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author | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-04-19 00:47:55 +0000 |
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committer | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-04-19 00:47:55 +0000 |
commit | 26a029d407be480d791972afb5975cf62c9360a6 (patch) | |
tree | f435a8308119effd964b339f76abb83a57c29483 /third_party/aom/av1/encoder/ml.c | |
parent | Initial commit. (diff) | |
download | firefox-26a029d407be480d791972afb5975cf62c9360a6.tar.xz firefox-26a029d407be480d791972afb5975cf62c9360a6.zip |
Adding upstream version 124.0.1.upstream/124.0.1
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to '')
-rw-r--r-- | third_party/aom/av1/encoder/ml.c | 171 |
1 files changed, 171 insertions, 0 deletions
diff --git a/third_party/aom/av1/encoder/ml.c b/third_party/aom/av1/encoder/ml.c new file mode 100644 index 0000000000..94cd56c5d1 --- /dev/null +++ b/third_party/aom/av1/encoder/ml.c @@ -0,0 +1,171 @@ +/* + * Copyright (c) 2016, 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 <assert.h> +#include <math.h> + +#include "aom_dsp/aom_dsp_common.h" +#include "aom_dsp/mathutils.h" +#include "av1/encoder/ml.h" + +void av1_nn_output_prec_reduce(float *const output, int num_output) { + const int prec_bits = 9; + const int prec = 1 << prec_bits; + const float inv_prec = (float)(1.0 / prec); + for (int i = 0; i < num_output; i++) { + output[i] = ((int)(output[i] * prec + 0.5)) * inv_prec; + } +} + +// 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_c(const float *input_nodes, + const NN_CONFIG *const nn_config, int reduce_prec, + float *const output) { + int num_input_nodes = nn_config->num_inputs; + int buf_index = 0; + float buf[2][NN_MAX_NODES_PER_LAYER]; + + // Propagate hidden layers. + const int num_layers = nn_config->num_hidden_layers; + assert(num_layers <= NN_MAX_HIDDEN_LAYERS); + for (int layer = 0; layer < num_layers; ++layer) { + const float *layer_weights = nn_config->weights[layer]; + const float *layer_bias = nn_config->bias[layer]; + float *output_nodes = buf[buf_index]; + const int num_output_nodes = nn_config->num_hidden_nodes[layer]; + assert(num_output_nodes < NN_MAX_NODES_PER_LAYER); + for (int node = 0; node < num_output_nodes; ++node) { + float val = layer_bias[node]; + for (int i = 0; i < num_input_nodes; ++i) + val += layer_weights[node * num_input_nodes + i] * input_nodes[i]; + // ReLU as activation function. + val = val > 0.0f ? val : 0.0f; // Could use AOMMAX(). + output_nodes[node] = val; + } + num_input_nodes = num_output_nodes; + input_nodes = output_nodes; + buf_index = 1 - buf_index; + } + + // Final output layer. + const float *layer_weights = nn_config->weights[num_layers]; + const float *layer_bias = nn_config->bias[num_layers]; + for (int node = 0; node < nn_config->num_outputs; ++node) { + float val = layer_bias[node]; + for (int i = 0; i < num_input_nodes; ++i) + val += layer_weights[node * num_input_nodes + i] * input_nodes[i]; + output[node] = val; + } + if (reduce_prec) av1_nn_output_prec_reduce(output, nn_config->num_outputs); +} + +#if CONFIG_NN_V2 +// Applies the ReLu activation to one fc layer +// output[i] = Max(input[i],0.0f) +static float *nn_relu(const float *input, FC_LAYER *layer) { + for (int i = 0; i < layer->num_outputs; ++i) { + layer->output[i] = AOMMAX(input[i], 0.0f); + } + + return layer->output; +} + +// Applies the Sigmoid activation to one fc layer +// output[i] = 1/(1+exp(input[i])) +static float *nn_sigmoid(const float *input, FC_LAYER *layer) { + for (int i = 0; i < layer->num_outputs; ++i) { + const float tmp = AOMMIN(AOMMAX(input[i], -10.0f), 10.0f); + layer->output[i] = 1.0f / (1.0f + expf(-tmp)); + } + + return layer->output; +} + +// Forward prediction in one fc layer, used in function av1_nn_predict_V2 +static float *nn_fc_forward(const float *input, FC_LAYER *layer) { + const float *weights = layer->weights; + const float *bias = layer->bias; + assert(layer->num_outputs < NN_MAX_NODES_PER_LAYER); + // fc + for (int node = 0; node < layer->num_outputs; ++node) { + float val = bias[node]; + for (int i = 0; i < layer->num_inputs; ++i) val += weights[i] * input[i]; + layer->output[node] = val; + weights += layer->num_inputs; + } + + // activation + switch (layer->activation) { + case NONE: return layer->output; + case RELU: return nn_relu(layer->output, layer); + case SIGMOID: return nn_sigmoid(layer->output, layer); + case SOFTSIGN: + assert(0 && "Softsign has not been supported in NN."); // TO DO + return NULL; + default: + assert(0 && "Unknown activation"); // Unknown activation + return NULL; + } +} + +void av1_nn_predict_v2(const float *feature, NN_CONFIG_V2 *nn_config, + int reduce_prec, float *output) { + const float *input_nodes = feature; + + // Propagate the layers. + const int num_layers = nn_config->num_hidden_layers; + assert(num_layers <= NN_MAX_HIDDEN_LAYERS); + for (int i = 0; i < num_layers; ++i) { + input_nodes = nn_fc_forward(input_nodes, nn_config->layer + i); + assert(nn_config->layer[i + 1].num_inputs == + nn_config->layer[i].num_outputs); + } + + // Final layer + input_nodes = nn_fc_forward(input_nodes, nn_config->layer + num_layers); + assert(nn_config->layer[num_layers].num_outputs == nn_config->num_logits); + // Copy the final layer output + memcpy(output, input_nodes, sizeof(*input_nodes) * nn_config->num_logits); + if (reduce_prec) av1_nn_output_prec_reduce(output, nn_config->num_logits); +} +#endif // CONFIG_NN_V2 + +void av1_nn_softmax(const float *input, float *output, int n) { + // Softmax function is invariant to adding the same constant + // to all input values, so we subtract the maximum input to avoid + // possible overflow. + float max_input = input[0]; + for (int i = 1; i < n; i++) max_input = AOMMAX(max_input, input[i]); + float sum_out = 0.0f; + for (int i = 0; i < n; i++) { + // Clamp to range [-10.0, 0.0] to prevent FE_UNDERFLOW errors. + const float normalized_input = AOMMAX(input[i] - max_input, -10.0f); + output[i] = expf(normalized_input); + sum_out += output[i]; + } + for (int i = 0; i < n; i++) output[i] /= sum_out; +} + +void av1_nn_fast_softmax_16_c(const float *input, float *output) { + const int kNumClasses = 16; + float max_input = input[0]; + for (int i = 1; i < kNumClasses; i++) max_input = AOMMAX(max_input, input[i]); + float sum_out = 0.0f; + for (int i = 0; i < kNumClasses; i++) { + // Clamp to range [-10.0, 0.0] to prevent FE_UNDERFLOW errors. + const float normalized_input = AOMMAX(input[i] - max_input, -10.0f); + output[i] = approx_exp(normalized_input); + sum_out += output[i]; + } + for (int i = 0; i < kNumClasses; i++) output[i] /= sum_out; +} |