diff options
Diffstat (limited to 'tests/validation/fixtures/FullyConnectedLayerFixture.h')
| -rw-r--r-- | tests/validation/fixtures/FullyConnectedLayerFixture.h | 372 |
1 files changed, 307 insertions, 65 deletions
diff --git a/tests/validation/fixtures/FullyConnectedLayerFixture.h b/tests/validation/fixtures/FullyConnectedLayerFixture.h index 7d767642f3..344187868f 100644 --- a/tests/validation/fixtures/FullyConnectedLayerFixture.h +++ b/tests/validation/fixtures/FullyConnectedLayerFixture.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,8 +21,8 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE -#define ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE +#ifndef ACL_TESTS_VALIDATION_FIXTURES_FULLYCONNECTEDLAYERFIXTURE_H +#define ACL_TESTS_VALIDATION_FIXTURES_FULLYCONNECTEDLAYERFIXTURE_H #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/Types.h" @@ -34,6 +34,7 @@ #include "tests/framework/Asserts.h" #include "tests/framework/Fixture.h" #include "tests/validation/Helpers.h" +#include "tests/validation/Validation.h" #include "tests/validation/reference/ActivationLayer.h" #include "tests/validation/reference/FullyConnectedLayer.h" #include "tests/validation/reference/Utils.h" @@ -54,7 +55,40 @@ public: using TBias = typename std::conditional < (std::is_same<TDecay, uint8_t>::value || std::is_same<TDecay, int8_t>::value), int32_t, T >::type; public: - template <typename...> + void setup_quantization(TensorShape weights_shape, TensorShape output_shape, QuantizationInfo &input_q_info, QuantizationInfo &weights_q_info, DataType data_type) + { + _hash = weights_shape[0] + weights_shape[1] + output_shape[0] + output_shape[1]; + const int32_t t_max = static_cast<int32_t>(std::numeric_limits<T>::max()); + const int32_t t_min = static_cast<int32_t>(std::numeric_limits<T>::min()); + + std::mt19937 generator(library->seed() + _hash); + std::uniform_real_distribution<float> distribution_float(-5.0f, 3.0f); + std::uniform_int_distribution<int32_t> distribution_t(t_min, t_max); + + const float scale_lhs = pow(2, distribution_float(generator)); // [2^-5, 2^3] + const float scale_rhs = pow(2, distribution_float(generator)); // [2^-5, 2^3] + const int32_t offset_lhs = distribution_t(generator); + const int32_t offset_rhs = distribution_t(generator); + + input_q_info = QuantizationInfo(scale_lhs, offset_lhs); + weights_q_info = QuantizationInfo(scale_rhs, offset_rhs); + + + const int k = weights_shape.x(); + QuantizationHint q_hint = suggest_mac_dst_q_info_and_bias(input_q_info, weights_q_info, k, data_type, 0.1f /* bias_fraction */, 4 /* number of standard deviations*/); + + _dst_q_info = q_hint.q_info; + _min_bias = q_hint.bias_min; + _max_bias = q_hint.bias_max; + + // Do not change here as these limits are the natural limits of the associated data types and + // are embedded in the computation of the dst quantization info. + _min_u8 = 0; + _max_u8 = 255; + _min_s8 = -128; + _max_s8 = 127; + } + void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo activation_info, bool mixed_layout = false) { @@ -64,7 +98,20 @@ public: _mixed_layout = mixed_layout; _data_type = data_type; _bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type; - _quantization_info = quantization_info; + + // Note : Quantization Info parameter from setup function is only used when quant datatype and activation function is not enabled or is identity. + if(is_data_type_quantized(data_type) && (!activation_info.enabled() || activation_info.activation() == ActivationFunction::IDENTITY)) + { + // Initialises quantization info with appropriate scale and offset for given input shapes. + setup_quantization(weights_shape, output_shape,_input_q_info, _weight_q_info, data_type); + } + else + { + _input_q_info = quantization_info; + _weight_q_info = quantization_info; + _dst_q_info = quantization_info; + } + _activation_info = activation_info; _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights); @@ -92,17 +139,17 @@ protected: { if(_data_type == DataType::QASYMM8) { - std::uniform_int_distribution<uint8_t> distribution(0, 30); + std::uniform_int_distribution<uint32_t> distribution(_min_u8, _max_u8); library->fill(tensor, distribution, i); } else if(_data_type == DataType::QASYMM8_SIGNED) { - std::uniform_int_distribution<int8_t> distribution(-15, 15); + std::uniform_int_distribution<int32_t> distribution(_min_s8, _max_s8); library->fill(tensor, distribution, i); } else if(_data_type == DataType::S32) { - std::uniform_int_distribution<int32_t> distribution(-50, 50); + std::uniform_int_distribution<int32_t> distribution(_min_bias, _max_bias); library->fill(tensor, distribution, i); } else if(_data_type == DataType::F16) @@ -144,10 +191,10 @@ protected: } // Create tensors - TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _quantization_info); - TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, _data_type, 1, _quantization_info); - TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _quantization_info); - TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _quantization_info); + TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _input_q_info); + TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, _data_type, 1, _weight_q_info); + TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1); + TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _dst_q_info); // Create Fully Connected layer info FullyConnectedLayerInfo fc_info; @@ -178,8 +225,8 @@ protected: ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); // Fill tensors - fill(AccessorType(src), 0); - fill(AccessorType(bias), 2); + fill(AccessorType(src), 0 + _hash); + fill(AccessorType(bias), 2 + _hash); if(!reshape_weights || !transpose_weights) { @@ -187,7 +234,7 @@ protected: RawTensor tmp(tmp_shape, _data_type, 1); // Fill with original shape - fill(tmp, 1); + fill(tmp, 1 + _hash); // Transpose elementwise tmp = transpose(tmp); @@ -204,7 +251,7 @@ protected: } else { - fill(AccessorType(weights), 1); + fill(AccessorType(weights), 1 + _hash); } if(_mixed_layout) @@ -223,16 +270,16 @@ protected: SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape) { // Create reference - SimpleTensor<T> src{ input_shape, _data_type, 1, _quantization_info }; - SimpleTensor<T> weights{ weights_shape, _data_type, 1, _quantization_info }; - SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _quantization_info }; + SimpleTensor<T> src{ input_shape, _data_type, 1, _input_q_info }; + SimpleTensor<T> weights{ weights_shape, _data_type, 1, _weight_q_info }; + SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, QuantizationInfo() }; // Fill reference - fill(src, 0); - fill(weights, 1); - fill(bias, 2); + fill(src, 0 + _hash); + fill(weights, 1 + _hash); + fill(bias, 2 + _hash); - return reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, output_shape), _activation_info, _quantization_info); + return reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, output_shape, _dst_q_info), _activation_info, _dst_q_info); } TensorType _target{}; @@ -240,15 +287,28 @@ protected: DataType _data_type{}; DataType _bias_data_type{}; bool _mixed_layout{ false }; - QuantizationInfo _quantization_info{}; + QuantizationInfo _input_q_info{}; + QuantizationInfo _weight_q_info{}; + QuantizationInfo _dst_q_info{}; ActivationLayerInfo _activation_info{}; + + // Random initialization limits + // Default values are previously handcrafted limits + // that sould be used when we don't use dynamic quantization + int32_t _min_bias{-50}; + int32_t _max_bias{50}; + + int32_t _min_u8{0}; + int32_t _max_u8{30}; + int32_t _min_s8{-15}; + int32_t _max_s8{15}; + int _hash{0}; }; template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false> class FullyConnectedLayerValidationFixture : public FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T> { public: - template <typename...