/* * Copyright (c) 2017-2023,2024 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. */ #ifndef ACL_TESTS_VALIDATION_HELPERS_H #define ACL_TESTS_VALIDATION_HELPERS_H #include "arm_compute/core/Types.h" #include "arm_compute/core/Utils.h" #include "arm_compute/function_info/ActivationLayerInfo.h" #include "support/Half.h" #include "tests/Globals.h" #include "tests/SimpleTensor.h" #include #include #include #include #include namespace arm_compute { namespace test { namespace validation { template struct is_floating_point : public std::is_floating_point { }; template <> struct is_floating_point : public std::true_type { }; template <> struct is_floating_point : public std::true_type { }; /** Helper struct to store the hints for * - destination quantization info * - minimum bias value * - maximum bias value * in quantized test construction. */ struct QuantizationHint { QuantizationInfo q_info; int32_t bias_min; int32_t bias_max; }; /** Helper function to get the testing range for each activation layer. * * @param[in] activation Activation function to test. * @param[in] data_type Data type. * * @return A pair containing the lower upper testing bounds for a given function. */ template std::pair get_activation_layer_test_bounds(ActivationLayerInfo::ActivationFunction activation, DataType data_type) { std::pair bounds; switch (data_type) { case DataType::F16: { using namespace half_float::literal; switch (activation) { case ActivationLayerInfo::ActivationFunction::TANH: case ActivationLayerInfo::ActivationFunction::SQUARE: case ActivationLayerInfo::ActivationFunction::LOGISTIC: case ActivationLayerInfo::ActivationFunction::SOFT_RELU: // Reduce range as exponent overflows bounds = std::make_pair(-2._h, 2._h); break; case ActivationLayerInfo::ActivationFunction::SQRT: // Reduce range as sqrt should take a non-negative number bounds = std::make_pair(0._h, 128._h); break; default: bounds = std::make_pair(-255._h, 255._h); break; } break; } case DataType::F32: switch (activation) { case ActivationLayerInfo::ActivationFunction::SOFT_RELU: // Reduce range as exponent overflows bounds = std::make_pair(-40.f, 40.f); break; case ActivationLayerInfo::ActivationFunction::SQRT: // Reduce range as sqrt should take a non-negative number bounds = std::make_pair(0.f, 255.f); break; default: bounds = std::make_pair(-255.f, 255.f); break; } break; default: ARM_COMPUTE_ERROR("Unsupported data type"); } return bounds; } /** Convert an asymmetric quantized simple tensor into float using tensor quantization information. * * @param[in] src Quantized tensor. * * @return Float tensor. */ template SimpleTensor convert_from_asymmetric(const SimpleTensor &src); /** Convert float simple tensor into quantized using specified quantization information. * * @param[in] src Float tensor. * @param[in] quantization_info Quantification information. * * \relates arm_compute::test::SimpleTensor * @return Quantized tensor. */ template SimpleTensor convert_to_asymmetric(const SimpleTensor &src, const QuantizationInfo &quantization_info); /** Convert quantized simple tensor into float using tensor quantization information. * * @param[in] src Quantized tensor. * * @return Float tensor. */ template SimpleTensor convert_from_symmetric(const SimpleTensor &src); /** Convert float simple tensor into quantized using specified quantization information. * * @param[in] src Float tensor. * @param[in] quantization_info Quantification information. * \relates arm_compute::test::SimpleTensor * @return Quantized tensor. */ template SimpleTensor convert_to_symmetric(const SimpleTensor &src, const QuantizationInfo &quantization_info); /** Matrix multiply between 2 float simple tensors * * @param[in] a Input tensor A * @param[in] b Input tensor B * @param[out] out Output tensor * */ template void matrix_multiply(const SimpleTensor &a, const SimpleTensor &b, SimpleTensor &out); /** Transpose matrix * * @param[in] in Input tensor * @param[out] out Output tensor * */ template void transpose_matrix(const SimpleTensor &in, SimpleTensor &out); /** Get a 2D tile from a tensor * * @note In case of out-of-bound reads, the tile will be filled with zeros * * @param[in] in Input tensor * @param[out] tile Tile * @param[in] coord Coordinates */ template void get_tile(const SimpleTensor &in, SimpleTensor &tile, const Coordinates &coord); /** Fill with zeros the input tensor in the area defined by anchor and shape * * @param[in] in Input tensor to fill with zeros * @param[out] anchor Starting point of the zeros area * @param[in] shape Ending point of the zeros area */ template void zeros(SimpleTensor &in, const Coordinates &anchor, const TensorShape &shape); /** Helper function to compute quantized min and max bounds * * @param[in] quant_info Quantization info to be used for conversion * @param[in] min Floating point minimum value to be quantized * @param[in] max Floating point maximum value to be quantized */ std::pair get_quantized_bounds(const QuantizationInfo &quant_info, float min, float max); /** Helper function to compute asymmetric quantized signed min and max bounds * * @param[in] quant_info Quantization info to be used for conversion * @param[in] min Floating point minimum value to be quantized * @param[in] max Floating point maximum value to be quantized */ std::pair get_quantized_qasymm8_signed_bounds(const QuantizationInfo &quant_info, float min, float max); /** Helper function to compute symmetric quantized min and max bounds * * @param[in] quant_info Quantization info to be used for conversion * @param[in] min Floating point minimum value to be quantized * @param[in] max Floating point maximum value to be quantized * @param[in] channel_id Channel id for per channel quantization info. */ std::pair get_symm_quantized_per_channel_bounds(const QuantizationInfo &quant_info, float min, float max, size_t channel_id = 0); /** Add random padding along the X axis (between 1 and 16 columns per side) to all the input tensors. * This is used in our validation suite in order to simulate implicit padding addition after configuring, but before allocating. * * @param[in] tensors List of tensors to add padding to * @param[in] data_layout (Optional) Data layout of the operator * @param[in] only_right_pad (Optional) Only right padding testing, in case of cl image padding * * @note This function adds padding to the input tensors only if data_layout == DataLayout::NHWC */ void add_padding_x(std::initializer_list tensors, const DataLayout &data_layout = DataLayout::NHWC, bool only_right_pad = false); /** For 2d convolution, given the Lhs/Rhs matrix quantization informations and the convolution dimension, * calculate a suitable output quantization and suggested bias range for obtaining non-saturated outputs with high probability. * * @param[in] in_q_info Input matrix quantization info * @param[in] weight_q_info Weights matrix quantization info * @param[in] height Height of the weights tensor * @param[in] width Width of the weights tensors * @param[in] channels Number of input channels * @param[in] data_type data type, only QASYMM8, QASYMM8_SIGNED are supported * @param[in] bias_fraction see @ref suggest_mac_dst_q_info_and_bias() for explanation * * @return QuantizationHint object containing the suggested output quantization info and min/max bias range */ QuantizationHint suggest_conv_dst_q_info_and_bias(const QuantizationInfo &in_q_info, const QuantizationInfo &weight_q_info, int32_t height, int32_t width, int32_t channels, DataType data_type, float bias_fraction); /** For a matrix multiplication, given the Lhs/Rhs matrix quantization informations and the matrix multiplication dimensions, * calculate a suitable output quantization and suggested bias range for obtaining non-saturated outputs with high probability. * * @param[in] lhs_q_info Lhs matrix quantization info * @param[in] rhs_q_info Rhs matrix quantization info * @param[in] m Number of rows of Lhs matrix * @param[in] n Number of columns of Rhs Matrix * @param[in] k Number of rows/columns of Rhs/Lhs Matrix * @param[in] data_type data type, only QASYMM8, QASYMM8_SIGNED are supported * @param[in] bias_fraction see @ref suggest_mac_dst_q_info_and_bias() for explanation * * @return QuantizationHint object containing the suggested output quantization info and min/max bias range */ QuantizationHint suggest_matmul_dst_q_info_and_bias(const QuantizationInfo &lhs_q_info, const QuantizationInfo &rhs_q_info, int32_t m, int32_t n, int32_t k, DataType data_type, float bias_fraction); /** For a multiply-accumulate (mac), given the Lhs/Rhs vector quantization informations and the dot product dimensions, * calculate a suitable output quantization and suggested bias range for obtaining non-saturated outputs with high probability. * * @param[in] lhs_q_info Lhs matrix quantization info * @param[in] rhs_q_info Rhs matrix quantization info * @param[in] k number of accumulations taking place in the sum, i.e. c_k = sum_k(a_k * b_k) * @param[in] data_type data type, only QASYMM8, QASYMM8_SIGNED are supported * @param[in] bias_fraction the fraction of bias amplitude compared to integer accummulation. * @param[in] num_sd (Optional) number of standard deviations we allow from the mean. Default value is 2. * * @return QuantizationHint object containing the suggested output quantization info and min/max bias range */ QuantizationHint suggest_mac_dst_q_info_and_bias(const QuantizationInfo &lhs_q_info, const QuantizationInfo &rhs_q_info, int32_t k, DataType data_type, float bias_fraction, int num_sd = 2); } // namespace validation } // namespace test } // namespace arm_compute #endif // ACL_TESTS_VALIDATION_HELPERS_H