COMPMID-2063: New Winograd implementation
Refactoring of winograd code reducing the size of the binaries
about 8X.
Change-Id: If8845bda324573e1a5cf436f354ac8603e88a92e
Signed-off-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/959
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Anthony Barbier <Anthony.barbier@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
index 1d92471..b4247be 100644
--- a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
@@ -33,7 +33,7 @@
#include "arm_compute/runtime/NEON/functions/NEGEMMAssemblyDispatch.h"
#include "support/ToolchainSupport.h"
-#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
+#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd.hpp"
namespace arm_compute
{
@@ -236,10 +236,10 @@
NEWinogradConvolutionLayer::NEWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(memory_manager), _gemm_function(memory_manager), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr), _activationlayer_function(),
- _permute_input(), _permute_weights(), _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(), _weights_hwio(), _input(), _weights(), _output(),
- _is_prepared(false), _is_activationlayer_enabled(false)
+ _permute_input(), _permute_weights(), _permute_output(), _input_transformed(), _output_transformed(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(),
+ _weights_hwio(), _input(), _weights(), _output(), _is_prepared(false), _is_activationlayer_enabled(false)
{
-} /* arm_compute */
+}
void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info,
bool enable_fast_math)
@@ -436,9 +436,9 @@
b_info.init(b_shape, 1, data_type, b_strides, 0, kernel_storage_size);
d_info.init(d_shape, 1, data_type, d_strides, 0, output_storage_size);
- _input_workspace.allocator()->init(a_info, storage_alignment);
+ _input_transformed.allocator()->init(a_info, storage_alignment);
_kernel_storage.allocator()->init(b_info, storage_alignment);
- _output_workspace.allocator()->init(d_info, storage_alignment);
+ _output_transformed.allocator()->init(d_info, storage_alignment);
// configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
TensorInfo info(TensorShape(_output->info()->dimension(2), _output->info()->dimension(0),
@@ -447,6 +447,8 @@
_output_nhwc.allocator()->init(info);
// Configure the InputTransform
+ _memory_group.manage(&_input_transformed);
+ _memory_group.manage(&_output_transformed);
_memory_group.manage(&_input_workspace);
_memory_group.manage(&_output_workspace);
@@ -456,7 +458,7 @@
_permute_input.configure(input, &_input_nhwc, PermutationVector(2U, 0U, 1U));
_input_nhwc.allocator()->allocate();
transform_input_kernel->configure(&_input_nhwc, in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type,
- &_input_workspace, input_matrix_stride);
+ &_input_transformed, input_matrix_stride, &_input_workspace);
// Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
_permute_weights.configure(weights, &_weights_hwio, PermutationVector(3U, 2U, 0U, 1U));
@@ -465,26 +467,39 @@
//The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method
_memory_group.manage(&_output_nhwc);
- transform_output_kernel->configure(biases, &_output_workspace,
+ transform_output_kernel->configure(biases, &_output_transformed,
output_matrix_stride, &_output_nhwc,
- in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels);
+ in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels, &_output_workspace);
}
else
{
transform_input_kernel->configure(_input, in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type,
- &_input_workspace, input_matrix_stride);
+ &_input_transformed, input_matrix_stride, &_input_workspace);
// Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
_permute_weights.configure(weights, &_weights_hwio, PermutationVector(3U, 0U, 1U, 2U));
transform_weights_kernel->configure(&_weights_hwio, &_kernel_storage, kernel_matrix_stride, out_channels, in_channels);
- transform_output_kernel->configure(biases, &_output_workspace,
+ transform_output_kernel->configure(biases, &_output_transformed,
output_matrix_stride, _output,
- in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels);
+ in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels, &_output_workspace);
}
- _gemm_function.configure(&_input_workspace, &_kernel_storage, nullptr, &_output_workspace, 1.0f, 0.f);
+ //Configure input/output workspaces, get_working_space_size() must be called after configure()
+ const unsigned int max_num_threads = NEScheduler::get().num_threads_hint();
+ const size_t input_workspace_size = transform_input_kernel->get_working_space_size(max_num_threads);
+ const size_t output_workspace_size = transform_output_kernel->get_working_space_size(max_num_threads);
+
+ TensorInfo input_workspace_info(TensorShape(input_workspace_size), 1, _input->info()->data_type());
+ _input_workspace.allocator()->init(input_workspace_info);
+
+ TensorInfo output_workspace_info(TensorShape(output_workspace_size), 1, _output->info()->data_type());
+ _output_workspace.allocator()->init(output_workspace_info);
+
+ _gemm_function.configure(&_input_transformed, &_kernel_storage, nullptr, &_output_transformed, 1.0f, 0.f);
+ _input_transformed.allocator()->allocate();
+ _output_transformed.allocator()->allocate();
_input_workspace.allocator()->allocate();
_output_workspace.allocator()->allocate();