> void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type, ActivationLayerInfo activation_info) { @@ -262,7 +322,6 @@ template <typename TensorType, typename AccessorType, typename FunctionType, typ class FullyConnectedLayerValidationQuantizedFixture : public FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T> { public: - template <typename...> void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo activation_info) { @@ -273,7 +332,7 @@ public: }; template <typename TensorType, typename AccessorType, typename FunctionType, typename T> -class FullyConnectedWithDynamicWeightsFixture : public framework::Fixture +class FullyConnectedWithDynamicTensorsFixture : public framework::Fixture { private: template <typename U> @@ -289,6 +348,21 @@ private: std::uniform_real_distribution<float> distribution(-1.0f, 1.0f); library->fill(tensor, distribution, i); } + else if(_data_type == DataType::QASYMM8) + { + std::uniform_int_distribution<uint32_t> distribution(_min_u8, _max_u8); + library->fill(tensor, distribution, i); + } + else if(_data_type == DataType::QASYMM8_SIGNED) + { + std::uniform_int_distribution<int32_t> distribution(_min_s8, _max_s8); + library->fill(tensor, distribution, i); + } + else if(_data_type == DataType::S32) + { + std::uniform_int_distribution<int32_t> distribution(_min_bias, _max_bias); + library->fill(tensor, distribution, i); + } else { library->fill_tensor_uniform(tensor, i); @@ -316,82 +390,198 @@ private: } } - void validate_with_tolerance(TensorType &target, SimpleTensor<T> &ref) + void validate_with_tolerance(TensorType &target, SimpleTensor<float> &ref) { - if(_data_type == DataType::F32) - { - constexpr RelativeTolerance<float> rel_tolerance_f32(0.05f); - constexpr AbsoluteTolerance<float> abs_tolerance_f32(0.0001f); - validate(AccessorType(target), ref, rel_tolerance_f32, 0, abs_tolerance_f32); - } - else - { - validate(AccessorType(target), ref); - } + constexpr RelativeTolerance<float> rel_tolerance_f32(0.01f); + constexpr AbsoluteTolerance<float> abs_tolerance_f32(0.001f); + validate(AccessorType(target), ref, rel_tolerance_f32, 0, abs_tolerance_f32); + } + + void validate_with_tolerance(TensorType &target, SimpleTensor<half_float::half> &ref) + { + constexpr AbsoluteTolerance<float> abs_tolerance_f16(0.3f); + const RelativeTolerance<half_float::half> rel_tolerance_f16(half_float::half(0.2f)); + constexpr float tolerance_num_f16 = 0.07f; + + validate(AccessorType(target), ref, rel_tolerance_f16, tolerance_num_f16, abs_tolerance_f16); + } + + void validate_with_tolerance(TensorType &target, SimpleTensor<uint8_t> &ref) + { + constexpr AbsoluteTolerance<uint32_t> tolerance_qasymm8(1); + validate(AccessorType(target), ref, tolerance_qasymm8); + } + + void validate_with_tolerance(TensorType &target, SimpleTensor<int8_t> &ref) + { + constexpr AbsoluteTolerance<int32_t> tolerance_qasymm8_signed(1); + validate(AccessorType(target), ref, tolerance_qasymm8_signed); + } + + void setup_quantization(TensorShape weights_shape, TensorShape output_shape, QuantizationInfo &input_q_info, QuantizationInfo &weights_q_info, DataType data_type) + { + _hash = weights_shape[0] + weights_shape[1] + output_shape[0] + output_shape[1]; + + const int32_t t_max = static_cast<int32_t>(std::numeric_limits<T>::max()); + const int32_t t_min = static_cast<int32_t>(std::numeric_limits<T>::min()); + + std::mt19937 generator(library->seed() + _hash); + std::uniform_real_distribution<float> distribution_float(-5.0f, 3.0f); + std::uniform_int_distribution<int32_t> distribution_t(t_min, t_max); + + const float scale_lhs = pow(2, distribution_float(generator)); // [2^-5, 2^3] + const float scale_rhs = pow(2, distribution_float(generator)); // [2^-5, 2^3] + const int32_t offset_lhs = distribution_t(generator); + const int32_t offset_rhs = distribution_t(generator); + + input_q_info = QuantizationInfo(scale_lhs, offset_lhs); + weights_q_info = QuantizationInfo(scale_rhs, offset_rhs); + + const int k = weights_shape.x(); + QuantizationHint q_hint = suggest_mac_dst_q_info_and_bias(input_q_info, weights_q_info, k, data_type, 0.1f /* bias_fraction */, 4 /* number of standard deviations*/); + + _dst_q_info = q_hint.q_info; + _min_bias = q_hint.bias_min; + _max_bias = q_hint.bias_max; + + // Do not change here as these limits are the natural limits of the associated data types and + // are embedded in the computation of the dst quantization info. + _min_u8 = 0; + _max_u8 = 255; + _min_s8 = -128; + _max_s8 = 127; } public: - template <typename...