diff options
Diffstat (limited to 'src')
9 files changed, 1275 insertions, 122 deletions
diff --git a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp index 68064ee7c7..3100bf7308 100644 --- a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp +++ b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp @@ -41,23 +41,34 @@ namespace arm_compute namespace { -inline bool is_kernel_size_supported(Size2D size) +inline bool is_kernel_size_supported(DataType data_type, Size2D size) { - const std::array<Size2D, 8> supported_input_sizes = { { Size2D(1, 3), Size2D(3, 1), Size2D(5, 5), Size2D(3, 3), Size2D(1, 5), Size2D(5, 1), Size2D(7, 1), Size2D(1, 7) } }; - return std::end(supported_input_sizes) != std::find(std::begin(supported_input_sizes), std::end(supported_input_sizes), size); + const std::array<Size2D, 8> f32_support = { { Size2D(1, 3), Size2D(3, 1), Size2D(5, 5), Size2D(3, 3), Size2D(1, 5), Size2D(5, 1), Size2D(7, 1), Size2D(1, 7) } }; + const std::array<Size2D, 8> f16_support = { { Size2D(3, 3) } }; + + switch(data_type) + { + case DataType::F16: + return std::end(f16_support) != std::find(std::begin(f16_support), std::end(f16_support), size); + case DataType::F32: + return std::end(f32_support) != std::find(std::begin(f32_support), std::end(f32_support), size); + default: + return false; + } } Status validate_arguments_winograd_weight_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); const size_t idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); const auto input_width = input->dimension(idx_width); const auto input_height = input->dimension(idx_height); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(Size2D(input_width, input_height)), "Only 1x3, 3x1, 1x5, 5x1, 7x1, 1x7, 3x3 and 5x5 kernels are supported"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(input_width, input_height)), + "Only 1x3, 3x1, 1x5, 5x1, 7x1, 1x7, 3x3 and 5x5 kernels are supported"); ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4); const Size2D &output_tile = winograd_info.output_tile_size; const std::array<Size2D, 8> supported_tile_sizes = { { Size2D(2U, 2U), Size2D(4U, 4U), Size2D(1U, 6U), Size2D(6U, 1U), Size2D(4, 1), Size2D(1, 4), Size2D(2, 1), Size2D(1, 2) } }; @@ -89,9 +100,9 @@ Status validate_arguments_winograd_input_trans(const ITensorInfo *input, const I const PadStrideInfo &conv_info = winograd_info.convolution_info; ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd input transform only supports unit strides"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(Size2D(kernel_dims.width, kernel_dims.height)), + ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(kernel_dims.width, kernel_dims.height)), "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported"); // Validate configured output @@ -128,9 +139,9 @@ Status validate_arguments_winograd_output_trans(const ITensorInfo *input, const ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != num_tiles.area()); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(Size2D(kernel_dims.width, kernel_dims.height)), + ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(kernel_dims.width, kernel_dims.height)), "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported"); const std::array<unsigned int, 3> supported_gemm_sizes = { { 8U, 16U, 36U } }; @@ -162,22 +173,19 @@ std::pair<Status, Window> validate_and_configure_window_winograd_output_trans(IT } } // namespace -template <typename T> -Status INEWinogradLayerTransformWeightsKernel<T>::validate(const ITensorInfo *input, const ITensorInfo *weights) +Status INEWinogradLayerTransformWeightsKernel::validate(const ITensorInfo *input, const ITensorInfo *weights) { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); const DataLayout data_layout = input->data_layout(); const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(Size2D(weights->dimension(width_idx), weights->dimension(height_idx))), + ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(weights->dimension(width_idx), weights->dimension(height_idx))), "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported"); ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); return Status{}; } -template class INEWinogradLayerTransformWeightsKernel<float>; - template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int num_output_channels, int num_input_channels) const { @@ -262,6 +270,11 @@ template class NEWinogradLayerTransformWeightsKernel<float, 1, 4, 1, 5>; template class NEWinogradLayerTransformWeightsKernel<float, 4, 1, 5, 1>; template class NEWinogradLayerTransformWeightsKernel<float, 1, 2, 1, 7>; template class NEWinogradLayerTransformWeightsKernel<float, 2, 1, 7, 1>; + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +template class NEWinogradLayerTransformWeightsKernel<__fp16, 4, 4, 3, 3>; +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + // Input transform template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> @@ -396,6 +409,10 @@ template class NEWinogradLayerTransformInputKernel<float, 4, 1, 5, 1>; template class NEWinogradLayerTransformInputKernel<float, 1, 2, 1, 7>; template class NEWinogradLayerTransformInputKernel<float, 2, 1, 7, 1>; +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +template class NEWinogradLayerTransformInputKernel<__fp16, 4, 4, 3, 3>; +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + // Output transform template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> @@ -524,4 +541,7 @@ template class NEWinogradLayerTransformOutputKernel<float, 4, 1, 5, 1>; template class NEWinogradLayerTransformOutputKernel<float, 1, 2, 1, 7>; template class NEWinogradLayerTransformOutputKernel<float, 2, 1, 7, 1>; +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +template class NEWinogradLayerTransformOutputKernel<__fp16, 4, 4, 3, 3>; +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC } // namespace arm_compute diff --git a/src/core/NEON/kernels/convolution/winograd/padding.cpp b/src/core/NEON/kernels/convolution/winograd/padding.cpp index 46fe57c7c9..04aa472c3c 100644 --- a/src/core/NEON/kernels/convolution/winograd/padding.cpp +++ b/src/core/NEON/kernels/convolution/winograd/padding.cpp @@ -85,6 +85,15 @@ template void copy_and_pad_tile( unsigned int, unsigned int, unsigned int, unsigned int, float ); +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +template void copy_and_pad_tile( + unsigned int, unsigned int, unsigned int, + const __fp16 *, unsigned int, unsigned int, + __fp16 *, unsigned int, unsigned int, + unsigned int, unsigned int, unsigned int, unsigned int, __fp16 +); +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + template <unsigned int TileRows, unsigned int TileCols> void CopyCropped<TileRows, TileCols>::execute( const size_t size, @@ -163,4 +172,21 @@ template void crop_and_copy_tile( unsigned int crop_right ); +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +template void crop_and_copy_tile( + unsigned int tile_rows, + unsigned int