diff options
Diffstat (limited to 'src/runtime/NEON')
6 files changed, 54 insertions, 46 deletions
diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp index 3bb69b1ffc..4bc8439d93 100644 --- a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp @@ -72,7 +72,7 @@ void NEDepthwiseConvolutionLayer3x3::configure_generic(ITensor _memory_group.manage(&_accumulator); _accumulator.allocator()->init(TensorInfo(accum_shape, 1, DataType::S32, output->info()->quantization_info())); _accumulator.info()->set_data_layout(accum_layout); - zero_value = PixelValue(static_cast<uint32_t>(input->info()->quantization_info().offset)); + zero_value = PixelValue(static_cast<uint32_t>(input->info()->quantization_info().uniform().offset)); } if(!_is_nchw) @@ -109,13 +109,15 @@ void NEDepthwiseConvolutionLayer3x3::configure_generic(ITensor // Configure biases accumulation if(_is_quantized) { - const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info(); + const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); + const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform(); + const UniformQuantizationInfo oq_info = (output->info()->total_size() == 0) ? iq_info : output->info()->quantization_info().uniform(); - float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale; + float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; int output_multiplier; int output_shift; quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - _output_stage_kernel.configure(&_accumulator, biases, _is_nchw ? output : &_permuted_output, output_multiplier, output_shift, output_quant_info.offset); + _output_stage_kernel.configure(&_accumulator, biases, _is_nchw ? output : &_permuted_output, output_multiplier, output_shift, oq_info.offset); _accumulator.allocator()->allocate(); } else if(_has_bias) @@ -459,13 +461,15 @@ void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weigh // Output staged configuration if(_is_quantized) { - const QuantizationInfo output_quant_info = output->info()->quantization_info(); + const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); + const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform(); + const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform(); - float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale; + float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; int output_multiplier; int output_shift; quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - _output_stage_kernel.configure(&_output_reshaped, biases, output_to_use, output_multiplier, output_shift, output_quant_info.offset); + _output_stage_kernel.configure(&_output_reshaped, biases, output_to_use, output_multiplier, output_shift, oq_info.offset); _output_reshaped.allocator()->allocate(); } @@ -483,8 +487,8 @@ void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weigh PixelValue zero_w(static_cast<int32_t>(0)); if(_is_quantized) { - zero_in = PixelValue(static_cast<int32_t>(input->info()->quantization_info().offset)); - zero_w = PixelValue(static_cast<int32_t>(weights->info()->quantization_info().offset)); + zero_in = PixelValue(static_cast<int32_t>(input->info()->quantization_info().uniform().offset)); + zero_w = PixelValue(static_cast<int32_t>(weights->info()->quantization_info().uniform().offset)); } BorderSize border_size = _v2mm_kernel.border_size(); _v2mm_input_fill_border.configure(&_input_reshaped, border_size, BorderMode::CONSTANT, zero_in); diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp index e1a17db6d4..7a74a7ea90 100644 --- a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp +++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp @@ -44,8 +44,8 @@ Status validate_mm(const ITensorInfo &input, const ITensorInfo &weights, const I { // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() // Extract and negate input and weights offset - const QuantizationInfo input_quantization_info(input.quantization_info().scale, -input.quantization_info().offset); - const QuantizationInfo weights_quantization_info(weights.quantization_info().scale, -weights.quantization_info().offset); + const QuantizationInfo input_quantization_info(input.quantization_info().uniform().scale, -input.quantization_info().uniform().offset); + const QuantizationInfo weights_quantization_info(weights.quantization_info().uniform().scale, -weights.quantization_info().uniform().offset); // Validate gemmlowp function ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(&input.clone()->set_quantization_info(input_quantization_info), @@ -90,8 +90,8 @@ void NEFullyConnectedLayer::configure_mm(const ITensor *input, const ITensor *we const QuantizationInfo input_quantization_info = input->info()->quantization_info(); const QuantizationInfo weights_quantization_info = weights->info()->quantization_info(); - input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); - weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); + input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset)); + weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset)); // Configure gemmlowp function _mm_gemmlowp.configure(input, weights, nullptr, output); @@ -227,11 +227,15 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh // Configure output stage for asymmetric quantized types if(_is_quantized) { - float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output->info()->quantization_info().scale; + const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); + const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform(); + const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform(); + + float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; int output_multiplier; int output_shift; quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - _gemmlowp_output_stage.configure(&_gemmlowp_output, biases, output, output_multiplier, output_shift, output->info()->quantization_info().offset); + _gemmlowp_output_stage.configure(&_gemmlowp_output, biases, output, output_multiplier, output_shift, oq_info.offset); _gemmlowp_output.allocator()->allocate(); } diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp index a2c4e8a8b1..c011ddd18f 100644 --- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp @@ -109,15 +109,15 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w { // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() // Extract and negate input and weights offset - const QuantizationInfo input_quantization_info = input->info()->quantization_info(); - const QuantizationInfo weights_quantization_info = weights->info()->quantization_info(); + const UniformQuantizationInfo iqinfo = input->info()->quantization_info().uniform(); + const UniformQuantizationInfo wqinfo = weights->info()->quantization_info().uniform(); - input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); - weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); + input->info()->set_quantization_info(QuantizationInfo(iqinfo.scale, -iqinfo.offset)); + weights->info()->set_quantization_info(QuantizationInfo(wqinfo.scale, -wqinfo.offset)); - const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input_quantization_info : output->info()->quantization_info(); + const UniformQuantizationInfo oqinfo = (output->info()->total_size() == 0) ? iqinfo : output->info()->quantization_info().uniform(); - float multiplier = input_quantization_info.scale * weights->info()->quantization_info().scale / output_quant_info.scale; + float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale; int output_multiplier; int output_shift; quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); @@ -132,10 +132,10 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w }; if(_is_activationlayer_enabled && supported_acts.count(act_info.activation()) != 0) { - const int a_const_int = output_quant_info.quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP); - const int b_const_int = output_quant_info.quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP); + const int a_const_int = quantize_qasymm8(act_info.a(), oqinfo); + const int b_const_int = quantize_qasymm8(act_info.b(), oqinfo); - min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int; + min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? oqinfo.offset : b_const_int; max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int; _is_activationlayer_enabled = false; @@ -143,7 +143,7 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w GEMMLowpOutputStageInfo output_info; output_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; - output_info.gemmlowp_offset = output_quant_info.offset; + output_info.gemmlowp_offset = oqinfo.offset; output_info.gemmlowp_multiplier = output_multiplier; output_info.gemmlowp_shift = output_shift; output_info.gemmlowp_min_bound = min_activation; @@ -152,8 +152,8 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w _mm_gemmlowp.configure(input, weights, biases, output, GEMMInfo(false, false, true, gemm_3d_depth, _skip_im2col, false, output_info)); // Revert back QuantizatioInfo as input and weights could be used in other convolution layers - input->info()->set_quantization_info(input_quantization_info); - weights->info()->set_quantization_info(weights_quantization_info); + input->info()->set_quantization_info(QuantizationInfo(iqinfo.scale, iqinfo.offset)); + weights->info()->set_quantization_info(QuantizationInfo(wqinfo.scale, wqinfo.offset)); } else { @@ -174,17 +174,17 @@ Status NEGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITens { // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() // Extract and negate input and weights offset - const QuantizationInfo input_quantization_info = input->quantization_info(); - const QuantizationInfo weights_quantization_info = weights->quantization_info(); + const UniformQuantizationInfo iqinfo = input->quantization_info().uniform(); + const UniformQuantizationInfo wqinfo = weights->quantization_info().uniform(); std::unique_ptr<ITensorInfo> input_qa = input->clone(); std::unique_ptr<ITensorInfo> weights_qa = weights->clone(); - input_qa->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); - weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); + input_qa->set_quantization_info(QuantizationInfo(iqinfo.scale, -iqinfo.offset)); + weights_qa->set_quantization_info(QuantizationInfo(wqinfo.scale, -wqinfo.offset)); - const QuantizationInfo output_quant_info = (output->total_size() == 0) ? input_quantization_info : output->quantization_info(); + const UniformQuantizationInfo oqinfo = (output->total_size() == 0) ? iqinfo : output->quantization_info().uniform(); - float multiplier = input_quantization_info.scale * weights->quantization_info().scale / output_quant_info.scale; + float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale; int output_multiplier; int output_shift; quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); @@ -199,16 +199,16 @@ Status NEGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITens }; if(is_activation_enabled && supported_acts.count(act_info.activation()) != 0) { - const int a_const_int = output_quant_info.quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP); - const int b_const_int = output_quant_info.quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP); + const int a_const_int = quantize_qasymm8(act_info.a(), oqinfo); + const int b_const_int = quantize_qasymm8(act_info.b(), oqinfo); - min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int; + min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? oqinfo.offset : b_const_int; max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int; } GEMMLowpOutputStageInfo output_info; output_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; - output_info.gemmlowp_offset = output_quant_info.offset; + output_info.gemmlowp_offset = oqinfo.offset; output_info.gemmlowp_multiplier = output_multiplier; output_info.gemmlowp_shift = output_shift; output_info.gemmlowp_min_bound = min_activation; diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp index 54f49a6707..d8773e37ab 100644 --- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp +++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp @@ -61,8 +61,8 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, _mtx_b_reshape_kernel = nullptr; // Set internal variables - _a_offset = a->info()->quantization_info().offset; - _b_offset = b->info()->quantization_info().offset; + _a_offset = a->info()->quantization_info().uniform().offset; + _b_offset = b->info()->quantization_info().uniform().offset; _run_vector_matrix_multiplication = a->info()->dimension(1) < 2; _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); _is_prepared = false; @@ -224,8 +224,8 @@ Status NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITenso TensorInfo tmp_b_info{}; TensorInfo mm_result_s32_info{}; - int32_t a_offset = a->quantization_info().offset; - int32_t b_offset = b->quantization_info().offset; + int32_t a_offset = a->quantization_info().uniform().offset; + int32_t b_offset = b->quantization_info().uniform().offset; const bool reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); bool fuse_output_stage = gemm_info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE; diff --git a/src/runtime/NEON/functions/NEPoolingLayer.cpp b/src/runtime/NEON/functions/NEPoolingLayer.cpp index cbfd68485f..d92086d08d 100644 --- a/src/runtime/NEON/functions/NEPoolingLayer.cpp +++ b/src/runtime/NEON/functions/NEPoolingLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -55,7 +55,7 @@ void NEPoolingLayer::configure(ITensor *input, ITensor *output, const PoolingLay PixelValue zero_value(0.f); if(is_data_type_quantized_asymmetric(input->info()->data_type()) && !pool_info.exclude_padding()) { - zero_value = PixelValue(static_cast<uint32_t>(input->info()->quantization_info().offset)); + zero_value = PixelValue(static_cast<uint32_t>(input->info()->quantization_info().uniform().offset)); } _border_handler.configure(input, _pooling_layer_kernel.border_size(), border_mode, zero_value); break; diff --git a/src/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.cpp b/src/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.cpp index 049bf66689..0499d9930f 100644 --- a/src/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.cpp +++ b/src/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.cpp @@ -72,9 +72,9 @@ std::unique_ptr<depthwise::IDepthwiseConvolution> create_convolver(const ITensor // Create quantized convolver if(data_type == DataType::QASYMM8) { - const QuantizationInfo &input_qinfo = input->info()->quantization_info(); - const QuantizationInfo &weights_qinfo = weights->info()->quantization_info(); - const QuantizationInfo &output_qinfo = output->info()->quantization_info(); + const UniformQuantizationInfo input_qinfo = input->info()->quantization_info().uniform(); + const UniformQuantizationInfo weights_qinfo = weights->info()->quantization_info().uniform(); + const UniformQuantizationInfo output_qinfo = output->info()->quantization_info().uniform(); // Check that quantization info are in the range [0, 255] ARM_COMPUTE_ERROR_ON(input_qinfo.offset < 0 || input_qinfo.offset > 255); |