COMPMID-784: Added support for biases in WinogradLayer.
1) Updated to the latest code from the RSH repo.
2) Moved winograd transforms into kernels.
3) Added support for biases
Change-Id: I7f39f34a599b49d7d9b549cc10a4f4d4a8007ab8
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/117474
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp
index ea48e1f..e2e4e40 100644
--- a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp
@@ -55,7 +55,7 @@
float *const output, /** Pointer to NHWC ordered output tensor, in the spatial domain. */
float *const winograd_output /** Pointer to working space for the output tensor in the Winograd domain. Must be at least the size returned by `get_output_storage_size`. */
)
- : convolver(n_batches, n_input_channels, n_input_rows, n_input_cols, n_output_channels, same_padding, weights, weights_storage, input, winograd_input, output, winograd_output)
+ : convolver(n_batches, n_input_channels, n_input_rows, n_input_cols, n_output_channels, same_padding, weights, weights_storage, input, winograd_input, nullptr, output, winograd_output)
{
}
T convolver;
@@ -65,24 +65,6 @@
{
}
-void Winograd3x3F32::transform_output()
-{
- auto win = _pimpl->convolver.output_transform.get_window();
- _pimpl->convolver.output_transform.run(0, win);
-}
-
-void Winograd3x3F32::transform_input()
-{
- auto win = _pimpl->convolver.input_transform.get_window();
- _pimpl->convolver.input_transform.run(0, win);
-}
-
-void Winograd3x3F32::transform_weights()
-{
- auto win = _pimpl->convolver.weights_transform.get_window();
- _pimpl->convolver.weights_transform.run(0, win);
-}
-
Winograd3x3F32::Winograd3x3F32(
const int n_batches, /** Number of batches in the input and output tensors. */
const int n_input_channels, /** Number of feature maps in a batch of the input tensor. */
@@ -146,4 +128,128 @@
const size_t last_gemm = window.x().end();
_convolver->_pimpl->convolver.gemms.run(first_gemm, last_gemm);
}
+
+INEWinogradLayerTransformKernel::INEWinogradLayerTransformKernel()
+ : _convolver(nullptr)
+{
+}
+
+void INEWinogradLayerTransformKernel::configure(Winograd3x3F32 *convolver)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(convolver);
+ _convolver = convolver;
+}
+
+// Weights transform
+
+void NEWinogradLayerTransformWeightsKernel::configure(Winograd3x3F32 *convolver)
+{
+ INEWinogradLayerTransformKernel::configure(convolver);
+ Window win;
+ auto win_last = _convolver->_pimpl->convolver.weights_transform.get_window();
+ win.set(Window::DimX, Window::Dimension(0, win_last, 1));
+ INEKernel::configure(win);
+}
+
+void NEWinogradLayerTransformWeightsKernel::run(const Window &window, const ThreadInfo &info)
+{
+ ARM_COMPUTE_UNUSED(info);
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ const size_t fst = window.x().start();
+ const size_t lst = window.x().end();
+ _convolver->_pimpl->convolver.weights_transform.run(fst, lst);
+}
+
+bool NEWinogradLayerTransformWeightsKernel::is_parallelisable() const
+{
+ return false;
+}
+
+// Input transform
+
+void NEWinogradLayerTransformInputKernel::configure(Winograd3x3F32 *convolver)
+{
+ INEWinogradLayerTransformKernel::configure(convolver);
+ Window win;
+ auto win_last = _convolver->_pimpl->convolver.input_transform.get_window();
+ win.set(Window::DimX, Window::Dimension(0, win_last, 1));
+ INEKernel::configure(win);
+}
+
+void NEWinogradLayerTransformInputKernel::run(const Window &window, const ThreadInfo &info)
+{
+ ARM_COMPUTE_UNUSED(info);
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ const size_t fst = window.x().start();
+ const size_t lst = window.x().end();
+ _convolver->_pimpl->convolver.input_transform.run(fst, lst);
+}
+bool NEWinogradLayerTransformInputKernel::is_parallelisable() const
+{
+ return false;
+}
+
+// Output transform
+NEWinogradLayerTransformOutputKernel::NEWinogradLayerTransformOutputKernel()
+ : _biases(nullptr), _output_workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output(nullptr), _n_batches(0), _n_rows(0), _n_cols(0), _n_channels(0)
+{
+}
+
+void NEWinogradLayerTransformOutputKernel::configure(
+ const ITensor *biases,
+ const float *const output_workingspace,
+ const int matrix_stride,
+ float *const output,
+ const int n_batches,
+ const int n_rows,
+ const int n_cols,
+ const int n_channels)
+{
+ using WinogradBase = winograd::WinogradGEMM<2, 2, 3, 3>;
+ using OutputTransform = typename WinogradBase::template OutputTransform<float>;
+
+ _biases = biases;
+ _output_workspace = output_workingspace;
+ _matrix_stride = matrix_stride;
+ _matrix_row_stride = roundup(n_channels, WinogradBase::Convolution<float, float>::N_BLOCK);
+ _output = output;
+ _n_batches = n_batches;
+ _n_rows = n_rows;
+ _n_cols = n_cols;
+ _n_channels = n_channels;
+
+ // We don't have the biases buffer at this stage as it hasn't been allocated, we pass in nullptr OutputTransform is only used here to compute the window
+ OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, nullptr, _output, _n_batches, _n_rows, _n_cols, _n_channels);
+ Window win;
+ auto win_last = output_transform.get_window();
+ win.set(Window::DimX, Window::Dimension(0, win_last, 1));
+ INEKernel::configure(win);
+}
+
+void NEWinogradLayerTransformOutputKernel::run(const Window &window, const ThreadInfo &info)
+{
+ ARM_COMPUTE_UNUSED(info);
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(_biases->buffer());
+ ARM_COMPUTE_ERROR_ON_NULLPTR(_output_workspace);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(_output);
+
+ using WinogradBase = winograd::WinogradGEMM<2, 2, 3, 3>;
+ using OutputTransform = typename WinogradBase::template OutputTransform<float>;
+
+ OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride,
+ reinterpret_cast<float *>(_biases->buffer()), _output,
+ _n_batches, _n_rows, _n_cols, _n_channels);
+
+ // The code below cannot be moved to configure because biases hasn't been allocated at that point
+ const size_t fst = window.x().start();
+ const size_t lst = window.x().end();
+ output_transform.run(fst, lst);
+}
+
+bool NEWinogradLayerTransformOutputKernel::is_parallelisable() const
+{
+ return false;
+}
+
} // namespace arm_compute