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Replacing RNN_ macros with existing OPUS_ ones

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Jean-Marc Valin 2023-06-23 00:02:12 -04:00
parent 5af9e9524a
commit 9f4fc8bbfa
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GPG key ID: 531A52533318F00A
12 changed files with 79 additions and 108 deletions
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11
dnn/nnet.c
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@ -40,6 +40,7 @@
#include "nnet_data.h"
#include "dred_rdovae_constants.h"
#include "plc_data.h"
#include "os_support.h"
#ifdef NO_OPTIMIZATIONS
#if defined(_MSC_VER)
@ -99,7 +100,7 @@ void compute_activation(float *output, const float *input, int N, int activation
output[i] = relu(input[i]);
} else if (activation == ACTIVATION_SOFTMAX) {
#ifdef SOFTMAX_HACK
RNN_COPY(output, input, N);
OPUS_COPY(output, input, N);
/*for (i=0;i<N;i++)
output[i] = input[i];*/
#else
@ -390,7 +391,7 @@ void compute_gru3(const GRULayer *gru, float *state, const float *input)
celt_assert(input != state);
celt_assert(gru->reset_after);
stride = 3*N;
RNN_COPY(zrh, input, 3*N);
OPUS_COPY(zrh, input, 3*N);
for (i=0;i<3*N;i++)
recur[i] = gru->bias[3*N + i];
sgemv_accum8x4(recur, gru->recurrent_weights, 3*N, N, stride, state);
@ -457,8 +458,8 @@ void compute_conv1d(const Conv1DLayer *layer, float *output, float *mem, const f
float tmp[MAX_CONV_INPUTS_ALL];
celt_assert(input != output);
celt_assert(layer->nb_inputs*layer->kernel_size <= MAX_CONV_INPUTS_ALL);
RNN_COPY(tmp, mem, layer->nb_inputs*(layer->kernel_size-1));
RNN_COPY(&tmp[layer->nb_inputs*(layer->kernel_size-1)], input, layer->nb_inputs);
OPUS_COPY(tmp, mem, layer->nb_inputs*(layer->kernel_size-1));
OPUS_COPY(&tmp[layer->nb_inputs*(layer->kernel_size-1)], input, layer->nb_inputs);
M = layer->nb_inputs*layer->kernel_size;
N = layer->nb_neurons;
stride = N;
@ -466,7 +467,7 @@ void compute_conv1d(const Conv1DLayer *layer, float *output, float *mem, const f
output[i] = layer->bias[i];
sgemv_accum(output, layer->input_weights, N, M, stride, tmp);
compute_activation(output, output, N, layer->activation);
RNN_COPY(mem, &tmp[layer->nb_inputs], layer->nb_inputs*(layer->kernel_size-1));
OPUS_COPY(mem, &tmp[layer->nb_inputs], layer->nb_inputs*(layer->kernel_size-1));
}
void compute_embedding(const EmbeddingLayer *layer, float *output, int input)