tile_cols, + unsigned int n_channels, + const __fp16 *inptr, + unsigned int in_row_stride, + unsigned int in_col_stride, + __fp16 *outptr, + unsigned int out_row_stride, + unsigned int out_col_stride, + unsigned int crop_top, + unsigned int crop_left, + unsigned int crop_bottom, + unsigned int crop_right +); +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC } // namespace padding diff --git a/src/core/NEON/kernels/convolution/winograd/winograd.cpp b/src/core/NEON/kernels/convolution/winograd/winograd.cpp index a4eb9fce59..867bb3c7f3 100644 --- a/src/core/NEON/kernels/convolution/winograd/winograd.cpp +++ b/src/core/NEON/kernels/convolution/winograd/winograd.cpp @@ -176,3 +176,7 @@ template class WinogradGEMM<4, 1, 5, 1, WinogradRoots::Integers>::Convolution<fl template class WinogradGEMM<1, 2, 1, 7, WinogradRoots::Integers>::Convolution<float, float, float, float>; template class WinogradGEMM<2, 1, 7, 1, WinogradRoots::Integers>::Convolution<float, float, float, float>; + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +template class WinogradGEMM<4, 4, 3, 3, WinogradRoots::Integers>::Convolution<__fp16, __fp16, __fp16, __fp16>; +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input_4x4_fp16_fp16_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input_4x4_fp16_fp16_integers.cpp new file mode 100644 index 0000000000..1ea68b5938 --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input_4x4_fp16_fp16_integers.cpp @@ -0,0 +1,257 @@ +/* + * Copyright (c) 2020 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + +#include "input.hpp" +#include "arm.hpp" + +namespace winograd +{ + +template <> +void InputTransform<4, 4, __fp16, __fp16, WinogradRoots::Integers>::transform_tile( + const int n_channels, + const __fp16* const input_base, + const int input_row_stride, + const int input_col_stride, + __fp16* outptr, + const int matrix_stride +) +{ + constexpr int inner_tile_rows = 4, inner_tile_cols = 4; + + // Get pointers into the input tile + const __fp16 *x_ptrs[inner_tile_rows][inner_tile_cols]; + for (int i = 0, xi = 0; i < inner_tile_rows; i++, xi++) + { + // Get a pointer into the row + const __fp16* const row_ptr = input_base + xi*input_row_stride; + + for (int j = 0, xj = 0; j < inner_tile_cols; j++, xj++) + { + x_ptrs[i][j] = row_ptr + xj*input_col_stride; + } + } + + // Matrices used/computed in this kernel. + __fp16 x[inner_tile_rows][inner_tile_cols]; + __fp16 XTx[inner_tile_rows][inner_tile_cols]; + __fp16 U[inner_tile_rows][inner_tile_cols]; + + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = XTx[i][j] = 0.0f; + } + } + + // Perform the Winograd input transformation for each channel in the input + // tensor. + int channels_remaining = n_channels; +#ifdef __aarch64__ + for (; channels_remaining >= 8; channels_remaining -= 8) + { + // Matrices used/computed in this kernel. + float16x8_t x[inner_tile_rows][inner_tile_cols]; + float16x8_t XTx[inner_tile_rows][inner_tile_cols]; + float16x8_t U[inner_tile_rows][inner_tile_cols]; + + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = vdupq_n_f16(0.0f); + XTx[i][j] = vdupq_n_f16(0.0f); + } + } + + // Load x + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = vld1q_f16(x_ptrs[i][j]); + x_ptrs[i][j] += 8; + } + } + + // Compute XT . x + for (int j = 0; j < inner_tile_cols; j++) + { + // XTx[0][j] = x[0][j] - x[2][j]; + XTx[0][j] = vsubq_f16(x[0][j], x[2][j]); + + // XTx[1][j] = x[1][j] + x[2][j]; + XTx[1][j] = vaddq_f16(x[1][j], x[2][j]); + + // XTx[2][j] = x[2][j] - x[1][j]; + XTx[2][j] = vsubq_f16(x[2][j], x[1][j]); + + // XTx[3][j] = x[1][j] - x[3][j]; + XTx[3][j] = vsubq_f16(x[1][j], x[3][j]); + } + + // Compute U = XT . x . X + for (int i = 0; i < inner_tile_rows; i++) + { + // U[i][0] = XTx[i][0] - XTx[i][2]; + U[i][0] = vsubq_f16(XTx[i][0], XTx[i][2]); + + // U[i][1] = XTx[i][1] + XTx[i][2]; + U[i][1] = vaddq_f16(XTx[i][1], XTx[i][2]); + + // U[i][2] = XTx[i][2] - XTx[i][1]; + U[i][2] = vsubq_f16(XTx[i][2], XTx[i][1]); + + // U[i][3] = XTx[i][1] - XTx[i][3]; + U[i][3] = vsubq_f16(XTx[i][1], XTx[i][3]); + } + + // Store the transformed matrix + for (int i = 0, m = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++, m++) + { + vst1q_f16(outptr + m*matrix_stride, U[i][j]); + } + } + outptr += 8; + } +#endif // __aarch64__ +#ifdef __arm_any__ + for (; channels_remaining >= 4; channels_remaining -= 4) + { + // Matrices used/computed in this kernel. + float16x4_t x[inner_tile_rows][inner_tile_cols]; + float16x4_t XTx[inner_tile_rows][inner_tile_cols]; + float16x4_t U[inner_tile_rows][inner_tile_cols]; + + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = vdup_n_f16(0.0f); + XTx[i][j] = vdup_n_f16(0.0f); + } + } + + // Load x + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = vld1_f16(x_ptrs[i][j]); + x_ptrs[i][j] += 4; + } + } + + // Compute XT . x + for (int j = 0; j < inner_tile_cols; j++) + { + // XTx[0][j] = x[0][j] - x[2][j]; + XTx[0][j] = vsub_f16(x[0][j], x[2][j]); + + // XTx[1][j] = x[1][j] + x[2][j]; + XTx[1][j] = vadd_f16(x[1][j], x[2][j]); + + // XTx[2][j] = x[2][j] - x[1][j]; + XTx[2][j] = vsub_f16(x[2][j], x[1][j]); + + // XTx[3][j] = x[1][j] - x[3][j]; + XTx[3][j] = vsub_f16(x[1][j], x[3][j]); + } + + // Compute U = XT . x . X + for (int i = 0; i < inner_tile_rows; i++) + { + // U[i][0] = XTx[i][0] - XTx[i][2]; + U[i][0] = vsub_f16(XTx[i][0], XTx[i][2]); + + // U[i][1] = XTx[i][1] + XTx[i][2]; + U[i][1] = vadd_f16(XTx[i][1], XTx[i][2]); + + // U[i][2] = XTx[i][2] - XTx[i][1]; + U[i][2] = vsub_f16(XTx[i][2], XTx[i][1]); + + // U[i][3] = XTx[i][1] - XTx[i][3]; + U[i][3] = vsub_f16(XTx[i][1], XTx[i][3]); + } + + // Store the transformed matrix + for (int i = 0, m = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++, m++) + { + vst1_f16(outptr + m*matrix_stride, U[i][j]); + } + } + outptr += 4; + } +#endif // __arm_any__ + for (; channels_remaining; channels_remaining--) + { + // Load x + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = *(x_ptrs[i][j]++); + } + } + + // Compute XT . x + for (int j = 0; j < inner_tile_cols; j++) + { + XTx[0][j] = x[0][j] - x[2][j]; + XTx[1][j] = x[1][j] + x[2][j]; + XTx[2][j] = x[2][j] - x[1][j]; + XTx[3][j] = x[1][j] - x[3][j]; + } + + // Compute U = XT . x . X + for (int i = 0; i < inner_tile_rows; i++) + { + U[i][0] = XTx[i][0] - XTx[i][2]; + U[i][1] = XTx[i][1] + XTx[i][2]; + U[i][2] = XTx[i][2] - XTx[i][1]; + U[i][3] = XTx[i][1] - XTx[i][3]; + } + + // Store the transformed matrix + for (int i = 0, m = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++, m++) + { + *(outptr + m*matrix_stride) = U[i][j]; + } + } + outptr++; + } +} + +template class InputTransform<4, 4, __fp16, __fp16, WinogradRoots::Integers>; + +} // namespace +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input_6x6_fp16_fp16_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input_6x6_fp16_fp16_integers.cpp new file mode 100644 index 0000000000..3eaf977826 --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/input_6x6_fp16_fp16_integers.cpp @@ -0,0 +1,277 @@ +/* + * Copyright (c) 2020 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +#include "arm.hpp" +#include "input.hpp" + +namespace winograd +{ +template <> +void InputTransform<6, 6, __fp16, __fp16, WinogradRoots::Integers>::transform_tile( + const int n_channels, + const __fp16* const input_base, + const int input_row_stride, + const int input_col_stride, + __fp16* outptr, + const int matrix_stride +) +{ + constexpr int inner_tile_rows = 6; + constexpr int inner_tile_cols = 6; + + // Get pointers into the input tile + const __fp16 *x_ptrs[inner_tile_rows][inner_tile_cols]; + for (int i = 0, xi = 0; i < inner_tile_rows; i++, xi++) + { + // Get a pointer into the row + const __fp16* const row_ptr = input_base + xi*input_row_stride; + + for (int j = 0, xj = 0; j < inner_tile_cols; j++, xj++) + { + x_ptrs[i][j] = row_ptr + xj*input_col_stride; + } + } + + // Matrices used/computed in this kernel. + __fp16 x[inner_tile_rows][inner_tile_cols]; + __fp16 XTx[inner_tile_rows][inner_tile_cols]; + __fp16 U[inner_tile_rows][inner_tile_cols]; + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = XTx[i][j] = 0.0f; + } + } + + // Perform the Winograd input transformation for each channel in the input + // tensor. + int channels_remaining = n_channels; + for (; channels_remaining >= 8; channels_remaining -= 8) + { + // Matrices used/computed in this kernel + float16x8_t x[inner_tile_rows][inner_tile_cols]; + float16x8_t XTx[inner_tile_rows][inner_tile_cols]; + float16x8_t U[inner_tile_rows][inner_tile_cols]; + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = vdupq_n_f16(0.0f); + XTx[i][j] = vdupq_n_f16(0.0f); + } + } + + // Read a 6x6 tile in the Winograd domain + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = vld1q_f16(x_ptrs[i][j]); + x_ptrs[i][j] += 8; + } + } + + // Compute XT . x + for (int j = 0; j < inner_tile_cols; j++) + { + // XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; + XTx[0][j] = vsubq_f16(vaddq_f16(x[4][j], vmulq_f16(x[0][j], vdupq_n_f16(4.0f))), vmulq_f16(x[2][j], vdupq_n_f16(5.0f))); + + // XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; + XTx[1][j] = vsubq_f16(vaddq_f16(x[3][j], x[4][j]), vmulq_f16(vaddq_f16(x[1][j], x[2][j]), vdupq_n_f16(4.0f))); + + // XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; + XTx[2][j] = vaddq_f16(vsubq_f16(x[4][j], x[3][j]), vmulq_f16(vsubq_f16(x[1][j], x[2][j]), vdupq_n_f16(4.0f))); + + // XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; + XTx[3][j] = vaddq_f16(vsubq_f16(x[4][j], x[2][j]), vmulq_f16(vsubq_f16(x[3][j], x[1][j]), vdupq_n_f16(2.0f))); + + // XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; + XTx[4][j] = vaddq_f16(vsubq_f16(x[4][j], x[2][j]), vmulq_f16(vsubq_f16(x[1][j], x[3][j]), vdupq_n_f16(2.0f))); + + // XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; + XTx[5][j] = vsubq_f16(vaddq_f16(x[5][j], vmulq_f16(x[1][j], vdupq_n_f16(4.0f))), vmulq_f16(x[3][j], vdupq_n_f16(5.0f))); + } + + // Compute U = XT . x . X + for (int i = 0; i < inner_tile_rows; i++) + { + // U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; + U[i][0] = vsubq_f16(vaddq_f16(XTx[i][4], vmulq_f16(XTx[i][0], vdupq_n_f16(4.0f))), vmulq_f16(XTx[i][2], vdupq_n_f16(5.0f))); + + // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; + U[i][1] = vsubq_f16(vaddq_f16(XTx[i][3], XTx[i][4]), vmulq_f16(vaddq_f16(XTx[i][1], XTx[i][2]), vdupq_n_f16(4.0f))); + + // U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; + U[i][2] = vaddq_f16(vsubq_f16(XTx[i][4], XTx[i][3]), vmulq_f16(vsubq_f16(XTx[i][1], XTx[i][2]), vdupq_n_f16(4.0f))); + + // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; + U[i][3] = vaddq_f16(vsubq_f16(XTx[i][4], XTx[i][2]), vmulq_f16(vsubq_f16(XTx[i][3], XTx[i][1]), vdupq_n_f16(2.0f))); + + // U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; + U[i][4] = vaddq_f16(vsubq_f16(XTx[i][4], XTx[i][2]), vmulq_f16(vsubq_f16(XTx[i][1], XTx[i][3]), vdupq_n_f16(2.0f))); + + // U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; + U[i][5] = vsubq_f16(vaddq_f16(XTx[i][5], vmulq_f16(XTx[i][1], vdupq_n_f16(4.0f))), vmulq_f16(XTx[i][3], vdupq_n_f16(5.0f))); + } + + // Store the transformed matrix + for (int i = 0, m = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++, m++) + { + vst1q_f16(outptr + m*matrix_stride, U[i][j]); + } + } + outptr += 8; + } + for (; channels_remaining >= 4; channels_remaining -= 4) + { + // Matrices used/computed in this kernel + float16x4_t x[inner_tile_rows][inner_tile_cols]; + float16x4_t XTx[inner_tile_rows][inner_tile_cols]; + float16x4_t U[inner_tile_rows][inner_tile_cols]; + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = vdup_n_f16(0.0f); + XTx[i][j] = vdup_n_f16(0.0f); + } + } + + // Read a 6x6 tile in the Winograd domain + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = vld1_f16(x_ptrs[i][j]); + x_ptrs[i][j] += 4; + } + } + + // Compute XT . x + for (int j = 0; j < inner_tile_cols; j++) + { + // XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; + XTx[0][j] = vsub_f16(vadd_f16(x[4][j], vmul_f16(x[0][j], vdup_n_f16(4.0f))), vmul_f16(x[2][j], vdup_n_f16(5.0f))); + + // XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; + XTx[1][j] = vsub_f16(vadd_f16(x[3][j], x[4][j]), vmul_f16(vadd_f16(x[1][j], x[2][j]), vdup_n_f16(4.0f))); + + // XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; + XTx[2][j] = vadd_f16(vsub_f16(x[4][j], x[3][j]), vmul_f16(vsub_f16(x[1][j], x[2][j]), vdup_n_f16(4.0f))); + + // XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; + XTx[3][j] = vadd_f16(vsub_f16(x[4][j], x[2][j]), vmul_f16(vsub_f16(x[3][j], x[1][j]), vdup_n_f16(2.0f))); + + // XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; + XTx[4][j] = vadd_f16(vsub_f16(x[4][j], x[2][j]), vmul_f16(vsub_f16(x[1][j], x[3][j]), vdup_n_f16(2.0f))); + + // XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; + XTx[5][j] = vsub_f16(vadd_f16(x[5][j], vmul_f16(x[1][j], vdup_n_f16(4.0f))), vmul_f16(x[3][j], vdup_n_f16(5.0f))); + } + + // Compute U = XT . x . X + for (int i = 0; i < inner_tile_rows; i++) + { + // U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; + U[i][0] = vsub_f16(vadd_f16(XTx[i][4], vmul_f16(XTx[i][0], vdup_n_f16(4.0f))), vmul_f16(XTx[i][2], vdup_n_f16(5.0f))); + + // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; + U[i][1] = vsub_f16(vadd_f16(XTx[i][3], XTx[i][4]), vmul_f16(vadd_f16(XTx[i][1], XTx[i][2]), vdup_n_f16(4.0f))); + + // U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; + U[i][2] = vadd_f16(vsub_f16(XTx[i][4], XTx[i][3]), vmul_f16(vsub_f16(XTx[i][1], XTx[i][2]), vdup_n_f16(4.0f))); + + // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; + U[i][3] = vadd_f16(vsub_f16(XTx[i][4], XTx[i][2]), vmul_f16(vsub_f16(XTx[i][3], XTx[i][1]), vdup_n_f16(2.0f))); + + // U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; + U[i][4] = vadd_f16(vsub_f16(XTx[i][4], XTx[i][2]), vmul_f16(vsub_f16(XTx[i][1], XTx[i][3]), vdup_n_f16(2.0f))); + + // U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; + U[i][5] = vsub_f16(vadd_f16(XTx[i][5], vmul_f16(XTx[i][1], vdup_n_f16(4.0f))), vmul_f16(XTx[i][3], vdup_n_f16(5.0f))); + } + + // Store