arm_compute v17.12
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
index 4b1bfd8..0ed3351 100644
--- a/src/runtime/CL/functions/CLConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp
@@ -27,6 +27,7 @@
#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
#include <cmath>
@@ -42,19 +43,22 @@
void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
if(biases != nullptr)
{
+ ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(weights->info()->data_type()));
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
}
- const bool _has_bias = (biases != nullptr);
+ const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
+ const unsigned bias_element = (append_biases) ? 1 : 0;
+ const ICLTensor *biases_to_use = (append_biases) ? biases : nullptr;
_transpose1xW = transpose1xW;
@@ -62,7 +66,7 @@
{
// Create tensor to store the reshaped weights
const unsigned int mat_weights_cols = weights->info()->dimension(3);
- const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0);
+ const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
TensorShape shape_wr(mat_weights_cols, mat_weights_rows);
const DataType dt = weights->info()->data_type();
const int fixed_point_position = weights->info()->fixed_point_position();
@@ -70,13 +74,13 @@
_weights_reshaped.allocator()->init(info_wr);
_memory_group.manage(&_weights_reshaped);
- _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped);
+ _weights_reshape_kernel.configure(weights, biases_to_use, &_weights_reshaped);
_weights_transposed_kernel.configure(&_weights_reshaped, output);
_weights_reshaped.allocator()->allocate();
}
else
{
- _weights_reshape_kernel.configure(weights, biases, output);
+ _weights_reshape_kernel.configure(weights, biases_to_use, output);
}
}
@@ -95,43 +99,77 @@
}
CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(),
- _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
+ : _memory_group(memory_manager), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _output_col2im_kernel(),
+ _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false),
+ _are_weights_reshaped(false), _is_quantized(false)
{
}
+void CLConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed)
+{
+ if(_is_quantized)
+ {
+ // 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();
+
+ 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));
+
+ _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));
+
+ // 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);
+ }
+ else
+ {
+ _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed);
+ }
+}
+
void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
+ ARM_COMPUTE_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->info()->data_type()));
+
+ _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
if(biases != nullptr)
{
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+ if(_is_quantized)
+ {
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+ }
ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3));
ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
}
- const DataType dt = input->info()->data_type();
- const int fixed_point_position = input->info()->fixed_point_position();
+ const DataType dt = input->info()->data_type();
// Set the GPU target for matrix multiply
_mm_kernel.set_target(CLScheduler::get().target());
- _has_bias = (biases != nullptr);
+ _append_bias = (biases != nullptr) && (!_is_quantized);
_are_weights_reshaped = weights_info.are_reshaped();
+ const unsigned bias_element = (_append_bias) ? 1 : 0;
+ const ICLTensor *biases_to_use = (_append_bias) ? biases : nullptr;
+
// Get parameters from conv_info
unsigned int stride_x = 0;
unsigned int stride_y = 0;
- unsigned int pad_x = 0;
- unsigned int pad_y = 0;
std::tie(stride_x, stride_y) = conv_info.stride();
- std::tie(pad_x, pad_y) = conv_info.pad();
// Get convolved dimensions
unsigned int conv_w = 0;
@@ -144,36 +182,44 @@
// Check if its a "fully connected" convolution
_is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+ const bool run_interleaved = (!_is_fully_connected_convolution && !_is_quantized);
unsigned int mat_weights_cols = weights->info()->dimension(3);
- unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0);
+ unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
// Reshape weights if needed
if(_are_weights_reshaped)
{
- mat_weights_cols = weights_info.num_kernels();
- const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4;
- mat_weights_rows = (_has_bias ? 1 + quarter_reshaped_cols : quarter_reshaped_cols);
+ if(_is_fully_connected_convolution || _is_quantized)
+ {
+ mat_weights_cols = weights->info()->dimension(0);
+ mat_weights_rows = weights->info()->dimension(1);
+ }
+ else
+ {
+ mat_weights_cols = weights_info.num_kernels();
+ const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4;
+ mat_weights_rows = quarter_reshaped_cols + bias_element;
+ }
}
else
{
- if(_is_fully_connected_convolution)
+ if(_is_fully_connected_convolution || _is_quantized)
{
// Create tensor to store the reshaped weights
TensorShape shape_wr(mat_weights_cols, mat_weights_rows);
- TensorInfo info_wr(shape_wr, 1, dt, fixed_point_position);
