| /* |
| * Copyright (c) 2018, Alliance for Open Media. All rights reserved |
| * |
| * This source code is subject to the terms of the BSD 2 Clause License and |
| * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License |
| * was not distributed with this source code in the LICENSE file, you can |
| * obtain it at www.aomedia.org/license/software. If the Alliance for Open |
| * Media Patent License 1.0 was not distributed with this source code in the |
| * PATENTS file, you can obtain it at www.aomedia.org/license/patent. |
| */ |
| |
| #include <stdbool.h> |
| #include <assert.h> |
| #include <pmmintrin.h> |
| |
| #include "config/av1_rtcd.h" |
| #include "av1/encoder/ml.h" |
| |
| // In order to avoid the high-latency of swapping between FPU and SIMD |
| // operations, we keep the result in a 128-bit register even though we only |
| // care about a single value. |
| static void nn_propagate_8to1(const float *const inputs, |
| const float *const weights, |
| __m128 *const output) { |
| const __m128 inputs_h = _mm_loadu_ps(&inputs[4]); |
| const __m128 inputs_l = _mm_loadu_ps(inputs); |
| |
| const __m128 weights_h = _mm_loadu_ps(&weights[4]); |
| const __m128 weights_l = _mm_loadu_ps(weights); |
| |
| const __m128 mul_h = _mm_mul_ps(inputs_h, weights_h); |
| const __m128 mul_l = _mm_mul_ps(inputs_l, weights_l); |
| // [7 6 5 4] [3 2 1 0] (weight and input indices) |
| |
| const __m128 vadd = _mm_add_ps(mul_l, mul_h); |
| // [7+3 6+2 5+1 4+0] |
| const __m128 hadd1 = _mm_hadd_ps(vadd, vadd); |
| // [7+6+3+2 5+4+1+0 7+6+3+2 5+4+1+0] |
| const __m128 hadd2 = _mm_hadd_ps(hadd1, hadd1); |
| // [7+6+5+4+3+2+1+0 7+6+5+4+3+2+1+0 7+6+5+4+3+2+1+0 7+6+5+4+3+2+1+0] |
| *output = _mm_add_ps(*output, hadd2); |
| } |
| |
| static void nn_propagate_4to1(const float *const inputs, |
| const float *const weights, |
| __m128 *const output) { |
| const __m128 inputs128 = _mm_loadu_ps(inputs); |
| |
| const __m128 weights128 = _mm_loadu_ps(weights); |
| |
| const __m128 mul = _mm_mul_ps(inputs128, weights128); |
| // [3 2 1 0] (weight and input indices) |
| |
| const __m128 hadd1 = _mm_hadd_ps(mul, mul); |
| // [3+2 1+0 3+2 1+0] |
| const __m128 hadd2 = _mm_hadd_ps(hadd1, hadd1); |
| // [3+2+1+0 3+2+1+0 3+2+1+0 3+2+1+0] |
| *output = _mm_add_ps(*output, hadd2); |
| } |
| |
| static void nn_propagate_4to4(const float *const inputs, |
| const float *const weights, __m128 *const outputs, |
| const int num_inputs) { |
| const __m128 inputs128 = _mm_loadu_ps(inputs); |
| |
| __m128 hadd[2]; |
| for (int i = 0; i < 2; i++) { // For each pair of outputs |
| const __m128 weight0 = _mm_loadu_ps(&weights[2 * i * num_inputs]); |
| const __m128 mul0 = _mm_mul_ps(weight0, inputs128); |
| const __m128 weight1 = _mm_loadu_ps(&weights[(2 * i + 1) * num_inputs]); |
| const __m128 mul1 = _mm_mul_ps(weight1, inputs128); |
| hadd[i] = _mm_hadd_ps(mul0, mul1); |
| } |
| // hadd[0] = [7+6 5+4 3+2 1+0] (weight indices) |
| // hadd[1] = [15+14 13+12 11+10 9+8] |
| |
| const __m128 hh = _mm_hadd_ps(hadd[0], hadd[1]); |
| // [15+14+13+12 11+10+9+8 7+6+5+4 3+2+1+0] |
| |
| *outputs = _mm_add_ps(*outputs, hh); |
