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path: root/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
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Diffstat (limited to 'src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp')
-rw-r--r--src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp112
1 files changed, 64 insertions, 48 deletions
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index 831f108b85..d322723150 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -66,13 +66,14 @@ void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const
Status CLConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, unsigned int num_groups)
{
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)
{
const int idx_kernels = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::BATCHES);
- ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(weights->data_type()));
+ ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized(weights->data_type()));
+
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(idx_kernels));
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
@@ -81,7 +82,6 @@ Status CLConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, co
if((output != nullptr) && (output->total_size() != 0))
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
-
CLWeightsReshapeKernel::validate(weights, biases, output, num_groups);
}
@@ -201,9 +201,9 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
const unsigned int kernel_width = weights->info()->dimension(idx_width);
const unsigned int kernel_height = weights->info()->dimension(idx_height);
+ const unsigned int num_kernels = weights->info()->dimension(idx_kernels);
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();
_is_prepared = weights_info.retain_internal_weights();
@@ -237,7 +237,7 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
conv_info,
dilation);
- unsigned int mat_weights_cols = weights->info()->dimension(idx_kernels) / num_groups;
+ unsigned int mat_weights_cols = num_kernels / num_groups;
const ICLTensor *biases_to_use = biases;
bool append_bias = false;
@@ -310,20 +310,28 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
}
GEMMLowpOutputStageInfo gemmlowp_output_stage;
- gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
- gemmlowp_output_stage.gemmlowp_offset = 0;
- gemmlowp_output_stage.gemmlowp_multiplier = 0;
- gemmlowp_output_stage.gemmlowp_shift = 0;
+ gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
+ gemmlowp_output_stage.gemmlowp_offset = 0;
// Configure output stage for quantized case
if(_is_quantized)
{
- const auto output_quant_info = (output->info()->total_size() == 0) ? iq_info : oq_info;
-
- const float multiplier = (iq_info.scale * wq_info.scale) / output_quant_info.scale;
- int output_multiplier = 0;
- int output_shift = 0;
- quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+ const auto output_quant_info = (output->info()->total_size() == 0) ? iq_info : oq_info;
+ const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->info()->data_type());
+ const unsigned int num_filters = (is_quantized_per_channel) ? num_kernels : 1;
+
+ gemmlowp_output_stage.is_quantized_per_channel = is_quantized_per_channel;
+
+ gemmlowp_output_stage.gemmlowp_multipliers.resize(num_filters);
+ gemmlowp_output_stage.gemmlowp_shifts.resize(num_filters);
+ quantization::compute_quantized_multipliers_and_shifts(input->info(),
+ weights->info(),
+ output->info(),
+ idx_kernels,
+ gemmlowp_output_stage.gemmlowp_multipliers.data(),
+ gemmlowp_output_stage.gemmlowp_shifts.data());
+ gemmlowp_output_stage.gemmlowp_multiplier = gemmlowp_output_stage.gemmlowp_multipliers[0];
+ gemmlowp_output_stage.gemmlowp_shift = gemmlowp_output_stage.gemmlowp_shifts[0];
int min_activation = 0;
int max_activation = 0;
@@ -350,11 +358,9 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
}
// Set the GEMMLowp output stage info
- gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset;
- gemmlowp_output_stage.gemmlowp_multiplier = output_multiplier;
- gemmlowp_output_stage.gemmlowp_shift = output_shift;
- gemmlowp_output_stage.gemmlowp_min_bound = min_activation;
- gemmlowp_output_stage.gemmlowp_max_bound = max_activation;
+ gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset;
+ gemmlowp_output_stage.gemmlowp_min_bound = min_activation;
+ gemmlowp_output_stage.gemmlowp_max_bound = max_activation;
}
// Configure and tune GEMM
@@ -396,8 +402,17 @@ Status CLGEMMConvolutionLayer::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(input, 1, DataType::QASYMM8, DataType::QSYMM8_PER_CHANNEL, DataType::F16, DataType::F32);
