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Diffstat (limited to 'src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp')
-rw-r--r--src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp88
1 files changed, 40 insertions, 48 deletions
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index f4377cdaf2..caff117e09 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -59,7 +59,7 @@ void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const I
Status NEConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, DataType::QSYMM8_PER_CHANNEL, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
if(biases != nullptr)
@@ -114,18 +114,18 @@ 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 UniformQuantizationInfo iqinfo = input->info()->quantization_info().uniform();
- const UniformQuantizationInfo wqinfo = weights->info()->quantization_info().uniform();
-
- input->info()->set_quantization_info(QuantizationInfo(iqinfo.scale, -iqinfo.offset));
- weights->info()->set_quantization_info(QuantizationInfo(wqinfo.scale, -wqinfo.offset));
-
- const UniformQuantizationInfo oqinfo = (output->info()->total_size() == 0) ? iqinfo : output->info()->quantization_info().uniform();
-
- 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);
+ const QuantizationInfo iqinfo = input->info()->quantization_info();
+ const QuantizationInfo wqinfo = weights->info()->quantization_info();
+ const QuantizationInfo oqinfo = (output->info()->total_size() == 0) ? iqinfo : output->info()->quantization_info();
+ const UniformQuantizationInfo uiqinfo = iqinfo.uniform();
+ const UniformQuantizationInfo uoqinfo = oqinfo.uniform();
+
+ input->info()->set_quantization_info(QuantizationInfo(uiqinfo.scale, -uiqinfo.offset));
+ if(!is_data_type_quantized_per_channel(weights->info()->data_type()))
+ {
+ const UniformQuantizationInfo uwqinfo = wqinfo.uniform();
+ weights->info()->set_quantization_info(QuantizationInfo(uwqinfo.scale, -uwqinfo.offset));
+ }
// Merge activation with output stage
int min_activation = 0;
@@ -133,26 +133,25 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w
if(supported_acts.count(act_info.activation()) != 0)
{
- const int a_const_int = quantize_qasymm8(act_info.a(), oqinfo);
- const int b_const_int = quantize_qasymm8(act_info.b(), oqinfo);
+ const int a_const_int = quantize_qasymm8(act_info.a(), uoqinfo);
+ const int b_const_int = quantize_qasymm8(act_info.b(), uoqinfo);
- min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? oqinfo.offset : b_const_int;
+ min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? uoqinfo.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 = oqinfo.offset;
- output_info.gemmlowp_multiplier = output_multiplier;
- output_info.gemmlowp_shift = output_shift;
- output_info.gemmlowp_min_bound = min_activation;
- output_info.gemmlowp_max_bound = max_activation;
+ output_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
+ output_info.gemmlowp_offset = uoqinfo.offset;
+ output_info.gemmlowp_min_bound = min_activation;
+ output_info.gemmlowp_max_bound = max_activation;
+ quantization::calculate_quantized_multipliers_less_than_one(iqinfo, wqinfo, oqinfo, output_info);
_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(QuantizationInfo(iqinfo.scale, iqinfo.offset));
- weights->info()->set_quantization_info(QuantizationInfo(wqinfo.scale, wqinfo.offset));
+ input->info()->set_quantization_info(iqinfo);
+ weights->info()->set_quantization_info(wqinfo);
}
else
{
@@ -176,20 +175,10 @@ 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 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(iqinfo.scale, -iqinfo.offset));
- weights_qa->set_quantization_info(QuantizationInfo(wqinfo.scale, -wqinfo.offset));
-
- const UniformQuantizationInfo oqinfo = (output->total_size() == 0) ? iqinfo : output->quantization_info().uniform();
-
- float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale;
- int output_multiplier;
- int output_shift;
- ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift));
+ const QuantizationInfo &iqinfo = input->quantization_info();
+ const QuantizationInfo &wqinfo = weights->quantization_info();
+ const QuantizationInfo &oqinfo = (output->total_size() == 0) ? iqinfo : output->quantization_info();
+ const UniformQuantizationInfo uoqinfo = oqinfo.uniform();
// Merge activation with output stage
int min_activation = 0;
@@ -201,22 +190,25 @@ 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 = quantize_qasymm8(act_info.a(), oqinfo);
- const int b_const_int = quantize_qasymm8(act_info.b(), oqinfo);
+ const int a_const_int = quantize_qasymm8(act_info.a(), uoqinfo);
+ const int b_const_int = quantize_qasymm8(act_info.b(), uoqinfo);
- min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? oqinfo.offset : b_const_int;
+ min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? uoqinfo.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 = oqinfo.offset;
- output_info.gemmlowp_multiplier = output_multiplier;
- output_info.gemmlowp_shift = output_shift;
- output_info.gemmlowp_min_bound = min_activation;
- output_info.gemmlowp_max_bound = max_activation;
+ output_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
+ output_info.gemmlowp_offset = uoqinfo.offset;
+ output_info.gemmlowp_min_bound = min_activation;
+ output_info.gemmlowp_max_bound = max_activation;
+ ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multipliers_less_than_one(iqinfo, wqinfo, oqinfo, output_info));
// Perform validation step on GEMMLowp
+ std::unique_ptr<ITensorInfo> input_qa = input->clone();
+ std::unique_ptr<ITensorInfo> weights_qa = weights->clone();
+ input_qa->set_quantization_info(QuantizationInfo(iqinfo.uniform().scale, -iqinfo.uniform().offset));
+ weights_qa->set_quantization_info(QuantizationInfo(wqinfo.uniform().scale, -wqinfo.uniform().offset));
return NEGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), biases, output, GEMMInfo(false, false, true, gemm_3d_depth, skip_im2col, false, output_info));
}
else
@@ -396,7 +388,7 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!");
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, DataType::QSYMM8_PER_CHANNEL, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_groups > 1, "Grouping (num_groups != 1) is not supported on NEON");
generated by cgit v1.2.1 (git 2.23.0) at 2024-04-25 23:47:46 +0000