Relax restrictions on aconv-based Conv2D - channel_depth is now mod4 - output_width is only required to be at least 8 Change-Id: I7989f17260d84c666247ccb84d000b2219856389
diff --git a/tflm/opt/BUILD b/tflm/opt/BUILD index 277eef2..28dde26 100644 --- a/tflm/opt/BUILD +++ b/tflm/opt/BUILD
@@ -22,6 +22,7 @@ "conv_s8.cc", "conv_s8_1x1.cc", "conv_s8_3x1_d48.cc", + "conv_s8_d4.cc", "conv_s8_d32.cc", "depthwise_conv_s16.cc", "depthwise_conv_s8.cc",
diff --git a/tflm/opt/conv_s8.cc b/tflm/opt/conv_s8.cc index 4582b9f..7d7d0ba 100644 --- a/tflm/opt/conv_s8.cc +++ b/tflm/opt/conv_s8.cc
@@ -189,19 +189,27 @@ const auto filter_height = filter_shape.Dims(1); const auto filter_width = filter_shape.Dims(2); const auto filter_depth = filter_shape.Dims(3); + const auto output_width = output_shape.Dims(2); const auto output_depth = output_shape.Dims(3); // use generic implementation by default auto fn = ConvS8Generic; - // special case of filter depth = 32n + // special case of filter_depth = 4n if (dilation_width_factor == 1 && dilation_height_factor == 1 && + stride_width <= 2 && stride_height <= 2 && filter_depth % 4 == 0 && + output_depth % 8 == 0 && output_width >= 8 && pad_width <= 1) { + fn = kelvin::opt::ConvS8D4; + } + + // special case of filter depth = 32n + else if (dilation_width_factor == 1 && dilation_height_factor == 1 && stride_width <= 2 && stride_height <= 2 && filter_depth % 32 == 0) { fn = kelvin::opt::ConvS8D32; } // special case of filter size 1x1 - if (filter_height == 1 && filter_width == 1 && stride_height == 1 && + else if (filter_height == 1 && filter_width == 1 && stride_height == 1 && stride_width == 1 && dilation_height_factor == 1 && dilation_width_factor == 1 && pad_height == 0 && pad_width == 0 && (output_depth % 8) == 0 && (input_depth % 32) == 0) { @@ -210,7 +218,7 @@ } // special case of filter size 48x3x1x48 - if (batches == 1 && filter_height == 3 && filter_width == 1 && + else if (batches == 1 && filter_height == 3 && filter_width == 1 && input_width == 1 && input_depth == 48 && output_depth == 48 && stride_height == 1 && stride_width == 1 && dilation_height_factor == 1 && dilation_width_factor == 1 && pad_height == 0 && pad_width == 0) {
diff --git a/tflm/opt/conv_s8.h b/tflm/opt/conv_s8.h index e1d88ef..02dd79b 100644 --- a/tflm/opt/conv_s8.h +++ b/tflm/opt/conv_s8.h
@@ -33,6 +33,16 @@ const int32_t* bias_data, const tflite::RuntimeShape& output_shape, int8_t* output_data); +// filter depth 4n +void ConvS8D4(const tflite::ConvParams& params, + const int32_t* output_multiplier, const int32_t* output_shift, + const tflite::RuntimeShape& input_shape, + const int8_t* input_data, + const tflite::RuntimeShape& filter_shape, + const int8_t* filter_data, + const tflite::RuntimeShape& bias_shape, const int32_t* bias_data, + const tflite::RuntimeShape& output_shape, int8_t* output_data); + // filter depth 32n void ConvS8D32(const tflite::ConvParams& params, const int32_t* output_multiplier, const int32_t* output_shift,
diff --git a/tflm/opt/conv_s8_d32.cc b/tflm/opt/conv_s8_d32.cc index e3e7e10..6572ae8 100644 --- a/tflm/opt/conv_s8_d32.cc +++ b/tflm/opt/conv_s8_d32.cc
@@ -21,239 +21,6 @@ #include "tflm/opt/conv_util.h" namespace kelvin::opt { -namespace { -void ConvS8D32Pw1Ow8Id8( - const tflite::ConvParams& params, const int32_t* output_multiplier, - const int32_t* output_shift, const tflite::RuntimeShape& input_shape, - const int8_t* input_data, const tflite::RuntimeShape& filter_shape, - const int8_t* filter_data, const tflite::RuntimeShape& bias_shape, - const int32_t* bias_data, const tflite::RuntimeShape& output_shape, - int8_t* output_data) { - // Get parameters. - const int32_t input_offset = params.input_offset; // r = s(q - Z) - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int32_t output_offset = params.output_offset; - - // Set min and max value of the output. - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - - // Consistency check. - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = input_shape.Dims(3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - - // Check dimensions of the tensors. - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int filter_input_depth = filter_shape.Dims(3); - const int groups = input_depth / filter_input_depth; - TFLITE_DCHECK_NE(groups, 0); - TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0); - const int filters_per_group = output_depth / groups; - TFLITE_DCHECK_NE(filters_per_group, 0); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - - union { - vconv_u8_t conv; - uint32_t raw; - } cmds; - cmds.conv.mode = 0; - cmds.conv.start = 0; - cmds.conv.stop = 7; - cmds.conv.sbias1 = input_offset; - cmds.conv.sdata1 = true; - cmds.conv.sbias2 = 0; - cmds.conv.sdata2 = true; - - const size_t swizzled_filter_data_size = - 8 * filter_height * filter_width * filter_input_depth; - std::unique_ptr<int8_t> swizzled_filter_data(reinterpret_cast<int8_t*>( - ::aligned_alloc(32, swizzled_filter_data_size))); - int8_t* p_swizzled_filter_data = swizzled_filter_data.get(); - int32_t swizzled_bias_data[32]; - int32_t swizzled_mult_data[32]; - int32_t swizzled_shift_data[32]; - - for (int out_channel = 0; out_channel + 8 <= output_depth; out_channel += 8) { - Filter_N_H_W_M<8>(filter_data + (out_channel * filter_height * - filter_width * filter_input_depth), - p_swizzled_filter_data, filter_height, filter_width, - filter_input_depth); - Swizzle(bias_data + out_channel, swizzled_bias_data, 8); - Swizzle(output_multiplier + out_channel, swizzled_mult_data, 8); - Swizzle(output_shift + out_channel, swizzled_shift_data, 8); - vld_w_x_m(v16, swizzled_bias_data); - vld_w_x_m(v20, swizzled_mult_data); - vld_w_x_m(v24, swizzled_shift_data); - vrsub_w_vx_m(v24, v24, 0); - - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - const int in_y_origin = (out_y * stride_height) - pad_height; - for (int out_x = 0; out_x + 8 <= output_width; out_x += 8) { - // 8x accumulators - vdup_w_x_m(v48, 0); - vdup_w_x_m(v52, 0); - acset_v(v48, v48); - for (int in_channel = 0; in_channel + 32 <= filter_input_depth; - in_channel += 32) { - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - const int in_y = in_y_origin + dilation_height_factor * filter_y; - const bool is_row_inside_input = - (in_y >= 0) && (in_y < input_height); - if (!is_row_inside_input) { - continue; - } - - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - int in_x[8]; - bool left_pad = false; - bool right_pad = false; - for (int i = 0; i < 8; ++i) { - const int in_x_origin = - ((out_x + i) * stride_width) - pad_width; - in_x[i] = in_x_origin + dilation_width_factor * filter_x; - if (in_x[i] < 0) { - left_pad = true; - } - if (in_x[i] >= input_width) { - right_pad = true; - } - } - - if (left_pad) { - vdup_b_x(v0, -input_offset); - vld_b_s_xx( - v1, - &input_data[tflite::Offset(input_shape, batch, in_y, - in_x[1], in_channel)], - input_depth * stride_width); - vld_b_s_xx( - v2, - &input_data[tflite::Offset(input_shape, batch, in_y, - in_x[2], in_channel)], - input_depth * stride_width); - vld_b_s_xx( - v3, - &input_data[tflite::Offset(input_shape, batch, in_y, - in_x[3], in_channel)], - input_depth * stride_width); - vld_b_s_xx_m( - v4, - &input_data[tflite::Offset(input_shape, batch, in_y, - in_x[4], in_channel)], - input_depth * stride_width); - } else if (right_pad) { - vld_b_s_xx_m( - v0, - &input_data[tflite::Offset(input_shape, batch, in_y, - in_x[0], in_channel)], - input_depth * stride_width); - vld_b_s_xx( - v4, - &input_data[tflite::Offset(input_shape, batch, in_y, - in_x[4], in_channel)], - input_depth * stride_width); - vld_b_s_xx( - v5, - &input_data[tflite::Offset(input_shape, batch, in_y, - in_x[5], in_channel)], - input_depth * stride_width); - vld_b_s_xx( - v6, - &input_data[tflite::Offset(input_shape, batch, in_y, - in_x[6], in_channel)], - input_depth * stride_width); - vdup_b_x(v7, -input_offset); - } else if (!left_pad && !right_pad) { - // Inputs - vld_b_s_xx_m( - v0, - &input_data[tflite::Offset(input_shape, batch, in_y, - in_x[0], in_channel)], - input_depth * stride_width); - vld_b_s_xx_m( - v4, - &input_data[tflite::Offset(input_shape, batch, in_y, - in_x[4], in_channel)], - input_depth * stride_width); - } else { - vdup_b_x(v0, -input_offset); - vdup_b_x(v7, -input_offset); - vld_b_s_xx_m( - v1, - &input_data[tflite::Offset(input_shape, batch, in_y, - in_x[1], in_channel)], - input_depth * stride_width); - vld_b_s_xx( - v5, - &input_data[tflite::Offset(input_shape, batch, in_y, - in_x[5], in_channel)], - input_depth * stride_width); - vld_b_s_xx( - v6, - &input_data[tflite::Offset(input_shape, batch, in_y, - in_x[6], in_channel)], - input_depth * stride_width); - } - size_t local_filter_offset = - (filter_y * filter_width * 8 * input_depth) + - (filter_x * 8 * input_depth) + (in_channel * 8); - int8_t* p_local_filter_start = - p_swizzled_filter_data + local_filter_offset; - vld_b_p_x_m(v8, p_local_filter_start); - vld_b_x_m(v12, p_local_filter_start); - - aconv_vxv(v48, v0, cmds, v8); - } - } - } - vcget(v48); - vadd_w_vv_m(v48, v48, v16); - vadd_w_vv_m(v52, v52, v16); - vdmulh_w_r_vv_m(v48, v48, v20); - vdmulh_w_r_vv_m(v52, v52, v20); - vsha_w_r_vv_m(v48, v48, v24); - vsha_w_r_vv_m(v52, v52, v24); - vadd_w_vx_m(v48, v48, output_offset); - vadd_w_vx_m(v52, v52, output_offset); - vmin_w_vx_m(v48, v48, output_activation_max); - vmin_w_vx_m(v52, v52, output_activation_max); - vmax_w_vx_m(v48, v48, output_activation_min); - vmax_w_vx_m(v52, v52, output_activation_min); - vsraqs_b_vx(v56, v48, 0); - vsraqs_b_vx(v57, v52, 0); - vstq_b_s_xx(v56, - &output_data[tflite::Offset(output_shape, batch, out_y, - out_x, out_channel)], - output_depth); - vstq_b_s_xx(v57, - &output_data[tflite::Offset(output_shape, batch, out_y, - out_x + 4, out_channel)], - output_depth); - } - } - } - } -} - -} // namespace // Fixed-point per-channel-quantization convolution reference kernel. void ConvS8D32(const tflite::ConvParams& params, @@ -304,14 +71,6 @@ const int output_height = output_shape.Dims(1); const int output_width = output_shape.Dims(2); - // filter_depth = 32n && input_channels = 8n && output_width = 8n - if (output_depth % 8 == 0 && output_width % 8 == 0 && pad_width <= 1) { - ConvS8D32Pw1Ow8Id8(params, output_multiplier, output_shift, input_shape, - input_data, filter_shape, filter_data, bias_shape, - bias_data, output_shape, output_data); - return; - } - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) {
diff --git a/tflm/opt/conv_s8_d4.cc b/tflm/opt/conv_s8_d4.cc new file mode 100644 index 0000000..0dd3e50 --- /dev/null +++ b/tflm/opt/conv_s8_d4.cc
@@ -0,0 +1,384 @@ +/* + * Copyright 2024 Google LLC + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +// Convolution based on Kelvin ops +// Data types: input: s8, filter: s8, bias: s32 +// Special case for filter depth = 4n + +#include <cstdlib> +#include <memory> + +#include "crt/kelvin.h" +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h" +#include "tensorflow/lite/kernels/internal/runtime_shape.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace kelvin::opt { +namespace { +void Filter_8_H_W_M(const int8_t* input, int8_t* output, int H, int W, int M) { + const int8_t(&in)[8][H][W][M] = *(int8_t(*)[8][H][W][M])input; + int8_t(&out)[H][W][M / 4][8][4] = *(int8_t(*)[H][W][M / 4][8][4]) output; + assert(M >= 4); + for (int zo = 0; zo < 8; ++zo) { + for (int ky = 0; ky < H; ++ky) { + for (int kx = 0; kx < W; ++kx) { + for (int zi = 0; zi < M; ++zi) { + const int zi_hi = zi >> 2; // div4 + const int zi_lo = zi & 3; // rem4 + out[ky][kx][zi_hi][zo][zi_lo] = in[zo][ky][kx][zi]; + } + } + } + } +} + +void Swizzle(const int32_t* input, int32_t* output, int N) { + const int32_t(&in)[N] = *(int32_t(*)[N])input; + int32_t(&out)[N * 4] = *(int32_t(*)[N * 4]) output; + // Convert to accumulator swizzle pattern. + for (int i = 0; i < N / 8; ++i) { + int32_t* out0 = out + i * 32 + 0; + int32_t* out1 = out + i * 32 + 16; + int32_t* out2 = out + i * 32 + 8; + int32_t* out3 = out + i * 32 + 24; + for (int j = 0; j < 4; ++j) { + const int32_t* p_in = in + i * 8; + for (int k = 0; k < 2; ++k) { + *out0++ = *p_in++; + *out1++ = *p_in++; + *out2++ = *p_in++; + *out3++ = *p_in++; + } + } + } +} +} // namespace + +void ConvS8D4( + const tflite::ConvParams& params, const int32_t* output_multiplier, + const int32_t* output_shift, const tflite::RuntimeShape& input_shape, + const int8_t* input_data, const tflite::RuntimeShape& filter_shape, + const int8_t* filter_data, const tflite::RuntimeShape& bias_shape, + const int32_t* bias_data, const tflite::RuntimeShape& output_shape, + int8_t* output_data) { + // Get parameters. + const int32_t input_offset = params.input_offset; // r = s(q - Z) + const int32_t neg_input_offset = -params.input_offset; // r = s(q - Z) + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int dilation_width_factor = params.dilation_width_factor; + const int dilation_height_factor = params.dilation_height_factor; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + const int32_t output_offset = params.output_offset; + + // Set min and max value of the output. + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + + // Consistency check. + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int input_depth = input_shape.Dims(3); + const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); + if (bias_data) { + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + } + + // Check dimensions of the tensors. + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int filter_input_depth = filter_shape.Dims(3); + const int groups = input_depth / filter_input_depth; + TFLITE_DCHECK_NE(groups, 0); + TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0); + const int filters_per_group = output_depth / groups; + TFLITE_DCHECK_NE(filters_per_group, 0); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + + union { + vconv_u8_t conv; + uint32_t raw; + } cmds; + cmds.conv.mode = 0; + cmds.conv.start = 0; + cmds.conv.stop = 7; + cmds.conv.sbias1 = input_offset; + cmds.conv.sdata1 = true; + cmds.conv.sbias2 = 0; + cmds.conv.sdata2 = true; + + const size_t swizzled_filter_data_size = + 8 * filter_height * filter_width * filter_input_depth; + std::unique_ptr<int8_t> swizzled_filter_data(reinterpret_cast<int8_t*>( + ::aligned_alloc(32, swizzled_filter_data_size))); + int8_t* p_swizzled_filter_data = swizzled_filter_data.get(); + int32_t swizzled_bias_data[32]; + int32_t swizzled_mult_data[32]; + int32_t swizzled_shift_data[32]; + + for (int out_channel = 0; out_channel + 8 <= output_depth; out_channel += 8) { + Filter_8_H_W_M(filter_data + (out_channel * filter_height * filter_width * + filter_input_depth), + p_swizzled_filter_data, filter_height, filter_width, + filter_input_depth); + Swizzle(bias_data + out_channel, swizzled_bias_data, 8); + Swizzle(output_multiplier + out_channel, swizzled_mult_data, 8); + Swizzle(output_shift + out_channel, swizzled_shift_data, 8); + vld_w_x_m(v16, swizzled_bias_data); + vld_w_x_m(v20, swizzled_mult_data); + vld_w_x_m(v24, swizzled_shift_data); + vrsub_w_vx_m(v24, v24, 0); + + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + const int in_y_origin = (out_y * stride_height) - pad_height; + int out_x = 0; + do { + int out_xs_this_iter = std::min(8, output_width - out_x); + // 8x accumulators + vdup_w_x_m(v48, 0); + vdup_w_x_m(v52, 0); + acset_v(v48, v48); + int in_channel = 0; + do { + int channels_this_iter = std::min(filter_input_depth, 32); + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + const int in_y = in_y_origin + dilation_height_factor * filter_y; + const bool is_row_inside_input = + (in_y >= 0) && (in_y < input_height); + if (!is_row_inside_input) { + continue; + } + + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + int in_x[8]; + bool right_pad = false; + int first_right_pad = -1; + for (int i = 0; i < 8; ++i) { + const int in_x_origin = + ((out_x + i) * stride_width) - pad_width; + in_x[i] = in_x_origin + dilation_width_factor * filter_x; + } + bool left_pad = (in_x[0] < 0); + for (int i = 7; i >= 0; --i) { + if (in_x[i] < input_width) { + break; + } + right_pad = true; + first_right_pad = i; + } + + if (left_pad) { + vdup_b_x(v0, -input_offset); + vld_b_s_xx( + v1, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[1], in_channel)], + input_depth * stride_width); + vld_b_s_xx( + v2, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[2], in_channel)], + input_depth * stride_width); + vld_b_s_xx( + v3, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[3], in_channel)], + input_depth * stride_width); + vld_b_s_xx_m( + v4, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[4], in_channel)], + input_depth * stride_width); + } else if (right_pad) { + int first_pad = std::min(first_right_pad, out_xs_this_iter); + switch (first_pad) { + case 0: + vdup_b_x(v0, neg_input_offset); + case 1: + vdup_b_x(v1, neg_input_offset); + case 2: + vdup_b_x(v2, neg_input_offset); + case 3: + vdup_b_x(v3, neg_input_offset); + case 4: + vdup_b_x(v4, neg_input_offset); + case 5: + vdup_b_x(v5, neg_input_offset); + case 6: + vdup_b_x(v6, neg_input_offset); + case 7: + vdup_b_x(v7, neg_input_offset); + } + switch (8 - first_pad) { // rest (stripmines?) + case 0: + vld_b_s_xx( + v7, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[7], in_channel)], + input_depth * stride_width); + case 1: + vld_b_s_xx( + v6, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[6], in_channel)], + input_depth * stride_width); + case 2: + vld_b_s_xx( + v5, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[5], in_channel)], + input_depth * stride_width); + case 3: + vld_b_s_xx( + v4, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[4], in_channel)], + input_depth * stride_width); + case 4: + vld_b_s_xx( + v3, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[3], in_channel)], + input_depth * stride_width); + case 5: + vld_b_s_xx( + v2, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[2], in_channel)], + input_depth * stride_width); + case 6: + vld_b_s_xx( + v1, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[1], in_channel)], + input_depth * stride_width); + case 7: + vld_b_s_xx( + v0, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[0], in_channel)], + input_depth * stride_width); + } + } else if (!left_pad && !right_pad) { + // Inputs + vld_b_s_xx_m( + v0, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[0], in_channel)], + input_depth * stride_width); + vld_b_s_xx_m( + v4, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[4], in_channel)], + input_depth * stride_width); + } else { + vdup_b_x(v0, -input_offset); + vdup_b_x(v7, -input_offset); + vld_b_s_xx_m( + v1, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[1], in_channel)], + input_depth * stride_width); + vld_b_s_xx( + v5, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[5], in_channel)], + input_depth * stride_width); + vld_b_s_xx( + v6, + &input_data[tflite::Offset(input_shape, batch, in_y, + in_x[6], in_channel)], + input_depth * stride_width); + } + size_t local_filter_offset = + (filter_y * filter_width * 8 * input_depth) + + (filter_x * 8 * input_depth) + (in_channel * 8); + int8_t* p_local_filter_start = + p_swizzled_filter_data + local_filter_offset; + vld_b_p_x_m(v8, p_local_filter_start); + vld_b_x_m(v12, p_local_filter_start); + + cmds.conv.stop = (channels_this_iter / 4) - 1; + aconv_vxv(v48, v0, cmds, v8); + } + } + in_channel += channels_this_iter; + } while (in_channel < filter_input_depth); + vcget(v48); + vadd_w_vv_m(v48, v48, v16); + vadd_w_vv_m(v52, v52, v16); + vdmulh_w_rn_vv_m(v48, v48, v20); + vdmulh_w_rn_vv_m(v52, v52, v20); + vsha_w_r_vv_m(v48, v48, v24); + vsha_w_r_vv_m(v52, v52, v24); + vadd_w_vx_m(v48, v48, output_offset); + vadd_w_vx_m(v52, v52, output_offset); + vmin_w_vx_m(v48, v48, output_activation_max); + vmin_w_vx_m(v52, v52, output_activation_max); + vmax_w_vx_m(v48, v48, output_activation_min); + vmax_w_vx_m(v52, v52, output_activation_min); + vsraqs_b_vx(v56, v48, 0); + vsraqs_b_vx(v57, v52, 0); + if (out_xs_this_iter >= 4) { + vstq_b_s_xx(v56, + &output_data[tflite::Offset(output_shape, batch, out_y, + out_x, out_channel)], + output_depth); + } else { + for (int i = 0; i < out_xs_this_iter; ++i) { + if (i > 0) { + vsliden_b_4_vv(v58, v56, v0); + vsliden_b_4_vv(v56, v58, v0); + } + vst_b_l_xx(v56, + &output_data[tflite::Offset(output_shape, batch, out_y, + out_x + i, out_channel)], + 8); + } + } + if (out_xs_this_iter == 8) { + vstq_b_s_xx(v57, + &output_data[tflite::Offset(output_shape, batch, out_y, + out_x + 4, out_channel)], + output_depth); + } else if (out_xs_this_iter > 4) { + for (int i = 4; i < out_xs_this_iter; ++i) { + if (i > 4) { + vsliden_b_4_vv(v58, v57, v0); + vsliden_b_4_vv(v57, v58, v0); + } + vst_b_l_xx(v57, + &output_data[tflite::Offset(output_shape, batch, out_y, + out_x + i, out_channel)], + 8); + } + } + out_x += out_xs_this_iter; + } while (out_x < output_width); + } + } + } +} +} // namespace kelvin::opt