+ const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->data_type());
+
+ if(is_quantized_per_channel)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() != DataType::QASYMM8, "Input data type not compatible with Weights");
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+ }
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights);
ARM_COMPUTE_RETURN_ERROR_ON_MSG((num_groups != 1) && (input->data_layout() != DataLayout::NCHW), "Grouping (num_groups != 1) with NHWC data layout is not supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG((num_groups != 1) && (input->data_type() == DataType::QASYMM8), "Grouping (num_groups != 1) is not supported with QASYMM8");
@@ -412,6 +427,7 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
const unsigned int kernel_width = weights->dimension(idx_width);
const unsigned int kernel_height = weights->dimension(idx_height);
+ const unsigned int num_kernels = weights->dimension(idx_kernels);
TensorInfo im2col_reshaped_info{};
TensorInfo info_gemm{};
@@ -419,15 +435,10 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
const ITensorInfo *gemm_input_to_use = input;
const ITensorInfo *gemm_output_to_use = output;
const ITensorInfo *weights_to_use = weights;
-
- const bool is_quantized = is_data_type_quantized_asymmetric(data_type);
- const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1);
- const bool skip_col2im = data_layout == DataLayout::NHWC;
- bool fuse_activation = true;
-
- const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
- const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = output->quantization_info().uniform();
+ const bool is_quantized = is_data_type_quantized_asymmetric(data_type);
+ const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1);
+ const bool skip_col2im = data_layout == DataLayout::NHWC;
+ bool fuse_activation = true;
ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(idx_channel) * num_groups) != input->dimension(idx_channel));
ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
@@ -463,7 +474,7 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
conv_info,
dilation);
- unsigned int mat_weights_cols = weights->dimension(idx_kernels) / num_groups;
+ unsigned int mat_weights_cols = num_kernels / num_groups;
const ITensorInfo *biases_to_use = biases;
bool append_bias = false;
@@ -514,20 +525,27 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
}
GEMMLowpOutputStageInfo gemmlowp_output_stage;
- gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
- gemmlowp_output_stage.gemmlowp_offset = 0;
- gemmlowp_output_stage.gemmlowp_multiplier = 0;
- gemmlowp_output_stage.gemmlowp_shift = 0;
+ gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
+ gemmlowp_output_stage.gemmlowp_offset = 0;
+ gemmlowp_output_stage.is_quantized_per_channel = is_quantized_per_channel;
if(is_quantized)
{
- const auto output_quant_info = (output->total_size() == 0) ? iq_info : oq_info;
-
- const float multiplier = (iq_info.scale * wq_info.scale) / output_quant_info.scale;
- int output_multiplier = 0;
- int output_shift = 0;
-
- ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift));
+ const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
+ const UniformQuantizationInfo oq_info = output->quantization_info().uniform();
+ const auto output_quant_info = (output->total_size() == 0) ? iq_info : oq_info;
+ const unsigned int num_filters = (is_quantized_per_channel) ? num_kernels : 1;
+
+ gemmlowp_output_stage.gemmlowp_multipliers.resize(num_filters);
+ gemmlowp_output_stage.gemmlowp_shifts.resize(num_filters);
+ quantization::compute_quantized_multipliers_and_shifts(input,
+ weights,
+ output,
+ idx_kernels,
+ gemmlowp_output_stage.gemmlowp_multipliers.data(),
+ gemmlowp_output_stage.gemmlowp_shifts.data());
+ gemmlowp_output_stage.gemmlowp_multiplier = gemmlowp_output_stage.gemmlowp_multipliers[0];
+ gemmlowp_output_stage.gemmlowp_shift = gemmlowp_output_stage.gemmlowp_shifts[0];
int min_activation = 0;
int max_activation = 0;
@@ -554,11 +572,9 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
}
// Set the GEMMLowp output stage info
- gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset;
- gemmlowp_output_stage.gemmlowp_multiplier = output_multiplier;
- gemmlowp_output_stage.gemmlowp_shift = output_shift;
- gemmlowp_output_stage.gemmlowp_min_bound = min_activation;
- gemmlowp_output_stage.gemmlowp_max_bound = max_activation;
+ gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset;
+ gemmlowp_output_stage.gemmlowp_min_bound = min_activation;
+ gemmlowp_output_stage.gemmlowp_max_bound = max_activation;
}
// In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-02 13:09:51 +0000