diff options
Diffstat (limited to 'tests/validation/Helpers.h')
| -rw-r--r-- | tests/validation/Helpers.h | 181 |
1 files changed, 106 insertions, 75 deletions
diff --git a/tests/validation/Helpers.h b/tests/validation/Helpers.h index 325cc0042e..e044620556 100644 --- a/tests/validation/Helpers.h +++ b/tests/validation/Helpers.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020 Arm Limited. + * Copyright (c) 2017-2023,2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,15 +21,19 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_TEST_VALIDATION_HELPERS_H -#define ARM_COMPUTE_TEST_VALIDATION_HELPERS_H +#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 <cmath> +#include <cstdint> #include <random> #include <type_traits> #include <utility> @@ -49,6 +53,23 @@ template <> struct is_floating_point<half> : public std::true_type { }; +template <> +struct is_floating_point<bfloat16> : 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. * @@ -62,13 +83,13 @@ std::pair<T, T> get_activation_layer_test_bounds(ActivationLayerInfo::Activation { std::pair<T, T> bounds; - switch(data_type) + switch (data_type) { case DataType::F16: { using namespace half_float::literal; - switch(activation) + switch (activation) { case ActivationLayerInfo::ActivationFunction::TANH: case ActivationLayerInfo::ActivationFunction::SQUARE: @@ -88,7 +109,7 @@ std::pair<T, T> get_activation_layer_test_bounds(ActivationLayerInfo::Activation break; } case DataType::F32: - switch(activation) + switch (activation) { case ActivationLayerInfo::ActivationFunction::SOFT_RELU: // Reduce range as exponent overflows @@ -110,72 +131,6 @@ std::pair<T, T> get_activation_layer_test_bounds(ActivationLayerInfo::Activation return bounds; } -/** Fill mask with the corresponding given pattern. - * - * @param[in,out] mask Mask to be filled according to pattern - * @param[in] cols Columns (width) of mask - * @param[in] rows Rows (height) of mask - * @param[in] pattern Pattern to fill the mask according to - */ -void fill_mask_from_pattern(uint8_t *mask, int cols, int rows, MatrixPattern pattern); - -/** Calculate output tensor shape give a vector of input tensor to concatenate - * - * @param[in] input_shapes Shapes of the tensors to concatenate across depth. - * - * @return The shape of output concatenated tensor. - */ -TensorShape calculate_depth_concatenate_shape(const std::vector<TensorShape> &input_shapes); - -/** Calculate output tensor shape for the concatenate operation along a given axis - * - * @param[in] input_shapes Shapes of the tensors to concatenate across width. - * @param[in] axis Axis to use for the concatenate operation - * - * @return The shape of output concatenated tensor. - */ -TensorShape calculate_concatenate_shape(const std::vector<TensorShape> &input_shapes, size_t axis); - -/** Parameters of Harris Corners algorithm. */ -struct HarrisCornersParameters -{ - float threshold{ 0.f }; /**< Threshold */ - float sensitivity{ 0.f }; /**< Sensitivity */ - float min_dist{ 0.f }; /**< Minimum distance */ - uint8_t constant_border_value{ 0 }; /**< Border value */ -}; - -/** Generate parameters for Harris Corners algorithm. */ -HarrisCornersParameters harris_corners_parameters(); - -/** Parameters of Canny edge algorithm. */ -struct CannyEdgeParameters -{ - int32_t upper_thresh{ 255 }; - int32_t lower_thresh{ 0 }; - uint8_t constant_border_value{ 0 }; -}; - -/** Generate parameters for Canny edge algorithm. */ -CannyEdgeParameters canny_edge_parameters(); - -/** Helper function to fill the Lut random by a ILutAccessor. - * - * @param[in,out] table Accessor at the Lut. - * - */ -template <typename T> -void fill_lookuptable(T &&table) -{ - std::mt19937 generator(library->seed()); - std::uniform_int_distribution<typename T::value_type> distribution(std::numeric_limits<typename T::value_type>::min(), std::numeric_limits<typename T::value_type>::max()); - - for(int i = std::numeric_limits<typename T::value_type>::min(); i <= std::numeric_limits<typename T::value_type>::max(); i++) - { - table[i] = distribution(generator); - } -} - /** Convert an asymmetric quantized simple tensor into float using tensor quantization information. * * @param[in] src Quantized tensor. @@ -190,6 +145,7 @@ SimpleTensor<float> convert_from_asymmetric(const SimpleTensor<T> &src); * @param[in] src Float tensor. * @param[in] quantization_info Quantification information. * + * \relates arm_compute::test::SimpleTensor * @return Quantized tensor. */ template <typename T> @@ -208,7 +164,7 @@ SimpleTensor<float> convert_from_symmetric(const SimpleTensor<T> &src); * * @param[in] src Float tensor. * @param[in] quantization_info Quantification information. - * + * \relates arm_compute::test::SimpleTensor * @return Quantized tensor. */ template <typename T> @@ -276,8 +232,83 @@ std::pair<int, int> get_quantized_qasymm8_signed_bounds(const QuantizationInfo & * @param[in] max Floating point maximum value to be quantized * @param[in] channel_id Channel id for per channel quantization info. */ -std::pair<int, int> get_symm_quantized_per_channel_bounds(const QuantizationInfo &quant_info, float min, float max, size_t channel_id = 0); +std::pair<int, int> +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<ITensor *> 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 /* ARM_COMPUTE_TEST_VALIDATION_HELPERS_H */ +#endif // ACL_TESTS_VALIDATION_HELPERS_H |