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