From 63e0beb9fb9646407d123e830165546e9129e95d Mon Sep 17 00:00:00 2001 From: Giorgio Arena Date: Fri, 24 Sep 2021 14:04:27 +0100 Subject: Add support for non-constant weights and biases in CpuFullyConnected Changing the approach for specifying that weights and biases tensors are non-constant by making it a member of TensorInfo rather than an option of the functions. Resolves: COMPMID-4222, COMPMID-4811 Signed-off-by: Giorgio Arena Change-Id: I9b0081ccbcf8271ce029ba6755563d64c59e1d32 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6313 Tested-by: Arm Jenkins Reviewed-by: Pablo Marquez Tello Reviewed-by: Michele Di Giorgio Comments-Addressed: Arm Jenkins --- .../fixtures/FullyConnectedLayerFixture.h | 128 +++++++++++++++++---- 1 file changed, 107 insertions(+), 21 deletions(-) (limited to 'tests/validation/fixtures/FullyConnectedLayerFixture.h') diff --git a/tests/validation/fixtures/FullyConnectedLayerFixture.h b/tests/validation/fixtures/FullyConnectedLayerFixture.h index 7d767642f3..3048c56f6b 100644 --- a/tests/validation/fixtures/FullyConnectedLayerFixture.h +++ b/tests/validation/fixtures/FullyConnectedLayerFixture.h @@ -232,7 +232,7 @@ protected: fill(weights, 1); fill(bias, 2); - return reference::activation_layer(reference::fully_connected_layer(src, weights, bias, output_shape), _activation_info, _quantization_info); + return reference::activation_layer(reference::fully_connected_layer(src, weights, bias, output_shape, _quantization_info), _activation_info, _quantization_info); } TensorType _target{}; @@ -273,7 +273,7 @@ public: }; template -class FullyConnectedWithDynamicWeightsFixture : public framework::Fixture +class FullyConnectedWithDynamicTensorsFixture : public framework::Fixture { private: template @@ -289,6 +289,16 @@ private: std::uniform_real_distribution distribution(-1.0f, 1.0f); library->fill(tensor, distribution, i); } + else if(_data_type == DataType::QASYMM8) + { + std::uniform_int_distribution distribution(0, 30); + library->fill(tensor, distribution, i); + } + else if(_data_type == DataType::S32) + { + std::uniform_int_distribution distribution(-50, 50); + library->fill(tensor, distribution, i); + } else { library->fill_tensor_uniform(tensor, i); @@ -324,6 +334,11 @@ private: constexpr AbsoluteTolerance abs_tolerance_f32(0.0001f); validate(AccessorType(target), ref, rel_tolerance_f32, 0, abs_tolerance_f32); } + else if(_data_type == DataType::QASYMM8) + { + constexpr AbsoluteTolerance tolerance_qasymm8(1); + validate(AccessorType(target), ref, tolerance_qasymm8); + } else { validate(AccessorType(target), ref); @@ -331,32 +346,51 @@ private: } public: + using TDecay = typename std::decay::type; + using TBias = typename std::conditional < (std::is_same::value || std::is_same::value), int32_t, T >::type; + template 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) { _data_type = data_type; + 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(); + // Setup tensor meta-data - TensorInfo src_info(src_shape, 1, data_type); + const TensorInfo src_info(src_shape, 1, data_type, src_qinfo); _src.allocator()->init(src_info); - TensorShape tr_weights_shape{ weights_shape[1], weights_shape[0] }; - TensorInfo wei_info(tr_weights_shape, 1, data_type); + TensorInfo wei_info(weights_shape, 1, data_type, weights_qinfo); + if(!constant_weights) + { + 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); _weights.allocator()->init(wei_info); - TensorInfo bias_info(bias_shape, 1, data_type); + TensorInfo bias_info(bias_shape, 1, bias_data_type); + bias_info.set_are_values_constant(constant_bias); _bias.allocator()->init(bias_info); - TensorInfo dst_info(dst_shape, 1, data_type); + const TensorInfo dst_info(dst_shape, 1, data_type, dst_qinfo); _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 = true; + fc_info.transpose_weights = false; + } FunctionType fc; fc.configure(&_src, &_weights, &_bias, &_dst, fc_info); @@ -369,29 +403,55 @@ public: // Run multiple iterations with different inputs constexpr int num_iterations = 5; int randomizer_offset = 0; + + // Create reference tensors + SimpleTensor src{ src_shape, data_type, 1, src_qinfo }; + SimpleTensor weights{ weights_shape, data_type, 1, weights_qinfo }; + SimpleTensor 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); + } + if(constant_bias) + { + fill(AccessorType(_bias), 2); + fill(bias, 2); + } + 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) + { + fill_transposed_weights(_weights, weights_shape, randomizer_offset + 1); + } + if(!constant_bias) + { + fill(AccessorType(_bias), randomizer_offset + 2); + } fc.run(); } // Run reference and compare { - SimpleTensor src{ src_shape, data_type }; - SimpleTensor weights{ weights_shape, data_type }; - SimpleTensor 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); + } + if(!constant_bias) + { + fill(bias, randomizer_offset + 2); + } - auto dst = reference::activation_layer(reference::fully_connected_layer(src, weights, bias, dst_shape), activation_info); + auto dst = reference::activation_layer(reference::fully_connected_layer(src, weights, bias, dst_shape, dst_qinfo), activation_info, dst_qinfo); // Validate validate_with_tolerance(_dst, dst); @@ -405,6 +465,32 @@ private: TensorType _src{}, _weights{}, _bias{}, _dst{}; DataType _data_type{ DataType::UNKNOWN }; }; + +template +class FullyConnectedWithDynamicWeightsFixture : public FullyConnectedWithDynamicTensorsFixture +{ +public: + template + void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape dst_shape, + DataType data_type, ActivationLayerInfo activation_info) + { + FullyConnectedWithDynamicTensorsFixture::setup(src_shape, weights_shape, bias_shape, + dst_shape, data_type, activation_info, false, true); + } +}; + +template +class FullyConnectedWithDynamicBiasFixture : public FullyConnectedWithDynamicTensorsFixture +{ +public: + template + void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape dst_shape, + DataType data_type, ActivationLayerInfo activation_info) + { + FullyConnectedWithDynamicTensorsFixture::setup(src_shape, weights_shape, bias_shape, + dst_shape, data_type, activation_info, true, false); + } +}; } // namespace validation } // namespace test } // namespace arm_compute -- cgit v1.2.1