- _weights_reshaped.allocator()->init(info_wr);
- _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */);
+ _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wr));
+ _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false /* 1xW transpose */);
}
else
{
// Create tensor to store transposed weights
const float transpose_width = 16.0f / input->info()->element_size();
TensorShape shape_wt(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
- TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position);
- _weights_reshaped.allocator()->init(info_wt);
- _reshape_weights.configure(weights, biases, &_weights_reshaped, true /* 1xW transpose */);
+ _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wt));
+ _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, true /* 1xW transpose */);
}
+ _weights_reshaped.info()->set_quantization_info(weights->info()->quantization_info());
weights = &_weights_reshaped;
}
@@ -184,16 +230,20 @@
shape_im2col.set(0, mat_input_cols);
shape_im2col.set(1, mat_input_rows);
shape_im2col.set(2, 1);
- _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position));
+ TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->info()->fixed_point_position());
+ im2col_reshaped_info.set_quantization_info(input->info()->quantization_info());
+ _input_im2col_reshaped.allocator()->init(im2col_reshaped_info);
_memory_group.manage(&_input_im2col_reshaped);
// Create tensor (interleave) to prepare input tensor for GEMM
- if(!_is_fully_connected_convolution)
+ if(run_interleaved)
{
TensorShape shape_interleaved = shape_im2col;
shape_interleaved.set(0, shape_interleaved.x() * 4);
shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
- _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position));
+ TensorInfo interleaved_info(shape_interleaved, 1, dt, input->info()->fixed_point_position());
+ interleaved_info.set_quantization_info(input->info()->quantization_info());
+ _input_interleaved_reshaped.allocator()->init(interleaved_info);
_memory_group.manage(&_input_interleaved_reshaped);
}
@@ -201,27 +251,51 @@
TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
shape_gemm.set(0, mat_weights_cols);
shape_gemm.set(1, mat_input_rows);
- _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, dt, fixed_point_position));
+ const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt;
+ // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
+ TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
+ info_gemm.set_quantization_info(output->info()->quantization_info());
+ _gemm_output.allocator()->init(info_gemm);
_memory_group.manage(&_gemm_output);
// Configure kernels
- _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias);
+ _input_im2col_kernel.set_target(CLScheduler::get().target());
+ _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias);
// Configure matrix multiply
- if(_is_fully_connected_convolution)
+ if(run_interleaved)
{
- // The matrix A and Matrix B have not been reshaped
- _mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f, false);
+ _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
+ configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output);
+ _input_interleaved_reshaped.allocator()->allocate();
}
else
{
- _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
- _mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f);
- _input_interleaved_reshaped.allocator()->allocate();
+ configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, false);
}
_input_im2col_reshaped.allocator()->allocate();
- _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h));
- _gemm_output.allocator()->allocate();
+
+ // Configure output stage for quantized case
+ if(_is_quantized)
+ {
+ float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output->info()->quantization_info().scale;
+ int output_multiplier, output_shift;
+ quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+ _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output->info()->quantization_info().offset);
+ _gemm_output.allocator()->allocate();
+ }
+
+ // Configure Col2Im
+ _output_col2im_kernel.set_target(CLScheduler::get().target());
+ _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h));
+ if(_is_quantized)
+ {
+ _tmp_output.allocator()->allocate();
+ }
+ else
+ {
+ _gemm_output.allocator()->allocate();
+ }
ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
@@ -243,15 +317,30 @@
_memory_group.acquire();
- // Run input reshaping
+ // Run im2col
CLScheduler::get().enqueue(_input_im2col_kernel);
- if(!_is_fully_connected_convolution)
+
+ if(!_is_fully_connected_convolution && !_is_quantized)
{
+ // Run interleave4x4
CLScheduler::get().enqueue(_input_interleave_kernel);
}
// Runs matrix multiply on reshaped matrices
- CLScheduler::get().enqueue(_mm_kernel);
+ if(_is_quantized)
+ {
+ _mm_gemmlowp.run();
+ }
+ else
+ {
+ CLScheduler::get().enqueue(_mm_kernel);
+ }
+
+ // Run output stage for quantized case
+ if(_is_quantized)
+ {
+ _gemmlowp_output_stage.run();
+ }
// Reshape output matrix
CLScheduler::get().enqueue(_output_col2im_kernel, false);