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Diffstat (limited to 'src/core/NEON/kernels/convolution/depthwise/impl_fp16_fp16.hpp')
| -rw-r--r-- | src/core/NEON/kernels/convolution/depthwise/impl_fp16_fp16.hpp | 439 |
1 files changed, 0 insertions, 439 deletions
diff --git a/src/core/NEON/kernels/convolution/depthwise/impl_fp16_fp16.hpp b/src/core/NEON/kernels/convolution/depthwise/impl_fp16_fp16.hpp deleted file mode 100644 index a00a1ef04a..0000000000 --- a/src/core/NEON/kernels/convolution/depthwise/impl_fp16_fp16.hpp +++ /dev/null @@ -1,439 +0,0 @@ -/* - * Copyright (c) 2018-2019 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. - */ - -/* - * !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! - * - * NOTE: Header to be included by implementation files only. - * - * !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! - */ -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -#include "arm.hpp" -#include "impl_base.hpp" - -#pragma once - -using namespace neon_convolution_kernels; - -namespace depthwise -{ - -template < - unsigned int OutputTileRows, unsigned int OutputTileCols, - unsigned int KernelRows, unsigned int KernelCols, - unsigned int StrideRows, unsigned int StrideCols -> -DepthwiseConvolution< - OutputTileRows, OutputTileCols, - KernelRows, KernelCols, StrideRows, StrideCols, - float16_t, float16_t, float16_t ->::DepthwiseConvolution( - int n_batches, int n_input_rows, int n_input_cols, int n_channels, - ActivationFunction activation, - unsigned int padding_top, - unsigned int padding_left, - unsigned int padding_bottom, - unsigned int padding_right -) : Base( - n_batches, n_input_rows, n_input_cols, n_channels, activation, - padding_top, padding_left, padding_bottom, padding_right - ) -{ -} - -template < - unsigned int OutputTileRows, unsigned int OutputTileCols, - unsigned int KernelRows, unsigned int KernelCols, - unsigned int StrideRows, unsigned int StrideCols -> -DepthwiseConvolution< - OutputTileRows, OutputTileCols, - KernelRows, KernelCols, StrideRows, StrideCols, - float16_t, float16_t, float16_t ->::DepthwiseConvolution( - int n_batches, int n_input_rows, int n_input_cols, int n_channels, - int n_output_rows, int n_output_cols, - ActivationFunction activation, - unsigned int padding_top, - unsigned int padding_left, - unsigned int padding_bottom, - unsigned int padding_right -) : Base( - n_batches, n_input_rows, n_input_cols, n_channels, - n_output_rows, n_output_cols, activation, - padding_top, padding_left, padding_bottom, padding_right - ) -{ -} - -template < - unsigned int OutputTileRows, unsigned int OutputTileCols, - unsigned int KernelRows, unsigned int KernelCols, - unsigned int StrideRows, unsigned int StrideCols -> -template <ActivationFunction Activation> -void DepthwiseConvolution< - OutputTileRows, OutputTileCols, - KernelRows, KernelCols, StrideRows, StrideCols, - float16_t, float16_t, float16_t ->::execute_tile( - int n_channels, - const void *weights_biases_ptr, - const float16_t *input, - const unsigned int in_row_stride, - const unsigned int in_col_stride, - float16_t *output, - const unsigned int out_row_stride, - const unsigned int out_col_stride -) -{ - // Instantiate pointers - const float16_t* __restrict__ inptr_base = input; - float16_t* __restrict__ outptr_base = output; - const float16_t* __restrict__ params = static_cast<const float16_t*>(weights_biases_ptr); - - // Perform the depthwise convolution - int channels_remaining = n_channels; - for (; channels_remaining >= 8; channels_remaining -= 8) - { - // Load input tile - float16x8_t u[Base::inner_tile_rows][Base::inner_tile_cols]; - for (int i = 0; i < Base::inner_tile_rows; i++) - { - const float16_t* const inptr_row = inptr_base + i*in_row_stride; - for (int j = 0; j < Base::inner_tile_cols; j++) - { - u[i][j] = vld1q_f16(inptr_row + j*in_col_stride); - } - } - inptr_base += 8; - - // Load weights tile - float16x8_t vbias = vld1q_f16(params); - params += 8; - - float16x8_t w[KernelRows][KernelCols]; - for (unsigned int i = 0; i < KernelRows; i++) - { - for (unsigned int j = 0; j < KernelCols; j++) - { - w[i][j] = vld1q_f16(params); - params += 8; - } - } - - // Perform the convolution - float16x8_t v[OutputTileRows][OutputTileCols]; - for (unsigned int out_i = 0; out_i < OutputTileRows; out_i++) - { - for (unsigned int out_j = 0; out_j < OutputTileCols; out_j++) - { - v[out_i][out_j] = vbias; - - // Base co-ordinate - const int base_i = out_i * StrideRows; - const int base_j = out_j * StrideCols; - - // Fill the accumulator - for (unsigned int in_i = 0; in_i < KernelRows; in_i++) - { - const unsigned int i = base_i + in_i; - for (unsigned int in_j = 0; in_j < KernelCols; in_j++) - { - const unsigned int j = base_j + in_j; - - // v[out_i][out_j] += w[in_i][in_j] * u[i][j]; - v[out_i][out_j] = vaddq_f16(v[out_i][out_j], vmulq_f16(w[in_i][in_j], u[i][j])); - } - } - - // Apply the activation function - if (Activation == ActivationFunction::ReLU || - Activation == ActivationFunction::ReLU6) - { - v[out_i][out_j] = vmaxq_f16(v[out_i][out_j], vdupq_n_f16(0.0f)); - } - if (Activation == ActivationFunction::ReLU6) - { - v[out_i][out_j] = vminq_f16(v[out_i][out_j], vdupq_n_f16(6.0f)); - } - } - } - - // Store the output tile - for (unsigned int i = 0; i < OutputTileRows; i++) - { - float16_t* const outptr_row = outptr_base + i*out_row_stride; - for (unsigned int j = 0; j < OutputTileCols; j++) - { - vst1q_f16(outptr_row + j*out_col_stride, v[i][j]); - } - } - outptr_base += 8; - } - for (; channels_remaining; channels_remaining--) - { - // Load input tile - float16_t u[Base::inner_tile_rows][Base::inner_tile_cols]; - for (int i = 0; i < Base::inner_tile_rows; i++) - { - const float16_t* const inptr_row = inptr_base + i*in_row_stride; - for (int j = 0; j < Base::inner_tile_cols; j++) - { - u[i][j] = *(inptr_row + j*in_col_stride); - } - } - inptr_base++; - - // Load weights tile - float16_t bias = *(params++); - float16_t w[KernelRows][KernelCols]; - for (unsigned int i = 0; i < KernelRows; i++) - { - for (unsigned int j = 0; j < KernelCols; j++) - { - w[i][j] = *(params++); - } - } - - // Perform the convolution - float16_t v[OutputTileRows][OutputTileCols]; - for (unsigned int out_i = 0; out_i < OutputTileRows; out_i++) - { - for (unsigned int out_j = 0; out_j < OutputTileCols; out_j++) - { - // Clear the accumulator - v[out_i][out_j] = bias; - - // Base co-ordinate - const int base_i = out_i * StrideRows; - const int base_j = out_j * StrideCols; - - // Fill the accumulator - for (unsigned int in_i = 0; in_i < KernelRows; in_i++) - { - const unsigned int i = base_i + in_i; - for (unsigned int in_j = 0; in_j < KernelCols; in_j++) - { - const int j = base_j + in_j; - v[out_i][out_j] += w[in_i][in_j] * u[i][j]; - } - } - - // Apply the activation function - if (Activation == ActivationFunction::ReLU || - Activation == ActivationFunction::ReLU6) - { - v[out_i][out_j] = std::max<float16_t>(0.0f, v[out_i][out_j]); - } - if (Activation == ActivationFunction::ReLU6) - { - v[out_i][out_j] = std::min<float16_t>(6.0f, v[out_i][out_j]); - } - } - } - - // Store the output tile - for (unsigned int i = 0; i < OutputTileRows; i++) - { - float16_t* const outptr_row = outptr_base + i*out_row_stride; - for (unsigned int j = 0; j < OutputTileCols; j++) - { - *(outptr_row + j*out_col_stride) = v[i][j]; - } - } - outptr_base++; - } -} - -template < - unsigned int OutputTileRows, unsigned int OutputTileCols, - unsigned int KernelRows, unsigned int KernelCols, - unsigned int StrideRows, unsigned int StrideCols -> -template <ActivationFunction Activation> -void DepthwiseConvolution< - OutputTileRows, OutputTileCols, - KernelRows, KernelCols, StrideRows, StrideCols, - float16_t, float16_t, float16_t ->::execute_tile( - int n_channels, - const void *weights_biases_ptr, - const float16_t * inptrs[Base::inner_tile_rows][Base::inner_tile_cols], - float16_t *outptrs[Base::output_tile_rows][Base::output_tile_cols] -) -{ - // Instantiate pointers - const float16_t* __restrict__ params = static_cast<const float16_t*>(weights_biases_ptr); - int n = 0; - - // Perform the depthwise convolution - int channels_remaining = n_channels; - for (; channels_remaining >= 8; channels_remaining -= 8, n += 8) - { - // Load input tile - float16x8_t u[Base::inner_tile_rows][Base::inner_tile_cols]; - for (int i = 0; i < Base::inner_tile_rows; i++) - { - for (int j = 0; j < Base::inner_tile_cols; j++) - { - u[i][j] = vld1q_f16(inptrs[i][j] + n); - } - } - - // Load weights tile - float16x8_t vbias = vld1q_f16(params); - params += 8; - - float16x8_t w[KernelRows][KernelCols]; - for (unsigned int i = 0; i < KernelRows; i++) - { - for (unsigned int j = 0; j < KernelCols; j++) - { - w[i][j] = vld1q_f16(params); - params += 8; - } - } - - // Perform the convolution - float16x8_t v[OutputTileRows][OutputTileCols]; - for (unsigned int out_i = 0; out_i < OutputTileRows; out_i++) - { - for (unsigned int out_j = 0; out_j < OutputTileCols; out_j++) - { - v[out_i][out_j] = vbias; - - // Base co-ordinate - const int base_i = out_i * StrideRows; - const int base_j = out_j * StrideCols; - - // Fill the accumulator - for (unsigned int in_i = 0; in_i < KernelRows; in_i++) - { - const unsigned int i = base_i + in_i; - for (unsigned int in_j = 0; in_j < KernelCols; in_j++) - { - const unsigned int j = base_j + in_j; - - // v[out_i][out_j] += w[in_i][in_j] * u[i][j]; - v[out_i][out_j] = vaddq_f16(v[out_i][out_j], vmulq_f16(w[in_i][in_j], u[i][j])); - } - } - - // Apply the activation function - if (Activation == ActivationFunction::ReLU || - Activation == ActivationFunction::ReLU6) - { - v[out_i][out_j] = vmaxq_f16(v[out_i][out_j], vdupq_n_f16(0.0f)); - } - if (Activation == ActivationFunction::ReLU6) - { - v[out_i][out_j] = vminq_f16(v[out_i][out_j], vdupq_n_f16(6.0f)); - } - } - } - - // Store the output tile - for (unsigned int i = 0; i < OutputTileRows; i++) - { - for (unsigned int j = 0; j < OutputTileCols; j++) - { - vst1q_f16(outptrs[i][j] + n, v[i][j]); - } - } - } - for (; channels_remaining; channels_remaining--, n++) - { - // Load input tile - float16_t u[Base::inner_tile_rows][Base::inner_tile_cols]; - for (int i = 0; i < Base::inner_tile_rows; i++) - { - for (int j = 0; j < Base::inner_tile_cols; j++) - { - u[i][j] = *(inptrs[i][j] + n); - } - } - - // Load weights tile - float16_t bias = *(params++); - float16_t w[KernelRows][KernelCols]; - for (unsigned int i = 0; i < KernelRows; i++) - { - for (unsigned int j = 0; j < KernelCols; j++) - { - w[i][j] = *(params++); - } - } - - // Perform the convolution - float16_t v[OutputTileRows][OutputTileCols]; - for (unsigned int out_i = 0; out_i < OutputTileRows; out_i++) - { - for (unsigned int out_j = 0; out_j < OutputTileCols; out_j++) - { - // Clear the accumulator - v[out_i][out_j] = bias; - - // Base co-ordinate - const int base_i = out_i * StrideRows; - const int base_j = out_j * StrideCols; - - // Fill the accumulator - for (unsigned int in_i = 0; in_i < KernelRows; in_i++) - { - const unsigned int i = base_i + in_i; - for (unsigned int in_j = 0; in_j < KernelCols; in_j++) - { - const int j = base_j + in_j; - v[out_i][out_j] += w[in_i][in_j] * u[i][j]; - } - } - - // Apply the activation function - if (Activation == ActivationFunction::ReLU || - Activation == ActivationFunction::ReLU6) - { - v[out_i][out_j] = std::max<float16_t>(0.0f, v[out_i][out_j]); - } - if (Activation == ActivationFunction::ReLU6) - { - v[out_i][out_j] = std::min<float16_t>(6.0f, v[out_i][out_j]); - } - } - } - - // Store the output tile - for (unsigned int i = 0; i < OutputTileRows; i++) - { - for (unsigned int j = 0; j < OutputTileCols; j++) - { - *(outptrs[i][j] + n) = v[i][j]; - } - } - } -} - -} // namespace depthwise -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |