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Diffstat (limited to 'src/core/NEON/kernels/convolution/depthwise/impl_qa8_qa8.hpp')
| -rw-r--r-- | src/core/NEON/kernels/convolution/depthwise/impl_qa8_qa8.hpp | 325 |
1 files changed, 325 insertions, 0 deletions
diff --git a/src/core/NEON/kernels/convolution/depthwise/impl_qa8_qa8.hpp b/src/core/NEON/kernels/convolution/depthwise/impl_qa8_qa8.hpp index 72f7c6b511..be73065b00 100644 --- a/src/core/NEON/kernels/convolution/depthwise/impl_qa8_qa8.hpp +++ b/src/core/NEON/kernels/convolution/depthwise/impl_qa8_qa8.hpp @@ -631,4 +631,329 @@ void QAsymm8DepthwiseConvolution< } } +template < + unsigned int OutputTileRows, unsigned int OutputTileCols, + unsigned int KernelRows, unsigned int KernelCols, + unsigned int StrideRows, unsigned int StrideCols +> +template<ActivationFunction Activation> +void QAsymm8DepthwiseConvolution< + OutputTileRows, OutputTileCols, KernelRows, KernelCols, StrideRows, StrideCols +>::execute_tile( + int n_channels, + const void* packed_params, + const uint8_t* inptrs[Base::inner_tile_rows][Base::inner_tile_cols], + uint8_t* outptrs[Base::output_tile_rows][Base::output_tile_cols] +) +{ + // Activation parameters (unused if Activation is None) + const uint8_t aqmin = _output_quant.offset; + const uint8_t aqmax = (Activation == ActivationFunction::ReLU6) ? + std::min<uint8_t>(255u, _output_quant.quantize(6.0f)) : 255u; + + // Byte type pointer to weights and biases + const uint8_t *wbptr = static_cast<const uint8_t *>(packed_params); + + // Offset into input/output tensors + int n = 0; + +#if defined(__aarch64__) // Under Aarch64 only use quad registers + for (; n_channels >= 16; n_channels -= 16, n += 16) + { + // Load biases + const int32x4_t biases[4] = { + vld1q_s32(reinterpret_cast<const int32_t *>(wbptr)), + vld1q_s32(reinterpret_cast<const int32_t *>(wbptr) + 4), + vld1q_s32(reinterpret_cast<const int32_t *>(wbptr) + 8), + vld1q_s32(reinterpret_cast<const int32_t *>(wbptr) + 12) + }; + wbptr += 16*sizeof(int32_t); + + // Load weights + uint8x16_t weights[KernelRows][KernelCols]; + for (unsigned int i = 0; i < KernelRows; i++) + { + for (unsigned int j = 0; j < KernelCols; j++) + { + weights[i][j] = vld1q_u8(wbptr); + wbptr += 16; + } + } + + // Load the input activations + uint8x16_t inputs[Base::inner_tile_rows][Base::inner_tile_cols]; + for (unsigned int i = 0; i < Base::inner_tile_rows; i++) + { + for (unsigned int j = 0; j < Base::inner_tile_cols; j++) + { + inputs[i][j] = vld1q_u8(inptrs[i][j] + n); + } + } + + // Perform the convolution + for (unsigned int oi = 0; oi < OutputTileRows; oi++) + { + for (unsigned int oj = 0; oj < OutputTileCols; oj++) + { + // Two sets of operations are required, we perform the + // multiply-accumulates for the convolution proper but must also sum + // the tile elements to account for the _weight_ offset. + uint32x4_t accs[4]; + for (unsigned int i = 0; i < 4; i++) + { + accs[i] = reinterpret_cast<uint32x4_t>(biases[i]); + } + + for (unsigned int wi = 0; wi < KernelRows; wi++) + { + for (unsigned int wj = 0; wj < KernelCols; wj++) + { + // Get relevant weight and activation pixel + const uint8x16_t w = weights[wi][wj]; + const uint8x16_t x = inputs[oi*StrideRows + wi][oj*StrideCols + wj]; + + // Perform multiplication and accumulation + const uint16x8_t muls[2] = { + vmull_u8(vget_low_u8(w), vget_low_u8(x)), + vmull_u8(vget_high_u8(w), vget_high_u8(x)) + }; + + const uint8x8_t woffset = vdup_n_u8(_weights_quant.offset); + const uint16x8_t sum_elems[2] = { + vmull_u8(vget_low_u8(x), woffset), + vmull_u8(vget_high_u8(x), woffset) + }; + + const uint32x4_t tmps[4] = { + vsubl_u16(vget_low_u16(muls[0]), vget_low_u16(sum_elems[0])), + vsubl_u16(vget_high_u16(muls[0]), vget_high_u16(sum_elems[0])), + vsubl_u16(vget_low_u16(muls[1]), vget_low_u16(sum_elems[1])), + vsubl_u16(vget_high_u16(muls[1]), vget_high_u16(sum_elems[1])), + }; + for (unsigned int i = 0; i < 4; i++) + { + accs[i] = vaddq_u32(accs[i], tmps[i]); + } + } + } + + // Rescale the accumulator and add in the new offset. + uint32x4_t final_accs[4]; + for (unsigned int i = 0; i < 4; i++) + { +#ifdef FIXED_POINT_REQUANTISATION + const int32x4_t y = rounding_divide_by_exp2( + saturating_doubling_high_mul( + reinterpret_cast<int32x4_t>(accs[i]), rescale_parameters.multiplier + ), + rescale_parameters.shift + ); + const int32x4_t offset = reinterpret_cast<int32x4_t>(vdupq_n_u32(_output_quant.offset)); + final_accs[i] = reinterpret_cast<uint32x4_t>(vmaxq_s32(vaddq_s32(y, offset), vdupq_n_s32(0))); +#else // floating point requantisation + float32x4_t fp_acc = vcvtq_f32_s32(reinterpret_cast<int32x4_t>(accs[i])); + fp_acc = vmulq_f32(fp_acc, vdupq_n_f32(rescale_parameters.rescale)); + fp_acc = vaddq_f32(fp_acc, vdupq_n_f32(static_cast<float>(_output_quant.offset))); + fp_acc = vmaxq_f32(fp_acc, vdupq_n_f32(0.0f)); + final_accs[i] = vcvtq_u32_f32(fp_acc); +#endif + } + + uint8x16_t output = vcombine_u8( + vqmovn_u16(vcombine_u16(vqmovn_u32(final_accs[0]), vqmovn_u32(final_accs[1]))), + vqmovn_u16(vcombine_u16(vqmovn_u32(final_accs[2]), vqmovn_u32(final_accs[3]))) + ); + + // Apply the activation function + if (Activation == ActivationFunction::ReLU || + Activation == ActivationFunction::ReLU6) + { + output = vmaxq_u8(output, vdupq_n_u8(aqmin)); + } + if (Activation == ActivationFunction::ReLU6) + { + output = vminq_u8(output, vdupq_n_u8(aqmax)); + } + + vst1q_u8(outptrs[oi][oj] + n, output); + } + } + } +#endif // defined(__aarch64__) + for (; n_channels >= 8; n_channels -= 8, n += 8) + { + const int32x4_t biases[2] = { + vld1q_s32(reinterpret_cast<const int32_t *>(wbptr)), + vld1q_s32(reinterpret_cast<const int32_t *>(wbptr) + 4), + }; + wbptr += 8*sizeof(int32_t); + + uint8x8_t weights[KernelRows][KernelCols]; + for (unsigned int i = 0; i < KernelRows; i++) + { + for (unsigned int j = 0; j < KernelCols; j++) + { + weights[i][j] = vld1_u8(wbptr); + wbptr += 8; + } + } + + uint8x8_t inputs[Base::inner_tile_rows][Base::inner_tile_cols]; + for (unsigned int i = 0; i < Base::inner_tile_rows; i++) + { + for (unsigned int j = 0; j < Base::inner_tile_cols; j++) + { + inputs[i][j] = vld1_u8(inptrs[i][j] + n); + } + } + + for (unsigned int oi = 0; oi < OutputTileRows; oi++) + { + for (unsigned int oj = 0; oj < OutputTileCols; oj++) + { + uint32x4_t accs[2]; + for (unsigned int i = 0; i < 2; i++) + { + accs[i] = reinterpret_cast<uint32x4_t>(biases[i]); + } + + for (unsigned int wi = 0; wi < KernelRows; wi++) + { + for (unsigned int wj = 0; wj < KernelCols; wj++) + { + const uint8x8_t w = weights[wi][wj]; + const uint8x8_t x = inputs[oi*StrideRows + wi][oj*StrideCols + wj]; + + const uint16x8_t muls = vmull_u8(w, x); + const uint8x8_t woffset = vdup_n_u8(_weights_quant.offset); + const uint16x8_t sum_elems = vmull_u8(x, woffset); + + const uint32x4_t tmps[2] = { + vsubl_u16(vget_low_u16(muls), vget_low_u16(sum_elems)), + vsubl_u16(vget_high_u16(muls), vget_high_u16(sum_elems)), + }; + for (unsigned int i = 0; i < 2; i++) + { + accs[i] = vaddq_u32(accs[i], tmps[i]); + } + } + } + + uint32x4_t