| /* |
| * 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. |
| */ |
| |
| #include <algorithm> |
| |
| #include "tflm/opt/conv_util.h" |
| |
| namespace kelvin::opt { |
| namespace { |
| void JumptableSwizzle(const int32_t* input, int32_t* output, int n) { |
| switch (n) { |
| case 32: |
| output[7] = input[28]; |
| output[15] = input[30]; |
| output[23] = input[29]; |
| output[31] = input[31]; |
| case 28: |
| output[6] = input[24]; |
| output[14] = input[26]; |
| output[22] = input[25]; |
| output[30] = input[27]; |
| case 24: |
| output[5] = input[20]; |
| output[13] = input[22]; |
| output[21] = input[21]; |
| output[29] = input[23]; |
| case 20: |
| output[4] = input[16]; |
| output[12] = input[18]; |
| output[20] = input[17]; |
| output[28] = input[19]; |
| case 16: |
| output[27] = input[15]; |
| output[19] = input[13]; |
| output[11] = input[14]; |
| output[3] = input[12]; |
| case 12: |
| output[2] = input[8]; |
| output[10] = input[10]; |
| output[18] = input[9]; |
| output[26] = input[11]; |
| case 8: |
| output[1] = input[4]; |
| output[9] = input[6]; |
| output[17] = input[5]; |
| output[25] = input[7]; |
| case 4: |
| output[0] = input[0]; |
| output[8] = input[2]; |
| output[16] = input[1]; |
| output[24] = input[3]; |
| } |
| } |
| |
| } // namespace |
| |
| #define CALCULATE_IN_X(in_x_origin) \ |
| { \ |
| _Pragma("GCC unroll 5") for (int i = 0; i < 5; ++i) { \ |
| in_x[i] = in_x_origin + (dilation_width_factor * i); \ |
| } \ |
| } |
| |
| #define CALCULATE_IN_Y(in_y_origin) \ |
| { \ |
| _Pragma("GCC unroll 5") for (int i = 0; i < 5; ++i) { \ |
| in_y[i] = in_y_origin + (dilation_height_factor * i); \ |
| } \ |
| } |
| |
| #define PAD_ROW_0(input_offset) \ |
| { \ |
| vdup_b_x(v27, -input_offset); \ |
| vdup_b_x(v28, -input_offset); \ |
| vdup_b_x(v29, -input_offset); \ |
| vdup_b_x(v30, -input_offset); \ |
| vdup_b_x(v31, -input_offset); \ |
| } |
| #define PAD_ROW_1(input_offset) \ |
| { \ |
| vdup_b_x(v32, -input_offset); \ |
| vdup_b_x(v33, -input_offset); \ |
| vdup_b_x(v34, -input_offset); \ |
| vdup_b_x(v35, -input_offset); \ |
| vdup_b_x(v36, -input_offset); \ |
| } |
| #define PAD_ROW_2(input_offset) \ |
| { \ |
| vdup_b_x(v37, -input_offset); \ |
| vdup_b_x(v38, -input_offset); \ |
| vdup_b_x(v39, -input_offset); \ |
| vdup_b_x(v40, -input_offset); \ |
| vdup_b_x(v41, -input_offset); \ |
| } |
| #define PAD_ROW_3(input_offset) \ |
| { \ |
| vdup_b_x(v42, -input_offset); \ |
| vdup_b_x(v43, -input_offset); \ |
| vdup_b_x(v44, -input_offset); \ |
| vdup_b_x(v45, -input_offset); \ |
| vdup_b_x(v46, -input_offset); \ |
| } |
| #define PAD_ROW_4(input_offset) \ |
| { \ |
| vdup_b_x(v47, -input_offset); \ |
| vdup_b_x(v48, -input_offset); \ |
| vdup_b_x(v49, -input_offset); \ |
| vdup_b_x(v50, -input_offset); \ |
| vdup_b_x(v51, -input_offset); \ |
| } |
| |
| #define LOAD_ROW_0(p_input, input_width, in_y, in_x) \ |
| { \ |
| const int8_t* p_row = p_input + (in_y[0] * input_width); \ |
| vdup_b_x(v27, *(p_row + in_x[0])); \ |
| vdup_b_x(v28, *(p_row + in_x[1])); \ |
| vdup_b_x(v29, *(p_row + in_x[2])); \ |
| vdup_b_x(v30, *(p_row + in_x[3])); \ |
| vdup_b_x(v31, *(p_row + in_x[4])); \ |
| } |
| |
| #define LOAD_ROW_1(p_input, input_width, in_y, in_x) \ |
| { \ |
| const int8_t* p_row = p_input + (in_y[1] * input_width); \ |
| vdup_b_x(v32, *(p_row + in_x[0])); \ |
| vdup_b_x(v33, *(p_row + in_x[1])); \ |
| vdup_b_x(v34, *(p_row + in_x[2])); \ |
| vdup_b_x(v35, *(p_row + in_x[3])); \ |
| vdup_b_x(v36, *(p_row + in_x[4])); \ |
| } |
| |
| #define LOAD_ROW_2(p_input, input_width, in_y, in_x) \ |
| { \ |
| const int8_t* p_row = p_input + (in_y[2] * input_width); \ |
| vdup_b_x(v37, *(p_row + in_x[0])); \ |
| vdup_b_x(v38, *(p_row + in_x[1])); \ |
| vdup_b_x(v39, *(p_row + in_x[2])); \ |
| vdup_b_x(v40, *(p_row + in_x[3])); \ |
| vdup_b_x(v41, *(p_row + in_x[4])); \ |
| } |
| |
| #define LOAD_ROW_3(p_input, input_width, in_y, in_x) \ |
| { \ |
| const int8_t* p_row = p_input + (in_y[3] * input_width); \ |
| vdup_b_x(v42, *(p_row + in_x[0])); \ |
| vdup_b_x(v43, *(p_row + in_x[1])); \ |
| vdup_b_x(v44, *(p_row + in_x[2])); \ |
| vdup_b_x(v45, *(p_row + in_x[3])); \ |
| vdup_b_x(v46, *(p_row + in_x[4])); \ |
| } |
| |
| #define LOAD_ROW_4(p_input, input_width, in_y, in_x) \ |
| { \ |
| const int8_t* p_row = p_input + (in_y[4] * input_width); \ |
| vdup_b_x(v47, *(p_row + in_x[0])); \ |
| vdup_b_x(v48, *(p_row + in_x[1])); \ |
| vdup_b_x(v49, *(p_row + in_x[2])); \ |
| vdup_b_x(v50, *(p_row + in_x[3])); \ |
| vdup_b_x(v51, *(p_row + in_x[4])); \ |
| } |
| |
| #define H_PAD_OR_LOAD_ROW_0(p_input, input_width, input_offset, in_y, in_x) \ |
| if (in_x[0] >= 0 && in_x[4] < input_width) { \ |
| LOAD_ROW_0(p_input, input_width, in_y, in_x); \ |
| } else { \ |
| const int8_t* p_row = p_input + (in_y[0] * input_width); \ |
| if (in_x[0] < 0 || in_x[0] >= input_width) { \ |
| vdup_b_x(v27, -input_offset); \ |
| } else { \ |
| vdup_b_x(v27, *(p_row + in_x[0])); \ |
| } \ |
| if (in_x[1] < 0 || in_x[1] >= input_width) { \ |
| vdup_b_x(v28, -input_offset); \ |
| } else { \ |
| vdup_b_x(v28, *(p_row + in_x[1])); \ |
| } \ |
| if (in_x[2] < 0 || in_x[2] >= input_width) { \ |
| vdup_b_x(v29, -input_offset); \ |
| } else { \ |
| vdup_b_x(v29, *(p_row + in_x[2])); \ |
| } \ |
| if (in_x[3] < 0 || in_x[3] >= input_width) { \ |
| vdup_b_x(v30, -input_offset); \ |
| } else { \ |
| vdup_b_x(v30, *(p_row + in_x[3])); \ |
| } \ |
| if (in_x[4] < 0 || in_x[4] >= input_width) { \ |
| vdup_b_x(v31, -input_offset); \ |
| } else { \ |
| vdup_b_x(v31, *(p_row + in_x[4])); \ |
| } \ |
| } |
| |
| #define H_PAD_OR_LOAD_ROW_1(p_input, input_width, input_offset, in_y, in_x) \ |
| if (in_x[0] >= 0 && in_x[4] < input_width) { \ |
| LOAD_ROW_1(p_input, input_width, in_y, in_x); \ |
| } else { \ |
| const int8_t* p_row = p_input + (in_y[1] * input_width); \ |
| if (in_x[0] < 0 || in_x[0] >= input_width) { \ |
| vdup_b_x(v32, -input_offset); \ |
| } else { \ |
| vdup_b_x(v32, *(p_row + in_x[0])); \ |
| } \ |
| if (in_x[1] < 0 || in_x[1] >= input_width) { \ |
| vdup_b_x(v33, -input_offset); \ |
| } else { \ |
| vdup_b_x(v33, *(p_row + in_x[1])); \ |
| } \ |
| if (in_x[2] < 0 || in_x[2] >= input_width) { \ |
| vdup_b_x(v34, -input_offset); \ |
| } else { \ |
| vdup_b_x(v34, *(p_row + in_x[2])); \ |
| } \ |
| if (in_x[3] < 0 || in_x[3] >= input_width) { \ |
| vdup_b_x(v35, -input_offset); \ |
| } else { \ |
| vdup_b_x(v35, *(p_row + in_x[3])); \ |
| } \ |
| if (in_x[4] < 0 || in_x[4] >= input_width) { \ |
| vdup_b_x(v36, -input_offset); \ |
| } else { \ |
| vdup_b_x(v36, *(p_row + in_x[4])); \ |
| } \ |
| } |
| |
| #define H_PAD_OR_LOAD_ROW_2(p_input, input_width, input_offset, in_y, in_x) \ |
| if (in_x[0] >= 0 && in_x[4] < input_width) { \ |
| LOAD_ROW_2(p_input, input_width, in_y, in_x); \ |
| } else { \ |
| const int8_t* p_row = p_input + (in_y[2] * input_width); \ |
| if (in_x[0] < 0 || in_x[0] >= input_width) { \ |
| vdup_b_x(v37, -input_offset); \ |
| } else { \ |
| vdup_b_x(v37, *(p_row + in_x[0])); \ |
| } \ |
| if (in_x[1] < 0 || in_x[1] >= input_width) { \ |
| vdup_b_x(v38, -input_offset); \ |
| } else { \ |
| vdup_b_x(v38, *(p_row + in_x[1])); \ |
| } \ |
| if (in_x[2] < 0 || in_x[2] >= input_width) { \ |
| vdup_b_x(v39, -input_offset); \ |
| } else { \ |
| vdup_b_x(v39, *(p_row + in_x[2])); \ |
| } \ |
| if (in_x[3] < 0 || in_x[3] >= input_width) { \ |
| vdup_b_x(v40, -input_offset); \ |
| } else { \ |
| vdup_b_x(v40, *(p_row + in_x[3])); \ |
| } \ |
| if (in_x[4] < 0 || in_x[4] >= input_width) { \ |
| vdup_b_x(v41, -input_offset); \ |
| } else { \ |
| vdup_b_x(v41, *(p_row + in_x[4])); \ |
| } \ |
| } |
| |
| #define H_PAD_OR_LOAD_ROW_3(p_input, input_width, input_offset, in_y, in_x) \ |
| if (in_x[0] >= 0 && in_x[4] < input_width) { \ |
| LOAD_ROW_3(p_input, input_width, in_y, in_x); \ |
| } else { \ |
| const int8_t* p_row = p_input + (in_y[3] * input_width); \ |
| if (in_x[0] < 0 || in_x[0] >= input_width) { \ |
| vdup_b_x(v42, -input_offset); \ |
| } else { \ |
| vdup_b_x(v42, *(p_row + in_x[0])); \ |
| } \ |
| if (in_x[1] < 0 || in_x[1] >= input_width) { \ |
| vdup_b_x(v43, -input_offset); \ |
| } else { \ |
| vdup_b_x(v43, *(p_row + in_x[1])); \ |
| } \ |
| if (in_x[2] < 0 || in_x[2] >= input_width) { \ |
| vdup_b_x(v44, -input_offset); \ |
| } else { \ |
| vdup_b_x(v44, *(p_row + in_x[2])); \ |
| } \ |
| if (in_x[3] < 0 || in_x[3] >= input_width) { \ |
| vdup_b_x(v45, -input_offset); \ |
| } else { \ |
| vdup_b_x(v45, *(p_row + in_x[3])); \ |
| } \ |
| if (in_x[4] < 0 || in_x[4] >= input_width) { \ |
| vdup_b_x(v46, -input_offset); \ |
| } else { \ |
| vdup_b_x(v46, *(p_row + in_x[4])); \ |
| } \ |
| } |
| |
| #define H_PAD_OR_LOAD_ROW_4(p_input, input_width, input_offset, in_y, in_x) \ |
| if (in_x[0] >= 0 && in_x[4] < input_width) { \ |
| LOAD_ROW_4(p_input, input_width, in_y, in_x); \ |
| } else { \ |
| const int8_t* p_row = p_input + (in_y[4] * input_width); \ |
| if (in_x[0] < 0 || in_x[0] >= input_width) { \ |
| vdup_b_x(v47, -input_offset); \ |
| } else { \ |
| vdup_b_x(v47, *(p_row + in_x[0])); \ |
| } \ |
| if (in_x[1] < 0 || in_x[1] >= input_width) { \ |
| vdup_b_x(v48, -input_offset); \ |
| } else { \ |
| vdup_b_x(v48, *(p_row + in_x[1])); \ |
| } \ |
| if (in_x[2] < 0 || in_x[2] >= input_width) { \ |
| vdup_b_x(v49, -input_offset); \ |
| } else { \ |
| vdup_b_x(v49, *(p_row + in_x[2])); \ |
| } \ |
| if (in_x[3] < 0 || in_x[3] >= input_width) { \ |
| vdup_b_x(v50, -input_offset); \ |
| } else { \ |
| vdup_b_x(v50, *(p_row + in_x[3])); \ |
| } \ |
| if (in_x[4] < 0 || in_x[4] >= input_width) { \ |
| vdup_b_x(v51, -input_offset); \ |
| } else { \ |
| vdup_b_x(v51, *(p_row + in_x[4])); \ |
| } \ |
| } |
| |
| #define _H_PAD_OR_LOAD_ROW(row, p_input, input_width, input_offset, in_y, \ |
| in_x) \ |
| H_PAD_OR_LOAD_ROW_##row(p_input, input_width, input_offset, in_y, in_x); |
| |
| #define _PAD_OR_LOAD_ROW(row, p_input, input_height, input_width, in_y, in_x, \ |
| input_offset) \ |
| { \ |
| if (in_y[row] < 0 || in_y[row] >= input_height) { \ |
| PAD_ROW_##row(input_offset); \ |
| } else { \ |
| _H_PAD_OR_LOAD_ROW(row, p_input, input_width, input_offset, in_y, in_x); \ |
| } \ |
| } |
| |
| #define PAD_OR_LOAD_ROW_0(p_input, input_height, input_width, in_y, in_x, \ |
| input_offset) \ |
| _PAD_OR_LOAD_ROW(0, p_input, input_height, input_width, in_y, in_x, \ |
| input_offset); |
| #define PAD_OR_LOAD_ROW_1(p_input, input_height, input_width, in_y, in_x, \ |
| input_offset) \ |
| _PAD_OR_LOAD_ROW(1, p_input, input_height, input_width, in_y, in_x, \ |
| input_offset); |
| #define PAD_OR_LOAD_ROW_2(p_input, input_height, input_width, in_y, in_x, \ |
| input_offset) \ |
| _PAD_OR_LOAD_ROW(2, p_input, input_height, input_width, in_y, in_x, \ |
| input_offset); |
| #define PAD_OR_LOAD_ROW_3(p_input, input_height, input_width, in_y, in_x, \ |
| input_offset) \ |
| _PAD_OR_LOAD_ROW(3, p_input, input_height, input_width, in_y, in_x, \ |
| input_offset); |
| #define PAD_OR_LOAD_ROW_4(p_input, input_height, input_width, in_y, in_x, \ |
| input_offset) \ |
| _PAD_OR_LOAD_ROW(4, p_input, input_height, input_width, in_y, in_x, \ |
| input_offset); |
| |
| #define COMPUTE(cmds, swizzled_bias_data) \ |
| { \ |
| vld_w_x_m(v60, swizzled_bias_data); \ |
| adwinit_v(v60, v60); \ |
| adwconv_vxv(v60, v27, cmds, v0); \ |
| adwconv_vxv(v60, v30, cmds, v3); \ |
| adwconv_vxv(v60, v33, cmds, v6); \ |
| adwconv_vxv(v60, v36, cmds, v9); \ |
| adwconv_vxv(v60, v39, cmds, v12); \ |
| adwconv_vxv(v60, v42, cmds, v15); \ |
| adwconv_vxv(v60, v45, cmds, v18); \ |
| adwconv_vxv(v60, v48, cmds, v21); \ |
| vdwconv_vxv(v60, v51, cmds, v24); \ |
| } |
| |
| #define OUTPUT(output_activation_min, output_activation_max, output_offset, \ |
| local_output_data, n_channels) \ |
| { \ |
| INT32_TO_INT8_OUTPUT_PIPELINE_INPLACE( \ |
| v60, v52, v56, output_activation_min, output_activation_max, \ |
| output_offset); \ |
| vsraqs_b_vx(v60, v60, 0); \ |
| vst_b_l_xx(v60, local_output_data, n_channels); \ |
| } |
| |
| // Estimated count of arithmetic ops: 58.297 M ops, equivalently 29.148 M MACs |
| void ConvPerChannelD1OD24_5x5( |
| 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 = tflite::MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_depth = input_shape.Dims(3); |
| const int output_depth = |
| tflite::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); |
| |
| // Scratch pads to juggle data |
| const size_t swizzled_filter_data_size = 24 * filter_height * filter_width; |
| std::unique_ptr<int8_t> swizzled_filter_data(reinterpret_cast<int8_t*>( |
| ::aligned_alloc(32, swizzled_filter_data_size))); |
| int32_t swizzled_bias_data[32]; |
| int32_t swizzled_output_multiplier[32]; |
| int32_t swizzled_output_shift[32]; |
| // Transpose filter for easy loading |
| for (int filter_y = 0; filter_y < filter_height; ++filter_y) { |
| for (int filter_x = 0; filter_x < filter_width; ++filter_x) { |
| for (int i = 0; i < 24; i++) { |
| int filter_location = |
| (filter_y * filter_width * 24) + (filter_x * 24) + i; |
| swizzled_filter_data.get()[filter_location] = |
| filter_data[tflite::Offset(filter_shape, i, filter_y, filter_x, 0)]; |
| } |
| } |
| } |
| const int8_t* p_flt_0 = swizzled_filter_data.get() + (0 * filter_width * 24); |
| const int8_t* p_flt_1 = swizzled_filter_data.get() + (1 * filter_width * 24); |
| const int8_t* p_flt_2 = swizzled_filter_data.get() + (2 * filter_width * 24); |
| const int8_t* p_flt_3 = swizzled_filter_data.get() + (3 * filter_width * 24); |
| const int8_t* p_flt_4 = swizzled_filter_data.get() + (4 * filter_width * 24); |
| vld_b_l_xx(v0, p_flt_0 + (0 * 24), 24); |
| vld_b_l_xx(v1, p_flt_0 + (1 * 24), 24); |
| vld_b_l_xx(v2, p_flt_0 + (2 * 24), 24); |
| vld_b_l_xx(v3, p_flt_0 + (3 * 24), 24); |
| vld_b_l_xx(v4, p_flt_0 + (4 * 24), 24); |
| |
| vld_b_l_xx(v5, p_flt_1 + (0 * 24), 24); |
| vld_b_l_xx(v6, p_flt_1 + (1 * 24), 24); |
| vld_b_l_xx(v7, p_flt_1 + (2 * 24), 24); |
| vld_b_l_xx(v8, p_flt_1 + (3 * 24), 24); |
| vld_b_l_xx(v9, p_flt_1 + (4 * 24), 24); |
| |
| vld_b_l_xx(v10, p_flt_2 + (0 * 24), 24); |
| vld_b_l_xx(v11, p_flt_2 + (1 * 24), 24); |
| vld_b_l_xx(v12, p_flt_2 + (2 * 24), 24); |
| vld_b_l_xx(v13, p_flt_2 + (3 * 24), 24); |
| vld_b_l_xx(v14, p_flt_2 + (4 * 24), 24); |
| |
| vld_b_l_xx(v15, p_flt_3 + (0 * 24), 24); |
| vld_b_l_xx(v16, p_flt_3 + (1 * 24), 24); |
| vld_b_l_xx(v17, p_flt_3 + (2 * 24), 24); |
| vld_b_l_xx(v18, p_flt_3 + (3 * 24), 24); |
| vld_b_l_xx(v19, p_flt_3 + (4 * 24), 24); |
| |
| vld_b_l_xx(v20, p_flt_4 + (0 * 24), 24); |
| vld_b_l_xx(v21, p_flt_4 + (1 * 24), 24); |
| vld_b_l_xx(v22, p_flt_4 + (2 * 24), 24); |
| vld_b_l_xx(v23, p_flt_4 + (3 * 24), 24); |
| vld_b_l_xx(v24, p_flt_4 + (4 * 24), 24); |
| vdup_b_x(v25, 0); |
| vdup_b_x(v26, 0); |
| |
| union { |
| vdwconv_u8_t dwconv; |
| uint32_t raw; |
| } cmds; |
| cmds.raw = 0; |
| cmds.dwconv.sdata1 = true; |
| cmds.dwconv.sbias1 = input_offset; |
| cmds.dwconv.sdata2 = true; |
| cmds.dwconv.sbias2 = 0; |
| cmds.dwconv.mode = 0; |
| cmds.dwconv.sparsity = 0; |
| cmds.dwconv.regbase = 0; |
| int out_channel = 0; |
| int n_channels = 24; |
| |
| memset(swizzled_bias_data, 0, 32 * sizeof(uint32_t)); |
| JumptableSwizzle(bias_data + out_channel, swizzled_bias_data, n_channels); |
| memset(swizzled_output_multiplier, 0, 32 * sizeof(uint32_t)); |
| JumptableSwizzle(output_multiplier + out_channel, swizzled_output_multiplier, |
| n_channels); |
| JumptableSwizzle(output_shift + out_channel, swizzled_output_shift, |
| n_channels); |
| vld_w_x_m(v52, swizzled_output_multiplier); |
| vld_w_x_m(v56, swizzled_output_shift); |
| vrsub_w_vx_m(v56, v56, 0); |
| |
| int8_t* local_output_data = output_data + out_channel; |
| int in_y[5]; |
| int in_x[5]; |
| int out_y = 0; |
| const int8_t* p_input = input_data; |
| // Handle top row padding |
| for (; out_y < pad_height; ++out_y) { |
| int out_x = 0; |
| const int in_y_origin = (out_y * stride_height) - pad_height; |
| CALCULATE_IN_Y(in_y_origin); |
| // Left padding required |
| for (; out_x < pad_width; ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| CALCULATE_IN_X(in_x_origin); |
| PAD_OR_LOAD_ROW_0(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_1(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_2(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_3(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_4(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| COMPUTE(cmds, swizzled_bias_data); |
| OUTPUT(output_activation_min, output_activation_max, output_offset, |
| local_output_data, n_channels); |
| local_output_data += output_depth; |
| } |
| // No side padding |
| for (; out_x < (output_width - pad_width); ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| CALCULATE_IN_X(in_x_origin); |
| PAD_OR_LOAD_ROW_0(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_1(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_2(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_3(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_4(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| COMPUTE(cmds, swizzled_bias_data); |
| OUTPUT(output_activation_min, output_activation_max, output_offset, |
| local_output_data, n_channels); |
| local_output_data += output_depth; |
| } |
| // Right padding required |
| for (; out_x < output_width; ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| CALCULATE_IN_X(in_x_origin); |
| PAD_OR_LOAD_ROW_0(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_1(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_2(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_3(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_4(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| COMPUTE(cmds, swizzled_bias_data); |
| OUTPUT(output_activation_min, output_activation_max, output_offset, |
| local_output_data, n_channels); |
| local_output_data += output_depth; |
| } |
| } |
| |
| // No height padding |
| for (; out_y < (output_height - pad_height); ++out_y) { |
| const int in_y_origin = (out_y * stride_height) - pad_height; |
| CALCULATE_IN_Y(in_y_origin); |
| // Left padding |
| int out_x = 0; |
| for (; out_x < pad_width; ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| CALCULATE_IN_X(in_x_origin); |
| PAD_OR_LOAD_ROW_0(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_1(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_2(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_3(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_4(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| COMPUTE(cmds, swizzled_bias_data); |
| OUTPUT(output_activation_min, output_activation_max, output_offset, |
| local_output_data, n_channels); |
| local_output_data += output_depth; |
| } |
| for (; out_x < (output_width - pad_width); ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| |
| CALCULATE_IN_X(in_x_origin); |
| LOAD_ROW_0(p_input, input_width, in_y, in_x); |
| LOAD_ROW_1(p_input, input_width, in_y, in_x); |
| LOAD_ROW_2(p_input, input_width, in_y, in_x); |
| LOAD_ROW_3(p_input, input_width, in_y, in_x); |
| LOAD_ROW_4(p_input, input_width, in_y, in_x); |
| COMPUTE(cmds, swizzled_bias_data); |
| OUTPUT(output_activation_min, output_activation_max, output_offset, |
| local_output_data, n_channels); |
| local_output_data += output_depth; |
| } |
| // Right padding |
| for (; out_x < output_width; ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| CALCULATE_IN_X(in_x_origin); |
| PAD_OR_LOAD_ROW_0(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_1(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_2(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_3(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_4(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| COMPUTE(cmds, swizzled_bias_data); |
| OUTPUT(output_activation_min, output_activation_max, output_offset, |
| local_output_data, n_channels); |
| local_output_data += output_depth; |
| } |
| } |
| |
| // Handle bottom row padding |
| for (; out_y < output_height; ++out_y) { |
| const int in_y_origin = (out_y * stride_height) - pad_height; |
| CALCULATE_IN_Y(in_y_origin); |
| int out_x = 0; |
| for (; out_x < pad_width; ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| CALCULATE_IN_X(in_x_origin); |
| PAD_OR_LOAD_ROW_0(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_1(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_2(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_3(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_4(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| COMPUTE(cmds, swizzled_bias_data); |
| OUTPUT(output_activation_min, output_activation_max, output_offset, |
| local_output_data, n_channels); |
| local_output_data += output_depth; |
| } |
| for (; out_x < (output_width - pad_width); ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| CALCULATE_IN_X(in_x_origin); |
| PAD_OR_LOAD_ROW_0(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_1(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_2(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_3(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_4(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| COMPUTE(cmds, swizzled_bias_data); |
| OUTPUT(output_activation_min, output_activation_max, output_offset, |
| local_output_data, n_channels); |
| local_output_data += output_depth; |
| } |
| for (; out_x < output_width; ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| CALCULATE_IN_X(in_x_origin); |
| PAD_OR_LOAD_ROW_0(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_1(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_2(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_3(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| PAD_OR_LOAD_ROW_4(p_input, input_height, input_width, in_y, in_x, |
| input_offset); |
| COMPUTE(cmds, swizzled_bias_data); |
| OUTPUT(output_activation_min, output_activation_max, output_offset, |
| local_output_data, n_channels); |
| local_output_data += output_depth; |
| } |
| } |
| } |
| |
| #undef PAD_OR_LOAD_ROW_0 |
| #undef PAD_OR_LOAD_ROW_1 |
| #undef PAD_OR_LOAD_ROW_2 |
| #undef PAD_OR_LOAD_ROW_3 |
| #undef PAD_OR_LOAD_ROW_4 |
| #undef _PAD_OR_LOAD_ROW |
| #undef _H_PAD_OR_LOAD_ROW |
| #undef H_PAD_OR_LOAD_ROW_0 |
| #undef H_PAD_OR_LOAD_ROW_1 |
| #undef H_PAD_OR_LOAD_ROW_2 |
| #undef H_PAD_OR_LOAD_ROW_3 |
| #undef H_PAD_OR_LOAD_ROW_4 |
| #undef LOAD_ROW_0 |
| #undef LOAD_ROW_1 |
| #undef LOAD_ROW_2 |
| #undef LOAD_ROW_3 |
| #undef LOAD_ROW_4 |
| #undef PAD_ROW_0 |
| #undef PAD_ROW_1 |
| #undef PAD_ROW_2 |
| #undef PAD_ROW_3 |
| #undef PAD_ROW_4 |
| #undef CALCULATE_IN_X |
| #undef CALCULATE_IN_Y |
| |
| void ConvPerChannelD1( |
| 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 = tflite::MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_depth = input_shape.Dims(3); |
| const int output_depth = tflite::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); |
| |
| // Scratch pads to juggle data |
| const size_t swizzled_filter_data_size = 32 * filter_height * filter_width; |
| std::unique_ptr<int8_t> swizzled_filter_data( |
| reinterpret_cast<int8_t*>( |
| ::aligned_alloc(32, swizzled_filter_data_size))); |
| int32_t swizzled_bias_data[32]; |
| int32_t swizzled_output_multiplier[32]; |
| int32_t swizzled_output_shift[32]; |
| |
| for (int out_channel = 0; out_channel < output_depth; out_channel += 32) { |
| int n_channels = std::min(32, output_depth - out_channel); |
| |
| // Transpose filter for easy loading |
| for (int filter_y = 0; filter_y < filter_height; ++filter_y) { |
| for (int filter_x = 0; filter_x < filter_width; ++filter_x) { |
| for (int i = 0; i < n_channels; i++) { |
| int filter_location = |
| (filter_y * filter_width * 32) + (filter_x * 32) + i; |
| swizzled_filter_data.get()[filter_location] = filter_data[ |
| tflite::Offset(filter_shape, out_channel + i, filter_y, filter_x, |
| 0)]; |
| } |
| } |
| } |
| |
| if (bias_data) { |
| JumptableSwizzle(bias_data + out_channel, swizzled_bias_data, n_channels); |
| vld_w_x_m(v52, swizzled_bias_data); |
| } else { |
| vdup_w_x_m(v52, 0); |
| } |
| |
| JumptableSwizzle(output_multiplier + out_channel, |
| swizzled_output_multiplier, n_channels); |
| vld_w_x_m(v56, swizzled_output_multiplier); |
| |
| JumptableSwizzle(output_shift + out_channel, swizzled_output_shift, |
| n_channels); |
| vld_w_x_m(v60, swizzled_output_shift); |
| vrsub_w_vx_m(v60, v60, 0); |
| |
| int8_t* local_output_data = output_data + out_channel; |
| |
| 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 < output_width; ++out_x) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| |
| // Accumulator loop |
| vmv_v_m(v48, v52); |
| for (int filter_y = 0; filter_y < filter_height; ++filter_y) { |
| const int in_y = in_y_origin + dilation_height_factor * filter_y; |
| if ((in_y < 0) || (in_y >= input_height)) { |
| continue; |
| } |
| |
| const int8_t* local_input_data = input_data + |
| tflite::Offset(input_shape, batch, in_y, 0, 0); |
| int filter_x = 0; |
| int in_x = in_x_origin; |
| const int8_t* local_filter_data = swizzled_filter_data.get() + |
| (filter_y * filter_width * 32); |
| while (in_x < 0) { |
| filter_x++; |
| in_x += dilation_width_factor; |
| local_filter_data += 32; |
| } |
| for (; (filter_x < filter_width) && (in_x < input_width); |
| ++filter_x, in_x += dilation_width_factor, |
| local_filter_data += 32) { |
| |
| int16_t input_val = local_input_data[in_x]; |
| int16_t input_val16 = static_cast<int16_t>( |
| input_val + input_offset); |
| vdup_h_x(v32, input_val16); |
| |
| vld_b_l_xx(v0, local_filter_data, n_channels); |
| vaddw_h_vx(v0, v0, 0); |
| |
| // Multiply |
| vmulw_w_vv(v4, v0, v32); |
| vmulw_w_vv(v6, v1, v32); |
| |
| // Accumulate |
| vadd_w_vv_m(v48, v48, v4); |
| } |
| } |
| |
| // Output pipeline |
| INT32_TO_INT8_OUTPUT_PIPELINE_INPLACE( |
| v48, v56, v60, output_activation_min, output_activation_max, |
| output_offset); |
| vsraqs_b_vx(v48, v48, 0); |
| vst_b_l_xx(v48, local_output_data, n_channels); |
| local_output_data += output_depth; |
| } |
| } |
| } |
| } |
| } |
| |
| } // namespace kelvin::opt |
