diff options
Diffstat (limited to 'tests/validation/fixtures/FullyConnectedLayerFixture.h')
-rw-r--r-- | tests/validation/fixtures/FullyConnectedLayerFixture.h | 205 |
1 files changed, 164 insertions, 41 deletions
diff --git a/tests/validation/fixtures/FullyConnectedLayerFixture.h b/tests/validation/fixtures/FullyConnectedLayerFixture.h index 7cfe6e49b9..05f20ac12b 100644 --- a/tests/validation/fixtures/FullyConnectedLayerFixture.h +++ b/tests/validation/fixtures/FullyConnectedLayerFixture.h @@ -55,6 +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: + 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<uint32_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<int32_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, _quantization_info), _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{}; @@ -240,8 +287,22 @@ 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> @@ -289,12 +350,17 @@ private: } else if(_data_type == DataType::QASYMM8) { - std::uniform_int_distribution<uint32_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<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 @@ -352,6 +418,40 @@ private: 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: 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; @@ -364,15 +464,22 @@ public: const bool is_quantized = is_data_type_quantized(data_type); const DataType bias_data_type = (is_quantized) ? DataType::S32 : data_type; - const QuantizationInfo src_qinfo = is_quantized ? QuantizationInfo(0.1f, 10) : QuantizationInfo(); - const QuantizationInfo weights_qinfo = is_quantized ? QuantizationInfo(0.3f, 20) : QuantizationInfo(); - const QuantizationInfo dst_qinfo = is_quantized ? QuantizationInfo(0.2f, 5) : QuantizationInfo(); + 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); + } // Configure TensorInfo Objects - const TensorInfo src_info(src_shape, 1, data_type, src_qinfo); - const TensorInfo dst_info(dst_shape, 1, data_type, dst_qinfo); + 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_qinfo); + TensorInfo wei_info(weights_shape, 1, data_type, _weights_q_info); if(!constant_weights && weights_reshaped) { @@ -412,20 +519,20 @@ public: int randomizer_offset = 0; // Create reference tensors - SimpleTensor<T> src{ src_shape, data_type, 1, src_qinfo }; - SimpleTensor<T> weights{ weights_shape, data_type, 1, weights_qinfo }; + 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); - fill(weights, 1); + fill(AccessorType(_weights), 1 + _hash); + fill(weights, 1 + _hash); } if(constant_bias && !remove_bias) { - fill(AccessorType(_bias), 2); - fill(bias, 2); + fill(AccessorType(_bias), 2 + _hash); + fill(bias, 2 + _hash); } // To remove bias, fill with 0 if(remove_bias && is_quantized) @@ -446,16 +553,16 @@ public: { if(weights_reshaped) { - fill_transposed_weights(_weights, weights_shape, randomizer_offset + 1); + fill_transposed_weights(_weights, weights_shape, randomizer_offset + 1 + _hash); } else { - fill(AccessorType(_weights), randomizer_offset + 1); + fill(AccessorType(_weights), randomizer_offset + 1 +_hash); } } if(!constant_bias && !remove_bias) { - fill(AccessorType(_bias), randomizer_offset + 2); + fill(AccessorType(_bias), randomizer_offset + 2 + _hash); } fc.run(); @@ -467,14 +574,14 @@ public: fill(src, randomizer_offset); if(!constant_weights) { - fill(weights, randomizer_offset + 1); + fill(weights, randomizer_offset + 1 + _hash); } if(!constant_bias && !remove_bias) { - fill(bias, randomizer_offset + 2); + fill(bias, randomizer_offset + 2 + _hash); } - auto dst = reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, dst_shape, dst_qinfo), activation_info, dst_qinfo); + 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); @@ -487,6 +594,22 @@ 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> @@ -521,7 +644,7 @@ public: 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 /* weights_reshaped (not used) */); + dst_shape, data_type, activation_info, true, false, false, false); } }; } // namespace validation |