arm_compute v17.09
Change-Id: I4bf8f4e6e5f84ce0d5b6f5ba570d276879f42a81
diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
index bd688cf..40862fc 100644
--- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
@@ -23,32 +23,41 @@
*/
#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"
+#include "arm_compute/core/NEON/kernels/arm32/NEGEMMAArch32Kernel.h"
+#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64Kernel.h"
#include "arm_compute/core/PixelValue.h"
+#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
+#include "support/ToolchainSupport.h"
+
+namespace arm_compute
+{
+#include "arm_compute/core/NEON/kernels/assembly/gemm_interleaved.hpp"
+#include "arm_compute/core/NEON/kernels/assembly/kernels/a32_sgemm_8x6.hpp"
+#include "arm_compute/core/NEON/kernels/assembly/kernels/a64_sgemm_12x8.hpp"
+} // namespace arm_compute
#include <cmath>
#include <tuple>
-using namespace arm_compute;
-
-NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights()
- : _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
+namespace arm_compute
+{
+NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
+ : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
{
}
void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F32);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, 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_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::QS8, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases);
ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
@@ -69,8 +78,11 @@
TensorInfo info_wr(shape_wr, 1, weights->info()->data_type(), weights->info()->fixed_point_position());
_weights_reshaped.allocator()->init(info_wr);
+ _memory_group.manage(&_weights_reshaped);
+
_weights_reshape_kernel.configure(weights, biases, &_weights_reshaped);
_weights_transposed_kernel.configure(&_weights_reshaped, output);
+
_weights_reshaped.allocator()->allocate();
}
else
@@ -81,32 +93,34 @@
void NEConvolutionLayerReshapeWeights::run()
{
+ _memory_group.acquire();
+
NEScheduler::get().schedule(&_weights_reshape_kernel, 3);
+
if(_transpose1xW)
{
NEScheduler::get().schedule(&_weights_transposed_kernel, Window::DimY);
}
+
+ _memory_group.release();
}
-NEConvolutionLayer::NEConvolutionLayer()
- : _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(),
- _gemm_output(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
+NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
+ : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _output_col2im_kernel(),
+ _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _workspace(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
{
}
void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F32);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights, output);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, 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);
if(biases != nullptr)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::QS8, DataType::F32);
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));
@@ -131,94 +145,165 @@
unsigned int conv_w = 0;
unsigned int conv_h = 0;
- const unsigned int kernel_width = (_are_weights_reshaped) ? weights_info.kernel_size() : weights->info()->dimension(0);
- std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width,
- stride_x, stride_y, pad_x, pad_y, conv_info.round());
- 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");
+ const unsigned int kernel_width = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0);
+ const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1);
+ std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
+ conv_info);
- // Check if its a "fully connected" convolution
+ // Check if its a "fully connected" convolution, i.e. the output size is 1x1xnum_kernels
_is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+#if defined(__arm__)
+ if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
+ {
+ _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch32Kernel>();
+ }
+#elif defined(__aarch64__)
+ if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
+ {
+ _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64Kernel>();
+ }
+#endif /* defined(__arm__) || defined(__aarch64__) */
+
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);
// Reshape weights if needed
- if(_are_weights_reshaped)
+ if(_mm_optimised_kernel != nullptr)
{
- mat_weights_cols = output->info()->dimension(2);
- const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4;
- mat_weights_rows = (_has_bias ? 1 + quarter_reshaped_cols : quarter_reshaped_cols);
- }
- else
- {
- if(_is_fully_connected_convolution)
+ if(_are_weights_reshaped)
{
- // 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 */);
+ mat_weights_cols = weights_info.num_kernels();
+ mat_weights_rows = weights->info()->dimension(1);
}
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 */);
+ TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
+
+ // Create tensor to store the reshaped weights
+ _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
+ _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */);
+ weights = &_weights_reshaped;
}
- weights = &_weights_reshaped;
+ }
+ else
+ {
+ if(_are_weights_reshaped)
+ {
+ mat_weights_cols = weights_info.num_kernels();
+ mat_weights_rows = weights->info()->dimension(0) / 4 + (_has_bias ? 1 : 0);
+ }
+ else
+ {
+ TensorShape reshaped_weights_shape;
+
+ if(_is_fully_connected_convolution)
+ {
+ reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
+ }
+ else
+ {
+ // Create tensor to store transposed weights
+ const float transpose_width = 16.0f / input->info()->element_size();
+ reshaped_weights_shape = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
+ static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
+ }
+
+ // Create tensor to store the reshaped weights
+ _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
+ _reshape_weights.configure(weights, biases, &_weights_reshaped, !_is_fully_connected_convolution /* 1xW transpose */);
+ weights = &_weights_reshaped;
+ }
}
// Create tensor to store im2col reshaped inputs
const unsigned int mat_input_cols = mat_weights_rows;
const unsigned int mat_input_rows = conv_w * conv_h;
- TensorShape shape_im2col = input->info()->tensor_shape();
+
+ TensorShape shape_im2col(input->info()->tensor_shape());
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));
+ _memory_group.manage(&_input_im2col_reshaped);
// Create tensor (interleave) to prepare input tensor for GEMM
- if(!_is_fully_connected_convolution)
+ if(!_is_fully_connected_convolution && _mm_optimised_kernel == nullptr)
{
- TensorShape shape_interleaved = shape_im2col;
+ 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));
+ _memory_group.manage(&_input_interleaved_reshaped);
}
// Create GEMM output tensor
- TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
+ 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));
+ _memory_group.manage(&_gemm_output);
// Configure kernels
- _input_im2col_kernel.configure(input, &_input_im2col_reshaped, std::make_pair(conv_w, conv_h), conv_info, _has_bias);
- if(_is_fully_connected_convolution)
+ _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias);
+
+#if defined(__arm__) || defined(__aarch64__)
+ if(_mm_optimised_kernel != nullptr)
{
- _mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f);
+ struct CPUInfo ci = NEScheduler::get().cpu_info();
+
+ const int M = _gemm_output.info()->tensor_shape().y();
+ const int N = _gemm_output.info()->tensor_shape().x();
+ const int K = _input_im2col_reshaped.info()->tensor_shape().x();
+
+#if defined(__arm__)
+ GemmInterleaved<sgemm_8x6, float, float> gemm(&ci, M, N, K, false, false);
+#elif defined(__aarch64__)
+ GemmInterleaved<sgemm_12x8, float, float> gemm(&ci, M, N, K, false, false);
+#endif /* defined(__arm__) || defined(__aarch64__) */
+
+ constexpr size_t alignment = 4096;
+ _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8));
+ _memory_group.manage(&_workspace);
+
+ // Configure matrix multiplication kernel
+ if(_is_fully_connected_convolution)
+ {
+ _mm_optimised_kernel->configure(&_input_im2col_reshaped, weights, &_gemm_output, &_workspace, 1.f, 0.f, false, false);
+ }
+ else
+ {
+ _mm_optimised_kernel->configure(&_input_im2col_reshaped, weights, &_gemm_output, &_workspace);
+ }
+
+ _workspace.allocator()->allocate();
}
else
+#endif /* defined(__arm__) || defined(__aarch64__) */
{
- _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
- _mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f);
+ if(_is_fully_connected_convolution)
+ {
+ _mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f);
+ }
+ 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();
+ }
}
+
+ _input_im2col_reshaped.allocator()->allocate();
_output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h));
+ _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");
// Allocate intermediate tensor
if(!_are_weights_reshaped)
{
_weights_reshaped.allocator()->allocate();
}
- _input_im2col_reshaped.allocator()->allocate();
- if(!_is_fully_connected_convolution)
- {
- _input_interleaved_reshaped.allocator()->allocate();
- }
- _gemm_output.allocator()->allocate();
}
void NEConvolutionLayer::run()
@@ -230,17 +315,30 @@
_reshape_weights.run();
}
+ _memory_group.acquire();
+
// Run input reshaping
NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY);
- if(!_is_fully_connected_convolution)
- {
- // Run interleave
- NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY);
- }
// Runs matrix multiply on reshaped matrices
- NEScheduler::get().schedule(&_mm_kernel, Window::DimY);
+ if(_mm_optimised_kernel != nullptr)
+ {
+ NEScheduler::get().schedule(_mm_optimised_kernel.get(), Window::DimY);
+ }
+ else
+ {
+ if(!_is_fully_connected_convolution)
+ {
+ // Run interleave
+ NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY);
+ }
+
+ NEScheduler::get().schedule(&_mm_kernel, Window::DimY);
+ }
// Reshape output matrix
NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);
+
+ _memory_group.release();
}
+} // namespace arm_compute