the transformed matrix + for (int i = 0, m = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++, m++) + { + vst1_f16(outptr + m*matrix_stride, U[i][j]); + } + } + outptr += 4; + } + for (; channels_remaining; channels_remaining--) + { + // Load x + for (int i = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++) + { + x[i][j] = *(x_ptrs[i][j]++); + } + } + + // Compute XT . x + for (int j = 0; j < inner_tile_cols; j++) + { + XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; + XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; + XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; + XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; + XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; + XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; + } + + // Compute U = XT . x . X + for (int i = 0; i < inner_tile_rows; i++) + { + U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; + U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; + U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; + U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; + U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; + U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; + } + + // Store the transformed matrix + for (int i = 0, m = 0; i < inner_tile_rows; i++) + { + for (int j = 0; j < inner_tile_cols; j++, m++) + { + *(outptr + m*matrix_stride) = U[i][j]; + } + } + outptr++; + } +} + +template class InputTransform<6, 6, __fp16, __fp16, WinogradRoots::Integers>; + +} // namespace winograd +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
\ No newline at end of file diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output.hpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output.hpp index fe47ccbde9..ed88098938 100644 --- a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output.hpp +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output.hpp @@ -48,15 +48,9 @@ MEMBERFN() _n_channels(n_channels), _output_min((activation.type == arm_gemm::Activation::Type::ReLU || activation.type == arm_gemm::Activation::Type::BoundedReLU) - ? static_cast<TOut>(0.0f) - : (std::numeric_limits<TOut>::has_infinity) - ? -std::numeric_limits<TOut>::infinity() - : std::numeric_limits<TOut>::lowest()), + ? static_cast<TOut>(0.0f) : TypeBounds<TOut>::lower()), _output_max((activation.type == arm_gemm::Activation::Type::BoundedReLU) - ? static_cast<TOut>(activation.param1) - : (std::numeric_limits<TOut>::has_infinity) - ? std::numeric_limits<TOut>::infinity() - : std::numeric_limits<TOut>::max()), + ? static_cast<TOut>(activation.param1) : TypeBounds<TOut>::upper()), _matrix_base(nullptr), _biases(nullptr), _matrix_stride(0), _matrix_row_stride(0), _matrix_batch_stride(0), _outptr(nullptr), _tiles_M(iceildiv(n_rows, output_tile_rows)), diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_4x4_3x3_fp16_fp16_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_4x4_3x3_fp16_fp16_integers.cpp new file mode 100644 index 0000000000..37b890d1bc --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/output_4x4_3x3_fp16_fp16_integers.cpp @@ -0,0 +1,255 @@ +/* + * Copyright (c) 2020 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +#include "arm.hpp" +#include "output.hpp" + +namespace winograd +{ + +template <> +void winograd::OutputTransform<3, 3, 6, 6, __fp16, __fp16, winograd::WinogradRoots::Integers>::transform_tile( + const int n_channels, + const __fp16* inptr, + const int matrix_stride, + const __fp16* bptr, + __fp16* const output, + const int output_row_stride, + const int output_col_stride, + const __fp16 output_min, + const __fp16 output_max +) +{ + // Construct a map to the output cells + __fp16 *outptrs[output_tile_rows][output_tile_cols]; + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + outptrs[i][j] = output + i*output_row_stride + j*output_col_stride; + } + } + + // For each channel of the output + int channels_remaining = n_channels; + +#ifdef __aarch64__ + for (; channels_remaining >= 8; channels_remaining -= 8) + { + // Matrices used and computed during this transform + float16x8_t F[6][6], FZ[6][4], f[4][4], b; + + // Read a 6x6 tile in the Winograd domain + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + F[i][j] = vld1q_f16(inptr + m*matrix_stride); + } + } + inptr += 8; + + // Compute the matrix F Z + for (int i = 0; i < 6; i++) + { + // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; + FZ[i][0] = vaddq_f16(vaddq_f16(vaddq_f16(F[i][0], F[i][1]), vaddq_f16(F[i][2], F[i][3])), F[i][4]); + + // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; + FZ[i][1] = vaddq_f16(vsubq_f16(F[i][1], F[i][2]), vmulq_f16(vsubq_f16(F[i][3], F[i][4]), vdupq_n_f16(2.0f))); + + // FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; + FZ[i][2] = vaddq_f16(vaddq_f16(F[i][1], F[i][2]), vmulq_f16(vaddq_f16(F[i][3], F[i][4]), vdupq_n_f16(4.0f))); + + // FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; + FZ[i][3] = vaddq_f16(vaddq_f16(vsubq_f16(F[i][1], F[i][2]), vmulq_f16(vsubq_f16(F[i][3], F[i][4]), vdupq_n_f16(8.0f))), F[i][5]); + } + + // Compute the output tile f = ZT F Z + for (int j = 0; j < 4; j++) + { + // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; + f[0][j] = vaddq_f16(vaddq_f16(vaddq_f16(FZ[0][j], FZ[1][j]), vaddq_f16(FZ[2][j], FZ[3][j])), FZ[4][j]); + + // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; + f[1][j] = vaddq_f16(vsubq_f16(FZ[1][j], FZ[2][j]), vmulq_f16(vsubq_f16(FZ[3][j], FZ[4][j]), vdupq_n_f16(2.0f))); + + // f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; + f[2][j] = vaddq_f16(vaddq_f16(FZ[1][j], FZ[2][j]), vmulq_f16(vaddq_f16(FZ[3][j], FZ[4][j]), vdupq_n_f16(4.0f))); + + // f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; + f[3][j] = vaddq_f16(vaddq_f16(vsubq_f16(FZ[1][j], FZ[2][j]), vmulq_f16(vsubq_f16(FZ[3][j], FZ[4][j]), vdupq_n_f16(8.0f))), FZ[5][j]); + } + + // Write out the output tile + if (bptr != nullptr) + { + b = vld1q_f16(bptr); + bptr += 8; + } + else + { + b = vdupq_n_f16(0.0f); + } + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + const auto y = + vmaxq_f16(vminq_f16(vaddq_f16(f[i][j], b), vdupq_n_f16(output_max)), + vdupq_n_f16(output_min)); + vst1q_f16(outptrs[i][j], y); + outptrs[i][j] += 8; + } + } + } +#endif // __aarch64__ +#ifdef __arm_any__ + for (; channels_remaining >= 4; channels_remaining -= 4) + { + // Matrices used and computed during this transform + float16x4_t F[6][6], FZ[6][4], f[4][4], b; + + // Read a 6x6 tile in the Winograd domain + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + F[i][j] = vld1_f16(inptr + m*matrix_stride); + } + } + inptr += 4; + + // Compute the matrix F Z + for (int i = 0; i < 6; i++) + { + // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; + FZ[i][0] = vadd_f16(vadd_f16(vadd_f16(F[i][0], F[i][1]), vadd_f16(F[i][2], F[i][3])), F[i][4]); + + // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; + FZ[i][1] = vadd_f16(vsub_f16(F[i][1], F[i][2]), vmul_f16(vsub_f16(F[i][3], F[i][4]), vdup_n_f16(2.0f))); + + // FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; + FZ[i][2] = vadd_f16(vadd_f16(F[i][1], F[i][2]), vmul_f16(vadd_f16(F[i][3], F[i][4]), vdup_n_f16(4.0f))); + + // FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; + FZ[i][3] = vadd_f16(vadd_f16(vsub_f16(F[i][1], F[i][2]), vmul_f16(vsub_f16(F[i][3], F[i][4]), vdup_n_f16(8.0f))), F[i][5]); + } + + // Compute the output tile f = ZT F Z + for (int j = 0; j < 4; j++) + { + // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; + f[0][j] = vadd_f16(vadd_f16(vadd_f16(FZ[0][j], FZ[1][j]), vadd_f16(FZ[2][j], FZ[3][j])), FZ[4][j]); + + // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; + f[1][j] = vadd_f16(vsub_f16(FZ[1][j], FZ[2][j]), vmul_f16(vsub_f16(FZ[3][j], FZ[4][j]), vdup_n_f16(2.0f))); + + // f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; + f[2][j] = vadd_f16(vadd_f16(FZ[1][j], FZ[2][j]), vmul_f16(vadd_f16(FZ[3][j], FZ[4][j]), vdup_n_f16(4.0f))); + + // f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; + f[3][j] = vadd_f16(vadd_f16(vsub_f16(FZ[1][j], FZ[2][j]), vmul_f16(vsub_f16(FZ[3][j], FZ[4][j]), vdup_n_f16(8.0f))), FZ[5][j]); + } + + // Write out the output tile + if (bptr != nullptr) + { + b = vld1_f16(bptr); + bptr += 4; + } + else + { + b = vdup_n_f16(0.0f); + } + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + const auto y = + vmax_f16(vmin_f16(vadd_f16(f[i][j], b), vdup_n_f16(output_max)), + vdup_n_f16(output_min)); + vst1_f16(outptrs[i][j], y); + outptrs[i][j] += 4; + } + } + } +#endif // __arm_any__ + for (; channels_remaining; channels_remaining--) + { + // Matrices used and computed during this transform + __fp16 F[6][6], FZ[6][4], f[4][4], b; + + // Read a 6x6 tile in the Winograd domain + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + F[i][j] = *(inptr + m*matrix_stride); + } + } + inptr++; + + // Compute the matrix F Z + for (int i = 0; i < 6; i++) + { + FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; + FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; + FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; + FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; + } + + // Compute the output tile f = ZT F Z + for (int j = 0; j < 4; j++) + { + f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; + f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; + f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; + f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; + } + + // Write out the output tile + if (bptr != nullptr) + { + b = *(bptr++); + } + else + { + b = 0.0f; + } + for (int i = 0; i < output_tile_rows; i++) + { + for (int j = 0; j < output_tile_cols; j++) + { + const auto y = std::max(std::min<__fp16>(f[i][j] + b, output_max), output_min); + *(outptrs[i][j]++) = y; + } + } + } +} + +template class OutputTransform<3, 3, 6, 6, __fp16, __fp16, winograd::WinogradRoots::Integers>; + +} // namespace winograd +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_4x4_3x3_fp16_fp16_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_4x4_3x3_fp16_fp16_integers.cpp new file mode 100644 index 0000000000..3c4f8b426c --- /dev/null +++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_4x4_3x3_fp16_fp16_integers.cpp @@ -0,0 +1,259 @@ +/* + * Copyright (c) 2020 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + +#include "arm.hpp" +#include "kernel.hpp" + +namespace winograd +{ + +template <> +void WeightTransform<3, 3, 6, 6, __fp16, __fp16, WinogradRoots::Integers>::execute( + const int n_output_channels, + const int n_input_channels, + const __fp16* const input, // NOTE: Data in HWIO order + __fp16* const output, + const int matrix_stride, + const int matrix_row_stride +) +{ + // Get pointers to each cell of the weight tensor + const auto weight_col_stride = n_input_channels * n_output_channels; + const auto weight_row_stride = 3 * weight_col_stride; + const __fp16 *inptrs[3][3]; + for (int i = 0; i < 3; i++) + { + for (int j = 0; j < 3; j++) + { + inptrs[i][j] = input + i*weight_row_stride + j*weight_col_stride; + } + } + + // For each input channel + for (int ic = 0; ic < n_input_channels; ic++) + { + __fp16 *outptr = output + ic * matrix_row_stride; + + // For each output channel + int channels_remaining = n_output_channels; +#ifdef __aarch64__ + for (; channels_remaining >= 8; channels_remaining -= 8) + { + // Matrices used and computed in this kernel + float16x8_t w[3][3], Ww[6][3], V[6][6]; + + // Read weights + for (int i = 0; i < 3; i++) + { + for (int j = 0; j < 3; j++) + { + w[i][j] = vld1q_f16(inptrs[i][j]); + inptrs[i][j] += 8; + } + } + + // Compute the matrix W w + for (int j = 0; j < 3; j++) + { + // Ww[0][j] = 6*w[0][j]; + Ww[0][j] = vmulq_n_f16(w[0][j], 6.0); + + // Ww[1][j] = -4*w[0][j] + -4*w[1][j] + -4*w[2][j]; + Ww[1][j] = vmulq_n_f16(vaddq_f16(vaddq_f16(w[0][j], w[1][j]), w[2][j]), -4.0); + + // Ww[2][j] = -4*w[0][j] + 4*w[1][j] + -4*w[2][j]; + Ww[2][j] = vmulq_n_f16(vsubq_f16(vsubq_f16(w[1][j], w[0][j]), w[2][j]), 4.0); + + // Ww[3][j] = 1*w[0][j] + 2*w[1][j] + 4*w[2][j]; + Ww[3][j] = vaddq_f16(vaddq_f16(w[0][j], vmulq_f16(w[1][j], vdupq_n_f16(2.0f))), vmulq_f16(w[2][j], vdupq_n_f16(4.0f))); + + // Ww[4][j] = 1*w[0][j] + -2*w[1][j] + 4*w[2][j]; + Ww[4][j] = vaddq_f16(vsubq_f16(w[0][j], vmulq_f16(w[1][j], vdupq_n_f16(2.0f))), vmulq_f16(w[2][j], vdupq_n_f16(4.0f))); + + // Ww[5][j] = 24*w[2][j]; + Ww[5][j] = vmulq_n_f16(w[2][j], 24.0f); + } + + // Compute V = W w WT + for (int i = 0; i < 6; i++) + { + const float recip576 = 1.0f / 576.0f; + + // V[i][0] = 6*Ww[i][0]; + V[i][0] = vmulq_n_f16(vmulq_n_f16(Ww[i][0], 6.0), recip576); + + // V[i][1] = -4*Ww[i][0] + -4*Ww[i][1] + -4*Ww[i][2]; + V[i][1] = vmulq_n_f16(vmulq_n_f16(vaddq_f16(vaddq_f16(Ww[i][0], Ww[i][1]), Ww[i][2]), -4.0), recip576); + + // V[i][2] = -4*Ww[i][0] + 4*Ww[i][1] + -4*Ww[i][2]; + V[i][2] = vmulq_n_f16(vmulq_n_f16(vsubq_f16(vsubq_f16(Ww[i][1], Ww[i][0]), Ww[i][2]), 4.0), recip576); + + // V[i][3] = 1*Ww[i][0] + 2*Ww[i][1] + 4*Ww[i][2]; + V[i][3] = vmulq_n_f16(vaddq_f16(vaddq_f16(Ww[i][0], vmulq_f16(Ww[i][1], vdupq_n_f16(2.0f))), vmulq_f16(Ww[i][2], vdupq_n_f16(4.0f))), recip576); + + // V[i][4] = 1*Ww[i][0] + -2*Ww[i][1] + 4*Ww[i][2]; + V[i][4] = vmulq_n_f16(vaddq_f16(vsubq_f16(Ww[i][0], vmulq_f16(Ww[i][1], vdupq_n_f16(2.0f))), vmulq_f16(Ww[i][2], vdupq_n_f16(4.0f))), recip576); + + // V[i][5] = 24*Ww[i][2]; + V[i][5] = vmulq_n_f16(vmulq_n_f16(Ww[i][2], 24.0f), recip576); + } + + // Store the transformed weights + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + vst1q_f16(outptr + m*matrix_stride, V[i][j]); + } + } + outptr += 8; + } +#endif // __aarch64__ +#ifdef __arm_any__ + for (; channels_remaining >= 4; channels_remaining -= 4) + { + // Matrices used and computed in this kernel + float16x4_t w[3][3], Ww[6][3], V[6][6]; + + // Read weights + for (int i = 0; i < 3; i++) + { + for (int j = 0; j < 3; j++) + { + w[i][j] = vld1_f16(inptrs[i][j]); + inptrs[i][j] += 4; + } + } + + // Compute the matrix W w + for (int j = 0; j < 3; j++) + { + // Ww[0][j] = 6*w[0][j]; + Ww[0][j] = vmul_n_f16(w[0][j], 6.0); + + // Ww[1][j] = -4*w[0][j] + -4*w[1][j] + -4*w[2][j]; + Ww[1][j] = vmul_n_f16(vadd_f16(vadd_f16(w[0][j], w[1][j]), w[2][j]), -4.0); + + // Ww[2][j] = -4*w[0][j] + 4*w[1][j] + -4*w[2][j]; + Ww[2][j] = vmul_n_f16(vsub_f16(vsub_f16(w[1][j], w[0][j]), w[2][j]), 4.0); + + // Ww[3][j] = 1*w[0][j] + 2*w[1][j] + 4*w[2][j]; + Ww[3][j] = vadd_f16(vadd_f16(w[0][j], vmul_f16(w[1][j], vdup_n_f16(2.0f))), vmul_f16(w[2][j], vdup_n_f16(4.0f))); + + // Ww[4][j] = 1*w[0][j] + -2*w[1][j] + 4*w[2][j]; + Ww[4][j] = vadd_f16(vsub_f16(w[0][j], vmul_f16(w[1][j], vdup_n_f16(2.0f))), vmul_f16(w[2][j], vdup_n_f16(4.0f))); + + // Ww[5][j] = 24*w[2][j]; + Ww[5][j] = vmul_n_f16(w[2][j], 24.0f); + } + + // Compute V = W w WT + for (int i = 0; i < 6; i++) + { + const float recip576 = 1.0f / 576.0f; + + // V[i][0] = 6*Ww[i][0]; + V[i][0] = vmul_n_f16(vmul_n_f16(Ww[i][0], 6.0), recip576); + + // V[i][1] = -4*Ww[i][0] + -4*Ww[i][1] + -4*Ww[i][2]; + V[i][1] = vmul_n_f16(vmul_n_f16(vadd_f16(vadd_f16(Ww[i][0], Ww[i][1]), Ww[i][2]), -4.0), recip576); + + // V[i][2] = -4*Ww[i][0] + 4*Ww[i][1] + -4*Ww[i][2]; + V[i][2] = vmul_n_f16(vmul_n_f16(vsub_f16(vsub_f16(Ww[i][1], Ww[i][0]), Ww[i][2]), 4.0), recip576); + + // V[i][3] = 1*Ww[i][0] + 2*Ww[i][1] + 4*Ww[i][2]; + V[i][3] = vmul_n_f16(vadd_f16(vadd_f16(Ww[i][0], vmul_f16(Ww[i][1], vdup_n_f16(2.0f))), vmul_f16(Ww[i][2], vdup_n_f16(4.0f))), recip576); + + // V[i][4] = 1*Ww[i][0] + -2*Ww[i][1] + 4*Ww[i][2]; + V[i][4] = vmul_n_f16(vadd_f16(vsub_f16(Ww[i][0], vmul_f16(Ww[i][1], vdup_n_f16(2.0f))), vmul_f16(Ww[i][2], vdup_n_f16(4.0f))), recip576); + + // V[i][5] = 24*Ww[i][2]; + V[i][5] = vmul_n_f16(vmul_n_f16(Ww[i][2], 24.0f), recip576); + } + + // Store the transformed weights + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + vst1_f16(outptr + m*matrix_stride, V[i][j]); + } + } + outptr += 4; + } +#endif // __arm_any__ + for (; channels_remaining; channels_remaining--) + { + // Matrices used and computed in this kernel + __fp16 w[3][3], Ww[6][3], V[6][6]; + + // Read weights + for (int i = 0; i < 3; i++) + { + for (int j = 0; j < 3; j++) + { + w[i][j] = *(inptrs[i][j]++); + } + } + + // Compute the matrix W w + for (int j = 0; j < 3; j++) + { + Ww[0][j] = 6*w[0][j]; + Ww[1][j] = -4*w[0][j] + -4*w[1][j] + -4*w[2][j]; + Ww[2][j] = -4*w[0][j] + 4*w[1][j] + -4*w[2][j]; + Ww[3][j] = 1*w[0][j] + 2*w[1][j] + 4*w[2][j]; + Ww[4][j] = 1*w[0][j] + -2*w[1][j] + 4*w[2][j]; + Ww[5][j] = 24*w[2][j]; + } + + // Compute V = W w WT + for (int i = 0; i < 6; i++) + { + V[i][0] = ( 6*Ww[i][0]) / 576.0; + V[i][1] = (-4*Ww[i][0] + -4*Ww[i][1] + -4*Ww[i][2]) / 576.0; + V[i][2] = (-4*Ww[i][0] + 4*Ww[i][1] + -4*Ww[i][2]) / 576.0; + V[i][3] = ( 1*Ww[i][0] + 2*Ww[i][1] + 4*Ww[i][2]) / 576.0; + V[i][4] = ( 1*Ww[i][0] + -2*Ww[i][1] + 4*Ww[i][2]) / 576.0; + V[i][5] = (24*Ww[i][2]) / 576.0; + } + + // Store the transformed weights + for (int i = 0, m = 0; i < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + *(outptr + m*matrix_stride) = V[i][j]; + } + } + outptr++; + } + } +} + +template class WeightTransform<3, 3, 6, 6, __fp16, __fp16, WinogradRoots::Integers>; + +} // namespace +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC diff --git a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp index 81190fbf0e..d567a18709 100644 --- a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp @@ -23,11 +23,11 @@ */ #include "arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h" +#include "arm_compute/core/CPP/Validate.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" -#include "arm_compute/core/Validate.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/NEON/NEScheduler.h" #include "arm_compute/runtime/NEON/functions/NEGEMMAssemblyDispatch.h" @@ -43,18 +43,32 @@ namespace inline Status validate_kernel_3x3(const Size2D input_dims, const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) { - if(input_dims.width > 4 && input_dims.height > 4) + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); + + if(input->data_type() == DataType::F32) { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>::validate(input, input0, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, input1, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, output, winograd_info))); + if(input_dims.width > 4 && input_dims.height > 4) + { + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>::validate(input, input0, winograd_info))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, input1, winograd_info))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, output, winograd_info))); + } + else + { + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, input0, winograd_info))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, input1, winograd_info))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, output, winograd_info))); + } } - else +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + else if(input->data_type() == DataType::F32) { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, input0, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, input1, winograd_info))); - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, output, winograd_info))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<__fp16, 4, 4, 3, 3>::validate(input, input0, winograd_info))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<__fp16, 4, 4, 3, 3>::validate(weights, input1, winograd_info))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<__fp16, 4, 4, 3, 3>::validate(batched_mm_output, biases, output, winograd_info))); } +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ if(act_info.enabled()) { @@ -79,6 +93,7 @@ inline Status validate_kernel_5x5(const ITensorInfo *input, const TensorInfo *in inline Status validate_kernel_3x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 6, 1, 3>::validate(input, input0, winograd_info))); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 6, 1, 3>::validate(weights, input1, winograd_info))); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 6, 1, 3>::validate(batched_mm_output, biases, output, winograd_info))); @@ -92,6 +107,7 @@ inline Status validate_kernel_3x1(const ITensorInfo *input, const TensorInfo *in inline Status validate_kernel_1x3(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 6, 1, 3, 1>::validate(input, input0, winograd_info))); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 6, 1, 3, 1>::validate(weights, input1, winograd_info))); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 6, 1, 3, 1>::validate(batched_mm_output, biases, output, winograd_info))); @@ -106,6 +122,7 @@ inline Status validate_kernel_1x3(const ITensorInfo *input, const TensorInfo *in inline Status validate_kernel_5x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 4, 1, 5>::validate(input, input0, winograd_info))); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 4, 1, 5>::validate(weights, input1, winograd_info))); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 4, 1, 5>::validate(batched_mm_output, biases, output, winograd_info))); @@ -118,6 +135,7 @@ inline Status validate_kernel_5x1(const ITensorInfo *input, const TensorInfo *in inline Status validate_kernel_1x5(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 1, 5, 1>::validate(input, input0, winograd_info))); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 1, 5, 1>::validate(weights, input1, winograd_info))); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 1, 5, 1>::validate(batched_mm_output, biases, output, winograd_info))); @@ -131,6 +149,7 @@ inline Status validate_kernel_1x5(const ITensorInfo *input, const TensorInfo *in inline Status validate_kernel_7x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 2, 1, 7>::validate(input, input0, winograd_info))); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 2, 1, 7>::validate(weights, input1, winograd_info))); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 2, 1, 7>::validate(batched_mm_output, biases, output, winograd_info))); @@ -144,6 +163,7 @@ inline Status validate_kernel_7x1(const ITensorInfo *input, const TensorInfo *in inline Status validate_kernel_1x7(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info) { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 1, 7, 1>::validate(input, input0, winograd_info))); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 1, 7, 1>::validate(weights, input1, winograd_info))); ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 1, 7, 1>::validate(batched_mm_output, biases, output, winograd_info))); @@ -169,21 +189,27 @@ inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input) Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info) { ARM_COMPUTE_UNUSED(output); + ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides."); if(biases != nullptr) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); } - return INEWinogradLayerTransformWeightsKernel<float>::validate(input, weights); + return INEWinogradLayerTransformWeightsKernel::validate(input, weights); } -Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims) +Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataType data_type) { Size2D output_tile = Size2D{}; if(kernel_dims == Size2D(3U, 3U)) { output_tile = (input_dims.width <= 4 || input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U); + if(data_type == DataType::F16) + { + output_tile = Size2D(4U, 4U); + } } else if(kernel_dims == Size2D(5U, 5U)) { @@ -216,12 +242,17 @@ Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims) return output_tile; } -bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size) +bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size, DataType data_type) { // Check if we want to configure a Winograd configuration which requires fast math using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>; - const std::vector<WinogradConfiguration> fast_math_winograd = + const std::vector<WinogradConfiguration> fast_math_winograd_f16 = + { + WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3)) + }; + + const std::vector<WinogradConfiguration> fast_math_winograd_f32 = { WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(5, 5)), WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5)) @@ -230,7 +261,15 @@ bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_siz auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height), std::pair<int, int>(kernel_size.width, kernel_size.height)); - return std::find(fast_math_winograd.begin(), fast_math_winograd.end(), p) != fast_math_winograd.end(); + switch(data_type) + { + case DataType::F16: + return std::find(fast_math_winograd_f16.begin(), fast_math_winograd_f16.end(), p) != fast_math_winograd_f16.end(); + case DataType::F32: + return std::find(fast_math_winograd_f32.begin(), fast_math_winograd_f32.end(), p) != fast_math_winograd_f32.end(); + default: + return false; + } } inline bool fuse_function_supported(const ActivationLayerInfo &act_info) @@ -256,7 +295,6 @@ arm_gemm::Activation arm_gemm_activation_from_acl_activation(const ActivationLay } } } - } //namespace NEWinogradConvolutionLayer::NEWinogradConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager) @@ -278,14 +316,16 @@ void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor * const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); - const Size2D input_dims = Size2D(input->info()->dimension(width_idx), input->info()->dimension(height_idx)); - const Size2D kernel_size = Size2D(weights->info()->dimension(width_idx), weights->info()->dimension(height_idx)); - const Size2D output_tile = winograd_output_tile(input_dims, kernel_size); + const Size2D input_dims = Size2D(input->info()->dimension(width_idx), input->info()->dimension(height_idx)); + const Size2D kernel_size = Size2D(weights->info()->dimension(width_idx), weights->info()->dimension(height_idx)); + const DataType data_type = input->info()->data_type(); + const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type); // Check if the Winograd configuration requires fast math if(!enable_fast_math) { - ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true"); + ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type), + "This Winograd configuration requires enable_fast_math=true"); } _weights = weights; @@ -293,101 +333,122 @@ void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor * _output = output; _is_prepared = false; - std::unique_ptr<INEWinogradLayerTransformInputKernel<float>> transform_input_kernel; - std::unique_ptr<INEWinogradLayerTransformWeightsKernel<float>> transform_weights_kernel; - std::unique_ptr<INEWinogradLayerTransformOutputKernel<float>> transform_output_kernel; - int n_gemms = 0; int N_BLOCK = 0; // Size of block used by GEMM. - if(kernel_size == Size2D(3, 3)) + std::unique_ptr<INEWinogradLayerTransformInputKernel> transform_input_kernel; + std::unique_ptr<INEWinogradLayerTransformWeightsKernel> transform_weights_kernel; + std::unique_ptr<INEWinogradLayerTransformOutputKernel> transform_output_kernel; + + if(data_type == DataType::F32) { - if(input->info()->dimension(width_idx) > 4 && input->info()->dimension(height_idx) > 4) + if(kernel_size == Size2D(3, 3)) { - using config = NEWinogradLayerConfiguration<float, float, 4, 4, 3, 3>; + if(input->info()->dimension(width_idx) > 4 && input->info()->dimension(height_idx) > 4) + { + using config = NEWinogradLayerConfiguration<float, float, 4, 4, 3, 3>; + transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); + transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); + transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); + n_gemms = config::WinogradBase::N_GEMMS; + N_BLOCK = config::WinogradConv::N_BLOCK; + } + else + { + using config = NEWinogradLayerConfiguration<float, float, 2, 2, 3, 3>; + transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); + transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); + transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); + n_gemms = config::WinogradBase::N_GEMMS; + N_BLOCK = config::WinogradConv::N_BLOCK; + } + } + else if(kernel_size == Size2D(5, 5)) + { + using config = NEWinogradLayerConfiguration<float, float, 2, 2, 5, 5>; transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); n_gemms = config::WinogradBase::N_GEMMS; N_BLOCK = config::WinogradConv::N_BLOCK; } - else + else if(kernel_size == Size2D(1, 3)) { - using config = NEWinogradLayerConfiguration<float, float, 2, 2, 3, 3>; + using config = NEWinogradLayerConfiguration<float, float, 6, 1, 3, 1>; transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); n_gemms = config::WinogradBase::N_GEMMS; N_BLOCK = config::WinogradConv::N_BLOCK; } + else if(kernel_size == Size2D(3, 1)) + { + using config = NEWinogradLayerConfiguration<float, float, 1, 6, 1, 3>; + transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); + transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); + transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); + n_gemms = config::WinogradBase::N_GEMMS; + N_BLOCK = config::WinogradConv::N_BLOCK; + } + else if(kernel_size == Size2D(1, 5)) + { + using config = NEWinogradLayerConfiguration<float, float, 4, 1, 5, 1>; + transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); + transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); + transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); + n_gemms = config::WinogradBase::N_GEMMS; + N_BLOCK = config::WinogradConv::N_BLOCK; + } + else if(kernel_size == Size2D(5, 1)) + { + using config = NEWinogradLayerConfiguration<float, float, 1, 4, 1, 5>; + transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); + transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); + transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); + n_gemms = config::WinogradBase::N_GEMMS; + N_BLOCK = config::WinogradConv::N_BLOCK; + } + else if(kernel_size == Size2D(1, 7)) + { + using config = NEWinogradLayerConfiguration<float, float, 2, 1, 7, 1>; + transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); + transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); + transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); + n_gemms = config::WinogradBase::N_GEMMS; + N_BLOCK = config::WinogradConv::N_BLOCK; + } + else if(kernel_size == Size2D(7, 1)) + { + using config = NEWinogradLayerConfiguration<float, float, 1, 2, 1, 7>; + transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); + transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); + transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); + n_gemms = config::WinogradBase::N_GEMMS; + N_BLOCK = config::WinogradConv::N_BLOCK; + } + else + { + ARM_COMPUTE_ERROR("Not supported."); + } } - else if(kernel_size == Size2D(5, 5)) - { - using config = NEWinogradLayerConfiguration<float, float, 2, 2, 5, 5>; - transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else if(kernel_size == Size2D(1, 3)) - { - using config = NEWinogradLayerConfiguration<float, float, 6, 1, 3, 1>; - transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else if(kernel_size == Size2D(3, 1)) - { - using config = NEWinogradLayerConfiguration<float, float, 1, 6, 1, 3>; - transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else if(kernel_size == Size2D(1, 5)) - { - using config = NEWinogradLayerConfiguration<float, float, 4, 1, 5, 1>; - transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else if(kernel_size == Size2D(5, 1)) - { - using config = NEWinogradLayerConfiguration<float, float, 1, 4, 1, 5>; - transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else if(kernel_size == Size2D(1, 7)) - { - using config = NEWinogradLayerConfiguration<float, float, 2, 1, 7, 1>; - transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else if(kernel_size == Size2D(7, 1)) - { - using config = NEWinogradLayerConfiguration<float, float, 1, 2, 1, 7>; - transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); - transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); - transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); - n_gemms = config::WinogradBase::N_GEMMS; - N_BLOCK = config::WinogradConv::N_BLOCK; - } - else +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + else if(data_type == DataType::F16) { - ARM_COMPUTE_ERROR("Not supported."); + if(kernel_size == Size2D(3, 3)) + { + using config = NEWinogradLayerConfiguration<__fp16, __fp16, 4, 4, 3, 3>; + transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>(); + transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>(); + transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>(); + n_gemms = config::WinogradBase::N_GEMMS; + N_BLOCK = config::WinogradConv::N_BLOCK; + } + else + { + ARM_COMPUTE_ERROR("Not supported."); + } } +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC const PaddingType use_padding_type = (conv_info.pad_top() != 0u || conv_info.pad_left() != 0) ? PADDING_SAME : PADDING_VALID; const bool use_same_padding = use_padding_type == PADDING_SAME; @@ -397,7 +458,6 @@ void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor * const int out_channels = output->info()->dimension(channel_idx); const Tensor4DShape in_shape(internal_get_input_shape(input)); - const DataType data_type = input->info()->data_type(); const size_t data_type_size = input->info()->element_size(); // Get the memory required to instantiate a new Winograd operator. constexpr size_t storage_alignment = 64; @@ -592,14 +652,16 @@ Status NEWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITen const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); // Input shape, kernel size and output tile - const Size2D input_dims = Size2D(input->dimension(idx_width), input->dimension(idx_height)); - const Size2D kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height)); - const Size2D output_tile = winograd_output_tile(input_dims, kernel_size); + const Size2D input_dims = Size2D(input->dimension(idx_width), input->dimension(idx_height)); + const Size2D kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height)); + const DataType data_type = input->data_type(); + const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type); // Check if the Winograd configuration requires fast math if(!enable_fast_math) { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type), + "This Winograd configuration requires enable_fast_math=true"); } const WinogradInfo winograd_info = WinogradInfo(output_tile, @@ -706,5 +768,4 @@ void NEWinogradConvolutionLayer::prepare() _is_prepared = true; } } - } // namespace arm_compute |