final_accs[2]; + for (unsigned int i = 0; i < 2; i++) + { +#ifdef FIXED_POINT_REQUANTISATION + const int32x4_t y = rounding_divide_by_exp2( + saturating_doubling_high_mul( + reinterpret_cast<int32x4_t>(accs[i]), rescale_parameters.multiplier + ), + rescale_parameters.shift + ); + const int32x4_t offset = reinterpret_cast<int32x4_t>(vdupq_n_u32(_output_quant.offset)); + final_accs[i] = reinterpret_cast<uint32x4_t>(vmaxq_s32(vaddq_s32(y, offset), vdupq_n_s32(0))); +#else // floating point requantisation + float32x4_t fp_acc = vcvtq_f32_s32(reinterpret_cast<int32x4_t>(accs[i])); + fp_acc = vmulq_f32(fp_acc, vdupq_n_f32(rescale_parameters.rescale)); + fp_acc = vaddq_f32(fp_acc, vdupq_n_f32(static_cast<float>(_output_quant.offset))); + fp_acc = vmaxq_f32(fp_acc, vdupq_n_f32(0.0f)); + final_accs[i] = vcvtq_u32_f32(fp_acc); +#endif + } + + uint8x8_t output = vqmovn_u16(vcombine_u16(vqmovn_u32(final_accs[0]), vqmovn_u32(final_accs[1]))); + + // Apply the activation function + if (Activation == ActivationFunction::ReLU || + Activation == ActivationFunction::ReLU6) + { + output = vmax_u8(output, vdup_n_u8(aqmin)); + } + if (Activation == ActivationFunction::ReLU6) + { + output = vmin_u8(output, vdup_n_u8(aqmax)); + } + + vst1_u8(outptrs[oi][oj] + n, output); + } + } + } + for (; n_channels; n_channels--, n++) + { + // Load bias + const int32_t bias = *reinterpret_cast<const int32_t *>(wbptr); + wbptr += sizeof(int32_t); + + // Load weights + uint8_t weights[KernelRows][KernelCols]; + for (unsigned int i = 0; i < KernelRows; i++) + { + for (unsigned int j = 0; j < KernelCols; j++) + { + weights[i][j] = *(wbptr++); + } + } + + // Load the input activations + uint8_t inputs[Base::inner_tile_rows][Base::inner_tile_cols]; + for (unsigned int i = 0; i < Base::inner_tile_rows; i++) + { + for (unsigned int j = 0; j < Base::inner_tile_cols; j++) + { + inputs[i][j] = *(inptrs[i][j] + n); + } + } + + // Perform the convolution + for (unsigned int oi = 0; oi < OutputTileRows; oi++) + { + for (unsigned int oj = 0; oj < OutputTileCols; oj++) + { + int32_t acc = bias; + uint32_t element_sum = 0; + + for (unsigned int wi = 0; wi < KernelRows; wi++) + { + for (unsigned int wj = 0; wj < KernelCols; wj++) + { + const auto w = weights[wi][wj], x = inputs[oi*StrideRows + wi][oj*StrideCols + wj]; + acc += static_cast<int32_t>(static_cast<uint32_t>(w) * static_cast<uint32_t>(x)); + element_sum += static_cast<uint32_t>(x); + } + } + + acc -= static_cast<int32_t>(element_sum) * static_cast<int32_t>(_weights_quant.offset); + + // Requantize +#ifdef FIXED_POINT_REQUANTISATION + acc = rounding_divide_by_exp2( + saturating_doubling_high_mul(acc, rescale_parameters.multiplier), + rescale_parameters.shift + ); + acc += _output_quant.offset; + uint8_t output = clamp_to_limits<uint8_t>::clamp_and_cast<int32_t>(acc); +#else // floating point requantization + float fp_acc = static_cast<float>(acc); + fp_acc *= rescale_parameters.rescale; + fp_acc += static_cast<float>(_output_quant.offset); + fp_acc = std::max<float>(fp_acc, 0.0f); + uint8_t output = static_cast<uint8_t>(std::min<int32_t>(static_cast<int32_t>(fp_acc), 255)); +#endif + + // Apply the activation function + if (Activation == ActivationFunction::ReLU || + Activation == ActivationFunction::ReLU6) + { + output = std::max(output, aqmin); + } + if (Activation == ActivationFunction::ReLU6) + { + output = std::min(output, aqmax); + } + + *(outptrs[oi][oj] + n) = output; + } + } + } +} + } // namespace depthwise |