diff options
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 | 172 |
1 files changed, 169 insertions, 3 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 index 1ae8128d55..cbdb19a067 100644 --- a/src/core/NEON/kernels/convolution/depthwise/impl_fp16_fp16.hpp +++ b/src/core/NEON/kernels/convolution/depthwise/impl_fp16_fp16.hpp @@ -63,7 +63,6 @@ DepthwiseConvolution< { } - template < unsigned int OutputTileRows, unsigned int OutputTileCols, unsigned int KernelRows, unsigned int KernelCols, @@ -92,7 +91,6 @@ void DepthwiseConvolution< // Perform the depthwise convolution int channels_remaining = n_channels; -#ifdef __aarch64__ for (; channels_remaining >= 8; channels_remaining -= 8) { // Load input tile @@ -140,6 +138,8 @@ void DepthwiseConvolution< 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])); } } @@ -168,7 +168,6 @@ void DepthwiseConvolution< } outptr_base += 8; } -#endif // __aarch64__ for (; channels_remaining; channels_remaining--) { // Load input tile @@ -244,5 +243,172 @@ void DepthwiseConvolution< } } +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 |