> + using TDecay = typename std::decay<T>::type; + using TBias = typename std::conditional < (std::is_same<TDecay, uint8_t>::value || std::is_same<TDecay, int8_t>::value), int32_t, T >::type; + void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape dst_shape, - DataType data_type, ActivationLayerInfo activation_info) + DataType data_type, ActivationLayerInfo activation_info, bool constant_weights, bool constant_bias, bool weights_reshaped, bool remove_bias = false) { _data_type = data_type; - // Setup tensor meta-data - TensorInfo src_info(src_shape, 1, data_type); - _src.allocator()->init(src_info); + const bool is_quantized = is_data_type_quantized(data_type); + const DataType bias_data_type = (is_quantized) ? DataType::S32 : data_type; - TensorShape tr_weights_shape{ weights_shape[1], weights_shape[0] }; - TensorInfo wei_info(tr_weights_shape, 1, data_type); - _weights.allocator()->init(wei_info); + if (is_quantized && (!activation_info.enabled() || activation_info.activation() == ActivationFunction::IDENTITY)) + { + setup_quantization(weights_shape, dst_shape, _src_q_info, _weights_q_info, data_type); + } + else + { + _src_q_info = QuantizationInfo(0.1f, 10); + _dst_q_info = QuantizationInfo(0.3f, 20); + _weights_q_info = QuantizationInfo(0.2f, 5); + } - TensorInfo bias_info(bias_shape, 1, data_type); - _bias.allocator()->init(bias_info); + // Configure TensorInfo Objects + const TensorInfo src_info(src_shape, 1, data_type, _src_q_info); + const TensorInfo dst_info(dst_shape, 1, data_type, _dst_q_info); + TensorInfo bias_info(bias_shape, 1, bias_data_type); + TensorInfo wei_info(weights_shape, 1, data_type, _weights_q_info); - TensorInfo dst_info(dst_shape, 1, data_type); + if(!constant_weights && weights_reshaped) + { + const TensorShape tr_weights_shape{ weights_shape[1], weights_shape[0] }; + wei_info.set_tensor_shape(tr_weights_shape); + } + wei_info.set_are_values_constant(constant_weights); + bias_info.set_are_values_constant(constant_bias); + + // Initialise Tensors + _src.allocator()->init(src_info); + _weights.allocator()->init(wei_info); + if(!remove_bias) + _bias.allocator()->init(bias_info); _dst.allocator()->init(dst_info); // Configure FC layer and mark the weights as non constant FullyConnectedLayerInfo fc_info; - fc_info.activation_info = activation_info; - fc_info.are_weights_reshaped = true; - fc_info.transpose_weights = false; - fc_info.constant_weights = false; + fc_info.activation_info = activation_info; + if(!constant_weights) + { + fc_info.are_weights_reshaped = weights_reshaped; + fc_info.transpose_weights = !weights_reshaped; + } FunctionType fc; - fc.configure(&_src, &_weights, &_bias, &_dst, fc_info); + fc.configure(&_src, &_weights, (remove_bias) ? nullptr : &_bias, &_dst, fc_info); // Allocate all the tensors _src.allocator()->allocate(); _weights.allocator()->allocate(); - _bias.allocator()->allocate(); + if(!remove_bias) + _bias.allocator()->allocate(); _dst.allocator()->allocate(); // Run multiple iterations with different inputs constexpr int num_iterations = 5; int randomizer_offset = 0; + + // Create reference tensors + SimpleTensor<T> src{ src_shape, data_type, 1, _src_q_info }; + SimpleTensor<T> weights{ weights_shape, data_type, 1, _weights_q_info }; + SimpleTensor<TBias> bias{ bias_shape, bias_data_type }; + + // Fill weights and/or bias if they remain constant + if(constant_weights) + { + fill(AccessorType(_weights), 1 + _hash); + fill(weights, 1 + _hash); + } + if(constant_bias && !remove_bias) + { + fill(AccessorType(_bias), 2 + _hash); + fill(bias, 2 + _hash); + } + // To remove bias, fill with 0 + if(remove_bias && is_quantized) + { + library->fill_tensor_value(bias, 0); + } + else if(remove_bias) + { + library->fill_tensor_value(bias, (float)0.0); + } + for(int i = 0; i < num_iterations; ++i) { // Run target { fill(AccessorType(_src), randomizer_offset); - fill_transposed_weights(_weights, weights_shape, randomizer_offset + 1); - fill(AccessorType(_bias), randomizer_offset + 2); + if(!constant_weights) + { + if(weights_reshaped) + { + fill_transposed_weights(_weights, weights_shape, randomizer_offset + 1 + _hash); + } + else + { + fill(AccessorType(_weights), randomizer_offset + 1 +_hash); + } + } + if(!constant_bias && !remove_bias) + { + fill(AccessorType(_bias), randomizer_offset + 2 + _hash); + } fc.run(); } // Run reference and compare { - SimpleTensor<T> src{ src_shape, data_type }; - SimpleTensor<T> weights{ weights_shape, data_type }; - SimpleTensor<T> bias{ bias_shape, data_type }; - // Fill reference fill(src, randomizer_offset); - fill(weights, randomizer_offset + 1); - fill(bias, randomizer_offset + 2); + if(!constant_weights) + { + fill(weights, randomizer_offset + 1 + _hash); + } + if(!constant_bias && !remove_bias) + { + fill(bias, randomizer_offset + 2 + _hash); + } - auto dst = reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, dst_shape), activation_info); + auto dst = reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, dst_shape, _dst_q_info), activation_info, _dst_q_info); // Validate validate_with_tolerance(_dst, dst); @@ -404,8 +594,60 @@ public: private: TensorType _src{}, _weights{}, _bias{}, _dst{}; DataType _data_type{ DataType::UNKNOWN }; + + QuantizationInfo _src_q_info{}; + QuantizationInfo _weights_q_info{}; + QuantizationInfo _dst_q_info{}; + + // Random initialization limits + // Default values are previously handcrafted limits + // that sould be used when we don't use dynamic quantization + int32_t _min_bias{-50}; + int32_t _max_bias{50}; + + int32_t _min_u8{0}; + int32_t _max_u8{30}; + int32_t _min_s8{-15}; + int32_t _max_s8{15}; + int _hash{0}; +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class FullyConnectedWithDynamicWeightsFixture : public FullyConnectedWithDynamicTensorsFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape dst_shape, + DataType data_type, ActivationLayerInfo activation_info, bool weights_reshaped) + { + FullyConnectedWithDynamicTensorsFixture<TensorType, AccessorType, FunctionType, T>::setup(src_shape, weights_shape, bias_shape, + dst_shape, data_type, activation_info, false, true, weights_reshaped, false); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class FullyConnectedDynamicNoBiasFixture : public FullyConnectedWithDynamicTensorsFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape dst_shape, + DataType data_type, ActivationLayerInfo activation_info, bool weights_reshaped) + { + FullyConnectedWithDynamicTensorsFixture<TensorType, AccessorType, FunctionType, T>::setup(src_shape, weights_shape, bias_shape, + dst_shape, data_type, activation_info, false, true, weights_reshaped, true); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class FullyConnectedWithDynamicBiasFixture : public FullyConnectedWithDynamicTensorsFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape dst_shape, + DataType data_type, ActivationLayerInfo activation_info) + { + FullyConnectedWithDynamicTensorsFixture<TensorType, AccessorType, FunctionType, T>::setup(src_shape, weights_shape, bias_shape, + dst_shape, data_type, activation_info, true, false, false, false); + } }; } // namespace validation } // namespace test } // namespace arm_compute -#endif /* ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE */ +#endif // ACL_TESTS_VALIDATION_FIXTURES_FULLYCONNECTEDLAYERFIXTURE_H |