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author | Georgios Pinitas <georgios.pinitas@arm.com> | 2019-05-21 13:32:43 +0100 |
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committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2019-06-03 14:51:29 +0000 |
commit | 4c5469b192665c94118a8a558787cb9cec2d0765 (patch) | |
tree | 168aa969de8243bdbb1f25247dd9f54d037ae32c /src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp | |
parent | 43a129e94df41f9ac8bc78b702da5a387ada0494 (diff) | |
download | ComputeLibrary-4c5469b192665c94118a8a558787cb9cec2d0765.tar.gz |
COMPMID-2225: Add interface support for new quantized data types.
Add support for:
-QSYMM8, 8-bit quantized symmetric
-QSYMM8_PER_CHANNEL, 8-bit quantized symmetric with per channel quantization
Change-Id: I00c4ff98e44af37419470af61419ee95d0de2463
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1236
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp')
-rw-r--r-- | src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp | 44 |
1 files changed, 22 insertions, 22 deletions
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; |