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Update rnn.c, compute_gru support Keras double bias #190

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Update rnn.c, compute_gru support Keras double bias #190

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64 changes: 64 additions & 0 deletions src/rnn.c
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Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,14 @@
#include "rnn_data.h"
#include <stdio.h>

/* Keras now use double bias, so compute gru need to be modified, see https://kaixih.github.io/keras-cudnn-rnn/
Please remove "//" in the following line if Keras used for training uses double bias to store parameters.*/
//#define KERAS_DOUBLE_BIAS 1

#ifndef KERAS_DOUBLE_BIAS
#define KERAS_DOUBLE_BIAS 0
#endif

static OPUS_INLINE float tansig_approx(float x)
{
int i;
Expand Down Expand Up @@ -106,6 +114,61 @@ void compute_dense(const DenseLayer *layer, float *output, const float *input)
}
}

#if KERAS_DOUBLE_BIAS
void compute_gru(const GRULayer *gru, float *state, const float *input)
{
int i, j;
int N, M;
int stride;
float z[MAX_NEURONS];
float r[MAX_NEURONS];
float h[MAX_NEURONS];
M = gru->nb_inputs;
N = gru->nb_neurons;
stride = 3*N;

for (i=0;i<N;i++)
{
/* Compute update gate. */
float sum = gru->bias[i]+ gru->bias[stride+i];;
for (j=0;j<M;j++)
sum += gru->input_weights[j*stride + i]*input[j];
for (j=0;j<N;j++)
sum += gru->recurrent_weights[j*stride + i]*state[j];
z[i] = sigmoid_approx(WEIGHTS_SCALE*sum);
}
for (i=0;i<N;i++)
{
/* Compute reset gate. */
float sum = gru->bias[N + i] + gru->bias[stride+N+i];
for (j=0;j<M;j++)
sum += gru->input_weights[N + j*stride + i]*input[j];
for (j=0;j<N;j++)
sum += gru->recurrent_weights[N + j*stride + i]*state[j];
r[i] = sigmoid_approx(WEIGHTS_SCALE*sum);
}
for (i=0;i<N;i++)
{
/* Compute output. */
float sum = gru->bias[2*N + i];
for (j=0;j<M;j++)
sum += gru->input_weights[2*N + j*stride + i]*input[j];
float sum_r = gru->bias[stride + 2*N + i];
for (j=0;j<N;j++)
sum_r += (gru->recurrent_weights[2*N + j*stride + i]*state[j]);
sum += sum_r * r[i];

if (gru->activation == ACTIVATION_SIGMOID) sum = sigmoid_approx(WEIGHTS_SCALE*sum);
else if (gru->activation == ACTIVATION_TANH) sum = tansig_approx(WEIGHTS_SCALE*sum);
else if (gru->activation == ACTIVATION_RELU) sum = relu(WEIGHTS_SCALE*sum);
else *(int*)0=0;
h[i] = z[i]*state[i] + (1-z[i])*sum;
}

for (i=0;i<N;i++)
state[i] = h[i];
}
#else
void compute_gru(const GRULayer *gru, float *state, const float *input)
{
int i, j;
Expand Down Expand Up @@ -154,6 +217,7 @@ void compute_gru(const GRULayer *gru, float *state, const float *input)
for (i=0;i<N;i++)
state[i] = h[i];
}
#endif

#define INPUT_SIZE 42

Expand Down