.uniform().offset; |
| const float b_scale = b_q_info.uniform().scale; |
| |
| // Lhs/A stats |
| const float max_a = (t_max - a_offset) * a_scale; |
| const float min_a = (t_min - a_offset) * a_scale; |
| const float mean_a = (max_a + min_a) / 2; |
| const float var_a = (max_a - min_a) * (max_a - min_a) / 12; |
| |
| // Rhs/B stats |
| const float max_b = (t_max - b_offset) * b_scale; |
| const float min_b = (t_min - b_offset) * b_scale; |
| const float mean_b = (max_b + min_b) / 2; |
| const float var_b = (max_b - min_b) * (max_b - min_b) / 12; |
| |
| // Output stats |
| const float mean_out = k * mean_a * mean_b; |
| const float var_out = k * (var_a * var_b + var_a * mean_b * mean_b + var_b * mean_a * mean_a); |
| const float std_out = sqrt(var_out); |
| |
| // Output quantization setup |
| const float scale_out = 4 * std_out / 255; |
| const int32_t offset_out = static_cast<int32_t>(t_min - (mean_out - 2.f * std_out) / scale_out); |
| |
| c_q_info = QuantizationInfo(scale_out, offset_out); |
| return c_q_info; |
| } |
| |
| template void get_tile(const SimpleTensor<float> &in, SimpleTensor<float> &roi, const Coordinates &coord); |
| template void get_tile(const SimpleTensor<half> &in, SimpleTensor<half> &roi, const Coordinates &coord); |
| template void get_tile(const SimpleTensor<int> &in, SimpleTensor<int> &roi, const Coordinates &coord); |
| template void get_tile(const SimpleTensor<short> &in, SimpleTensor<short> &roi, const Coordinates &coord); |
| template void get_tile(const SimpleTensor<char> &in, SimpleTensor<char> &roi, const Coordinates &coord); |
| template void zeros(SimpleTensor<float> &in, const Coordinates &anchor, const TensorShape &shape); |
| template void zeros(SimpleTensor<half> &in, const Coordinates &anchor, const TensorShape &shape); |
| template void transpose_matrix(const SimpleTensor<float> &in, SimpleTensor<float> &out); |
| template void transpose_matrix(const SimpleTensor<half> &in, SimpleTensor<half> &out); |
| template void transpose_matrix(const SimpleTensor<int> &in, SimpleTensor<int> &out); |
| template void transpose_matrix(const SimpleTensor<short> &in, SimpleTensor<short> &out); |
| template void transpose_matrix(const SimpleTensor<char> &in, SimpleTensor<char> &out); |
| template void transpose_matrix(const SimpleTensor<int8_t> &in, SimpleTensor<int8_t> &out); |
| template void transpose_matrix(const SimpleTensor<uint8_t> &in, SimpleTensor<uint8_t> &out); |
| template void matrix_multiply(const SimpleTensor<float> &a, const SimpleTensor<float> &b, SimpleTensor<float> &out); |
| template void matrix_multiply(const SimpleTensor<half> &a, const SimpleTensor<half> &b, SimpleTensor<half> &out); |
| |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |