/*
* Copyright (c) 2020, 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 <arm_neon.h>
#include "config/aom_config.h"
#include "config/av1_rtcd.h"
#include "av1/encoder/ml.h"
static void nn_activate8(float32x4_t *out_h, float32x4_t *out_l,
const float32x4_t *zero) {
*out_h = vmaxq_f32(*out_h, *zero);
*out_l = vmaxq_f32(*out_l, *zero);
}
static void nn_activate4(float32x4_t *x,
const float32x4_t *zero) {
*x = vmaxq_f32(*x, *zero);
}
#define CLAMP_0(x) (x = x > 0 ? x : 0)
static void nn_propagate_8to1(
int num_inputs,
const float *
const inputs,
const float *
const weights,
const float *layer_bias,
float *
const output_nodes,
bool output_layer) {
const float32x4_t zero = vdupq_n_f32(0);
float32x4_t vadd = zero;
float total = *layer_bias;
for (
int in = 0; in < num_inputs; in += 8) {
const float32x4_t inputs_h = vld1q_f32(&inputs[in + 4]);
const float32x4_t inputs_l = vld1q_f32(&inputs[in]);
const float32x4_t weights_h = vld1q_f32(&weights[in + 4]);
const float32x4_t weights_l = vld1q_f32(&weights[in]);
vadd = vmlaq_f32(vadd, inputs_h, weights_h);
vadd = vmlaq_f32(vadd, inputs_l, weights_l);
}
#if AOM_ARCH_AARCH64
total += vaddvq_f32(vadd);
#else
float32x2_t vadd_lo = vadd_f32(vget_low_f32(vadd), vget_high_f32(vadd));
vadd_lo = vpadd_f32(vadd_lo, vadd_lo);
total += vget_lane_f32(vadd_lo, 0);
#endif
if (!output_layer) CLAMP_0(total);
*output_nodes = total;
}
static void nn_propagate_xto1(
int num_inputs,
const float *
const inputs,
const float *
const weights,
const float *layer_bias,
float *
const output_nodes) {
float32x4_t vadd = vdupq_n_f32(0);
float total = *layer_bias;
int j = num_inputs;
int in = 0;
while (j > 7) {
const float32x4_t inputs_h = vld1q_f32(&inputs[in + 4]);
const float32x4_t inputs_l = vld1q_f32(&inputs[in]);
const float32x4_t weights_h = vld1q_f32(&weights[in + 4]);
const float32x4_t weights_l = vld1q_f32(&weights[in]);
vadd = vmlaq_f32(vadd, inputs_h, weights_h);
vadd = vmlaq_f32(vadd, inputs_l, weights_l);
in += 8;
j -= 8;
}
#if AOM_ARCH_AARCH64
total += vaddvq_f32(vadd);
#else
float32x2_t vadd_lo = vadd_f32(vget_low_f32(vadd), vget_high_f32(vadd));
vadd_lo = vpadd_f32(vadd_lo, vadd_lo);
total += vget_lane_f32(vadd_lo, 0);
#endif
for (; in < num_inputs; in++) total += weights[in] * inputs[in];
*output_nodes = CLAMP_0(total);
}
static void nn_propagate_xsto1(
int num_inputs,
const float *
const inputs,
const float *
const weights,
const float *layer_bias,
float *
const output_nodes) {
float total = *layer_bias;
#if AOM_ARCH_AARCH64
const float32x4_t v_inputs = vld1q_f32(inputs);
const float32x4_t v_weights = vld1q_f32(weights);
const float32x4_t vadd = vmulq_f32(v_inputs, v_weights);
total += vaddvq_f32(vadd);
int in = 4;
#else
int in = 0;
#endif
for (; in < num_inputs; in++) total += weights[in] * inputs[in];
*output_nodes = CLAMP_0(total);
}
static void nn_propagate_4to1(
int num_inputs,
const float *
const inputs,
const float *
const weights,
const float *layer_bias,
float *
const output_nodes,
bool output_layer) {
const float32x4_t zero = vdupq_n_f32(0);
float32x4_t vadd = zero;
float total = *layer_bias;
for (
int in = 0; in < num_inputs; in += 4) {
const float32x4_t v_inputs = vld1q_f32(&inputs[in]);
const float32x4_t v_weights = vld1q_f32(&weights[in]);
vadd = vmlaq_f32(vadd, v_inputs, v_weights);
}
#if AOM_ARCH_AARCH64
total += vaddvq_f32(vadd);
#else
float32x2_t vadd_lo = vadd_f32(vget_low_f32(vadd), vget_high_f32(vadd));
vadd_lo = vpadd_f32(vadd_lo, vadd_lo);
total += vget_lane_f32(vadd_lo, 0);
#endif
if (!output_layer) CLAMP_0(total);
*output_nodes = total;
}
static void nn_propagate_4to4(
int num_inputs,
const float *
const inputs,
const float *
const weights,
const float *layer_bias,
float *
const output_nodes,
bool output_layer) {
float32x4_t outputs = vld1q_f32(layer_bias);
const float32x4_t zero = vdupq_n_f32(0);
float32x4_t mul0[2] = { zero, zero };
float32x4_t mul1[2] = { zero, zero };
for (
int in = 0; in < num_inputs; in += 4) {
const float32x4_t v_input = vld1q_f32(&inputs[in]);
for (
int i = 0; i < 2; i++) {
const float32x4_t weight0 = vld1q_f32(&weights[in + 2 * i * num_inputs]);
mul0[i] = vmlaq_f32(mul0[i], weight0, v_input);
const float32x4_t weight1 =
vld1q_f32(&weights[in + (2 * i + 1) * num_inputs]);
mul1[i] = vmlaq_f32(mul1[i], weight1, v_input);
}
}
for (
int i = 0; i < 2; i++)
#if AOM_ARCH_AARCH64
mul0[i] = vpaddq_f32(mul0[i], mul1[i]);
const float32x4_t hh = vpaddq_f32(mul0[0], mul0[1]);
#else
mul0[i] =
vcombine_f32(vpadd_f32(vget_low_f32(mul0[i]), vget_high_f32(mul0[i])),
vpadd_f32(vget_low_f32(mul1[i]), vget_high_f32(mul1[i])));
const float32x4_t hh =
vcombine_f32(vpadd_f32(vget_low_f32(mul0[0]), vget_high_f32(mul0[0])),
vpadd_f32(vget_low_f32(mul0[1]), vget_high_f32(mul0[1])));
#endif
outputs = vaddq_f32(outputs, hh);
if (!output_layer) nn_activate4(&outputs, &zero);
vst1q_f32(output_nodes, outputs);
}
static void nn_propagate_4to8(
const int num_inputs,
const float *
const inputs,
const float *
const weights,
const float *layer_bias,
float *
const output_nodes,
bool output_layer) {
float32x4_t out_h = vld1q_f32(&layer_bias[4]);
float32x4_t out_l = vld1q_f32(layer_bias);
const float32x4_t zero = vdupq_n_f32(0);
float32x4_t mul0[4] = { zero, zero, zero, zero };
float32x4_t mul1[4] = { zero, zero, zero, zero };
for (
int in = 0; in < num_inputs; in += 4) {
const float32x4_t v_input = vld1q_f32(&inputs[in]);
for (
int i = 0; i < 4; i++) {
const float32x4_t weight0 = vld1q_f32(&weights[in + 2 * i * num_inputs]);
const float32x4_t weight1 =
vld1q_f32(&weights[in + (2 * i + 1) * num_inputs]);
mul0[i] = vmlaq_f32(mul0[i], v_input, weight0);
mul1[i] = vmlaq_f32(mul1[i], v_input, weight1);
}
}
for (
int i = 0; i < 4; i++)
#if AOM_ARCH_AARCH64
mul0[i] = vpaddq_f32(mul0[i], mul1[i]);
const float32x4_t hh0 = vpaddq_f32(mul0[0], mul0[1]);
const float32x4_t hh1 = vpaddq_f32(mul0[2], mul0[3]);
#else
mul0[i] =
vcombine_f32(vpadd_f32(vget_low_f32(mul0[i]), vget_high_f32(mul0[i])),
vpadd_f32(vget_low_f32(mul1[i]), vget_high_f32(mul1[i])));
const float32x4_t hh0 =
vcombine_f32(vpadd_f32(vget_low_f32(mul0[0]), vget_high_f32(mul0[0])),
vpadd_f32(vget_low_f32(mul0[1]), vget_high_f32(mul0[1])));
const float32x4_t hh1 =
vcombine_f32(vpadd_f32(vget_low_f32(mul0[2]), vget_high_f32(mul0[2])),
vpadd_f32(vget_low_f32(mul0[3]), vget_high_f32(mul0[3])));
#endif
out_h = vaddq_f32(out_h, hh1);
out_l = vaddq_f32(out_l, hh0);
if (!output_layer) nn_activate8(&out_h, &out_l, &zero);
vst1q_f32(&output_nodes[4], out_h);
vst1q_f32(output_nodes, out_l);
}
static void nn_propagate_8to4(
const int num_inputs,
const float *
const inputs,
const float *
const weights,
const float *layer_bias,
float *
const output_nodes,
bool output_layer) {
float32x4_t outputs = vld1q_f32(layer_bias);
const float32x4_t zero = vdupq_n_f32(0);
float32x4_t add[4] = { zero, zero, zero, zero };
for (
int in = 0; in < num_inputs; in += 8) {
const float32x4_t inputs_l = vld1q_f32(&inputs[in]);
const float32x4_t inputs_h = vld1q_f32(&inputs[in + 4]);
for (
int i = 0; i < 4; i++) {
const float32x4_t weight_l = vld1q_f32(&weights[in + i * num_inputs]);
const float32x4_t weight_h = vld1q_f32(&weights[in + i * num_inputs + 4]);
add[i] = vmlaq_f32(add[i], inputs_l, weight_l);
add[i] = vmlaq_f32(add[i], inputs_h, weight_h);
}
}
#if AOM_ARCH_AARCH64
const float32x4_t hadd_h = vpaddq_f32(add[2], add[3]);
const float32x4_t hadd_l = vpaddq_f32(add[0], add[1]);
const float32x4_t haddhadd = vpaddq_f32(hadd_l, hadd_h);
#else
const float32x4_t hadd_h =
vcombine_f32(vpadd_f32(vget_low_f32(add[2]), vget_high_f32(add[2])),
vpadd_f32(vget_low_f32(add[3]), vget_high_f32(add[3])));
const float32x4_t hadd_l =
vcombine_f32(vpadd_f32(vget_low_f32(add[0]), vget_high_f32(add[0])),
vpadd_f32(vget_low_f32(add[1]), vget_high_f32(add[1])));
const float32x4_t haddhadd =
vcombine_f32(vpadd_f32(vget_low_f32(hadd_l), vget_high_f32(hadd_l)),
vpadd_f32(vget_low_f32(hadd_h), vget_high_f32(hadd_h)));
#endif
outputs = vaddq_f32(outputs, haddhadd);
if (!output_layer) nn_activate4(&outputs, &zero);
vst1q_f32(output_nodes, outputs);
}
// 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_neon(
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];
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) {
nn_propagate_4to8(num_inputs, input_nodes,
&layer_weights[out * num_inputs], &layer_bias[out],
&output_nodes[out], output_layer);
}
}
else if (num_inputs % 8 == 0 && num_outputs % 4 == 0) {
for (
int out = 0; out < num_outputs; out += 4) {
nn_propagate_8to4(num_inputs, input_nodes,
&layer_weights[out * num_inputs], &layer_bias[out],
&output_nodes[out], output_layer);
}
}
else if (num_inputs % 4 == 0 && num_outputs % 4 == 0) {
for (
int out = 0; out < num_outputs; out += 4) {
nn_propagate_4to4(num_inputs, input_nodes,
&layer_weights[out * num_inputs], &layer_bias[out],
&output_nodes[out], output_layer);
}
}
else if (num_inputs % 8 == 0) {
for (
int out = 0; out < num_outputs; out++) {
nn_propagate_8to1(num_inputs, input_nodes,
&layer_weights[out * num_inputs], &layer_bias[out],
&output_nodes[out], output_layer);
}
}
else if (num_inputs % 4 == 0) {
for (
int out = 0; out < num_outputs; out++) {
nn_propagate_4to1(num_inputs, input_nodes,
&layer_weights[out * num_inputs], &layer_bias[out],
&output_nodes[out], output_layer);
}
}
else if (num_inputs > 8) {
for (
int out = 0; out < num_outputs; out++) {
nn_propagate_xto1(num_inputs, input_nodes,
&layer_weights[out * num_inputs], &layer_bias[out],
&output_nodes[out]);
}
}
else if (num_inputs >= 4) {
for (
int out = 0; out < num_outputs; out++) {
nn_propagate_xsto1(num_inputs, input_nodes,
&layer_weights[out * num_inputs], &layer_bias[out],
&output_nodes[out]);
}
}
else {
for (
int node = 0; node < num_outputs; ++node) {
float val = layer_bias[node];
for (
int i = 0; i < num_inputs; ++i)
val += layer_weights[node * num_inputs + i] * input_nodes[i];
// ReLU as activation function.
val = val > 0.0f ? val : 0.0f;
// Could use AOMMAX().
output_nodes[node] = val;
}
}
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);
}