| } |
| |
| static void nn_propagate_4to8(const float *const inputs, |
| const float *const weights, __m128 *const out_h, |
| __m128 *const out_l, const int num_inputs) { |
| const __m128 inputs128 = _mm_loadu_ps(inputs); |
| |
| __m128 hadd[4]; |
| for (int i = 0; i < 4; i++) { // For each pair of outputs |
| const __m128 weight0 = _mm_loadu_ps(&weights[2 * i * num_inputs]); |
| const __m128 weight1 = _mm_loadu_ps(&weights[(2 * i + 1) * num_inputs]); |
| const __m128 mul0 = _mm_mul_ps(inputs128, weight0); |
| const __m128 mul1 = _mm_mul_ps(inputs128, weight1); |
| hadd[i] = _mm_hadd_ps(mul0, mul1); |
| } |
| // hadd[0] = [7+6 5+4 3+2 1+0] (weight indices) |
| // hadd[1] = [15+14 13+12 11+10 9+8] |
| // hadd[2] = [23+22 21+20 19+18 17+16] |
| // hadd[3] = [31+30 29+28 27+26 25+24] |
| |
| const __m128 hh0 = _mm_hadd_ps(hadd[0], hadd[1]); |
| // [15+14+13+12 11+10+9+8 7+6+5+4 3+2+1+0] |
| const __m128 hh1 = _mm_hadd_ps(hadd[2], hadd[3]); |
| // [31+30+29+28 27+26+25+24 23+22+21+20 19+18+17+16] |
| |
| *out_h = _mm_add_ps(*out_h, hh1); |
| *out_l = _mm_add_ps(*out_l, hh0); |
| } |
| |
| static void nn_propagate_8to4(const float *const inputs, |
| const float *const weights, __m128 *const outputs, |
| const int num_inputs) { |
| const __m128 inputs_h = _mm_loadu_ps(inputs + 4); |
| const __m128 inputs_l = _mm_loadu_ps(inputs); |
| // [7 6 5 4] [3 2 1 0] (input indices) |
| |
| __m128 add[4]; |
| for (int i = 0; i < 4; i++) { // For each output: |
| const __m128 weight_h = _mm_loadu_ps(&weights[i * num_inputs + 4]); |
| const __m128 weight_l = _mm_loadu_ps(&weights[i * num_inputs]); |
| const __m128 mul_h = _mm_mul_ps(inputs_h, weight_h); |
| const __m128 mul_l = _mm_mul_ps(inputs_l, weight_l); |
| add[i] = _mm_add_ps(mul_l, mul_h); |
| } |
| // add[0] = [7+3 6+2 5+1 4+0] |
| // add[1] = [15+11 14+10 13+9 12+8] |
| // add[2] = [23+19 22+18 21+17 20+16] |
| // add[3] = [31+27 30+26 29+25 28+24] |
| |
| const __m128 hadd_h = _mm_hadd_ps(add[2], add[3]); |
| // [31+30+27+26 29+28+25+24 23+22+19+18 21+20+17+16] |
| const __m128 hadd_l = _mm_hadd_ps(add[0], add[1]); |
| // [15+14+11+10 13+12+9+8 7+6+3+2 5+4+1+0] |
| |
| const __m128 haddhadd = _mm_hadd_ps(hadd_l, hadd_h); |
| // [31+30+29+28+27+26+25+24 23+22+21+20+19+18+17+16 |
| // 15+14+13+12+11+10+9+8 7+6+5+4+3+2+1+0] |
| |
| *outputs = _mm_add_ps(*outputs, haddhadd); |
| } |
| |
| static void nn_activate8(__m128 *out_h, __m128 *out_l) { |
| const __m128 zero = _mm_setzero_ps(); |
| *out_h = _mm_max_ps(*out_h, zero); |
| *out_l = _mm_max_ps(*out_l, zero); |
| } |
| |
| static void nn_activate4(__m128 *x) { *x = _mm_max_ps(*x, _mm_setzero_ps()); } |
| |
| // Calculate prediction based on the given input features and neural net config. |
| // Assume there are no more than NN_MAX_NODES_PER_LAYER nodes in each hidden |
| // layer. |
| void av1_nn_predict_sse3(const float *input_nodes, |
| const NN_CONFIG *const nn_config, int reduce_prec, |
| float *const output) { |
| float buf[2][NN_MAX_NODES_PER_LAYER]; |
| int buf_index = 0; |
| int num_inputs = nn_config->num_inputs; |
| |
| // Hidden layers, except the final iteration is the output layer. |
| for (int layer = 0; layer <= nn_config->num_hidden_layers; layer++) { |
| const float *layer_weights = nn_config->weights[layer]; |
| const float *layer_bias = nn_config->bias[layer]; |
| bool output_layer = (layer == nn_config->num_hidden_layers); |
| float *const output_nodes = output_layer ? output : &buf[buf_index][0]; |
| const int num_outputs = output_layer ? nn_config->num_outputs |
| : nn_config->num_hidden_nodes[layer]; |
| |
| if (num_inputs % 4 == 0 && num_outputs % 8 == 0) { |
| for (int out = 0; out < num_outputs; out += 8) { |
| __m128 out_h = _mm_loadu_ps(&layer_bias[out + 4]); |
| __m128 out_l = _mm_loadu_ps(&layer_bias[out]); |
| for (int in = 0; in < num_inputs; in += 4) { |
| nn_propagate_4to8(&input_nodes[in], |
| &layer_weights[out * num_inputs + in], &out_h, |
| &out_l, num_inputs); |
| } |
| if (!output_layer) nn_activate8(&out_h, &out_l); |
| _mm_storeu_ps(&output_nodes[out + 4], out_h); |
| _mm_storeu_ps(&output_nodes[out], out_l); |
| } |
| } else if (num_inputs % 8 == 0 && num_outputs % 4 == 0) { |
| for (int out = 0; out < num_outputs; out += 4) { |
| __m128 outputs = _mm_loadu_ps(&layer_bias[out]); |
| for (int in = 0; in < num_inputs; in += 8) { |
| nn_propagate_8to4(&input_nodes[in], |
| &layer_weights[out * num_inputs + in], &outputs, |
| num_inputs); |
| } |
| if (!output_layer) nn_activate4(&outputs); |
| _mm_storeu_ps(&output_nodes[out], outputs); |
| } |
| } else if (num_inputs % 4 == 0 && num_outputs % 4 == 0) { |
| for (int out = 0; out < num_outputs; out += 4) { |
| __m128 outputs = _mm_loadu_ps(&layer_bias[out]); |
| for (int in = 0; in < num_inputs; in += 4) { |
| nn_propagate_4to4(&input_nodes[in], |
| &layer_weights[out * num_inputs + in], &outputs, |
| num_inputs); |
| } |
| if (!output_layer) nn_activate4(&outputs); |
| _mm_storeu_ps(&output_nodes[out], outputs); |
| } |
| } else if (num_inputs % 8 == 0) { |
| for (int out = 0; out < num_outputs; out++) { |
| __m128 total = _mm_load1_ps(&layer_bias[out]); |
| for (int in = 0; in < num_inputs; in += 8) { |
| nn_propagate_8to1(&input_nodes[in], |
| &layer_weights[out * num_inputs + in], &total); |
| } |
| if (!output_layer) nn_activate4(&total); |
| output_nodes[out] = _mm_cvtss_f32(total); |
| } |
| } else if (num_inputs % 4 == 0) { |
| for (int out = 0; out < num_outputs; out++) { |
| __m128 total = _mm_load1_ps(&layer_bias[out]); |
| for (int in = 0; in < num_inputs; in += 4) { |
| nn_propagate_4to1(&input_nodes[in], |
| &layer_weights[out * num_inputs + in], &total); |
| } |
| if (!output_layer) nn_activate4(&total); |
| output_nodes[out] = _mm_cvtss_f32(total); |
| } |
| } else { |
| // Use SSE instructions for scalar operations to avoid the latency of |
| // swapping between SIMD and FPU modes. |
| for (int out = 0; out < num_outputs; out++) { |
| __m128 total = _mm_load1_ps(&layer_bias[out]); |
| for (int in_node = 0; in_node < num_inputs; in_node++) { |
| __m128 input = _mm_load1_ps(&input_nodes[in_node]); |
| __m128 weight = |
| _mm_load1_ps(&layer_weights[num_inputs * out + in_node]); |
| total = _mm_add_ps(total, _mm_mul_ps(input, weight)); |
| } |
| if (!output_layer) nn_activate4(&total); |
| output_nodes[out] = _mm_cvtss_f32(total); |
| } |
| } |
| input_nodes = output_nodes; |
| num_inputs = num_outputs; |
| buf_index = 1 - buf_index; |
| } |
| if (reduce_prec) av1_nn_output_prec_reduce(output, nn_config->num_outputs); |
| } |
| |
| // Based on N. N. Schraudolph. A Fast, Compact Approximation of the Exponential |
| // Function. Neural Computation, 11(4):853–862, 1999. |
| static AOM_INLINE __m128 approx_exp(__m128 y) { |
| #define A ((1 << 23) / 0.69314718056f) // (1 << 23) / ln(2) |
| #define B \ |
| 127 // Offset for the exponent according to IEEE floating point standard. |
| #define C 60801 // Magic number controls the accuracy of approximation |
| const __m128 multiplier = _mm_set1_ps(A); |
| const __m128i offset = _mm_set1_epi32(B * (1 << 23) - C); |
| |
| y = _mm_mul_ps(y, multiplier); |
| y = _mm_castsi128_ps(_mm_add_epi32(_mm_cvtps_epi32(y), offset)); |
| return y; |
| #undef A |
| #undef B |
| #undef C |
| } |
| |
| static AOM_INLINE __m128 reduce_max(__m128 reg) { |
| __m128 tmp_reg; |
| |
| tmp_reg = _mm_shuffle_ps(reg, reg, 0x4e); // 01 00 11 10 |
| reg = _mm_max_ps(reg, tmp_reg); |
| |
| tmp_reg = _mm_shuffle_ps(reg, reg, 0xb1); // 10 11 00 01 |
| reg = _mm_max_ps(reg, tmp_reg); |
| |
| return reg; |
| } |
| |
| static AOM_INLINE __m128 reduce_sum(__m128 reg) { |
| __m128 tmp_reg; |
| |
| tmp_reg = _mm_shuffle_ps(reg, reg, 0x4e); // 01 00 11 10 |
| reg = _mm_add_ps(reg, tmp_reg); |
| |
| tmp_reg = _mm_shuffle_ps(reg, reg, 0xb1); // 10 11 00 01 |
| reg = _mm_add_ps(reg, tmp_reg); |
| |
| return reg; |
| } |
| |
| void av1_nn_fast_softmax_16_sse3(const float *input, float *output) { |
| // Clips at -10 to avoid underflowing |
| const __m128 clipper = _mm_set1_ps(-10.0f); |
| |
| // Load in 16 values |
| __m128 in_0 = _mm_loadu_ps(&input[0]); |
| __m128 in_1 = _mm_loadu_ps(&input[4]); |
| __m128 in_2 = _mm_loadu_ps(&input[8]); |
| __m128 in_3 = _mm_loadu_ps(&input[12]); |
| |
| // Get the max |
| __m128 max_0 = _mm_max_ps(in_0, in_1); |
| __m128 max_1 = _mm_max_ps(in_2, in_3); |
| |
| max_0 = _mm_max_ps(max_0, max_1); |
| max_0 = reduce_max(max_0); |
| |
| // Subtract the max off and clip |
| in_0 = _mm_sub_ps(in_0, max_0); |
| in_1 = _mm_sub_ps(in_1, max_0); |
| in_2 = _mm_sub_ps(in_2, max_0); |
| in_3 = _mm_sub_ps(in_3, max_0); |
| |
| in_0 = _mm_max_ps(in_0, clipper); |
| in_1 = _mm_max_ps(in_1, clipper); |
| in_2 = _mm_max_ps(in_2, clipper); |
| in_3 = _mm_max_ps(in_3, clipper); |
| |
| // Exponentiate and compute the denominator |
| __m128 sum = in_0 = approx_exp(in_0); |
| in_1 = approx_exp(in_1); |
| sum = _mm_add_ps(sum, in_1); |
| in_2 = approx_exp(in_2); |
| sum = _mm_add_ps(sum, in_2); |
| in_3 = approx_exp(in_3); |
| sum = _mm_add_ps(sum, in_3); |
| sum = reduce_sum(sum); |
| |
| // Divide to get the probability |
| in_0 = _mm_div_ps(in_0, sum); |
| in_1 = _mm_div_ps(in_1, sum); |
| in_2 = _mm_div_ps(in_2, sum); |
| in_3 = _mm_div_ps(in_3, sum); |
| |
| _mm_storeu_ps(&output[0], in_0); |
| _mm_storeu_ps(&output[4], in_1); |
| _mm_storeu_ps(&output[8], in_2); |
| _mm_storeu_ps(&output[12], in_3); |
| } |