diff options
Diffstat (limited to 'dnn/nnet.c')
| -rw-r--r-- | dnn/nnet.c | 70 |
1 files changed, 9 insertions, 61 deletions
@@ -52,27 +52,11 @@ #define SOFTMAX_HACK -#define MAX_ACTIVATIONS (4096) - -static OPUS_INLINE void vec_swish(float *y, const float *x, int N) -{ - int i; - float tmp[MAX_ACTIVATIONS]; - celt_assert(N <= MAX_ACTIVATIONS); - vec_sigmoid(tmp, x, N); - for (i=0;i<N;i++) - y[i] = x[i]*tmp[i]; -} - -static OPUS_INLINE float relu(float x) -{ - return x < 0 ? 0 : x; -} void compute_generic_dense(const LinearLayer *layer, float *output, const float *input, int activation, int arch) { compute_linear(layer, output, input, arch); - compute_activation(output, output, layer->nb_outputs, activation); + compute_activation(output, output, layer->nb_outputs, activation, arch); } #define MAX_RNN_NEURONS_ALL IMAX(IMAX(FARGAN_MAX_RNN_NEURONS, PLC_MAX_RNN_NEURONS), DRED_MAX_RNN_NEURONS) @@ -99,10 +83,10 @@ void compute_generic_gru(const LinearLayer *input_weights, const LinearLayer *re compute_linear(recurrent_weights, recur, state, arch); for (i=0;i<2*N;i++) zrh[i] += recur[i]; - compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID); + compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID, arch); for (i=0;i<N;i++) h[i] += recur[2*N+i]*r[i]; - compute_activation(h, h, N, ACTIVATION_TANH); + compute_activation(h, h, N, ACTIVATION_TANH, arch); for (i=0;i<N;i++) h[i] = z[i]*state[i] + (1-z[i])*h[i]; for (i=0;i<N;i++) @@ -115,7 +99,7 @@ void compute_glu(const LinearLayer *layer, float *output, const float *input, in float act2[MAX_INPUTS]; celt_assert(layer->nb_inputs == layer->nb_outputs); compute_linear(layer, act2, input, arch); - compute_activation(act2, act2, layer->nb_outputs, ACTIVATION_SIGMOID); + compute_activation(act2, act2, layer->nb_outputs, ACTIVATION_SIGMOID, arch); if (input == output) { /* Give a vectorization hint to the compiler for the in-place case. */ for (i=0;i<layer->nb_outputs;i++) output[i] = output[i]*act2[i]; @@ -124,42 +108,6 @@ void compute_glu(const LinearLayer *layer, float *output, const float *input, in } } -void compute_activation(float *output, const float *input, int N, int activation) -{ - int i; - if (activation == ACTIVATION_SIGMOID) { - vec_sigmoid(output, input, N); - } else if (activation == ACTIVATION_TANH) { - vec_tanh(output, input, N); - } else if (activation == ACTIVATION_SWISH) { - vec_swish(output, input, N); - } else if (activation == ACTIVATION_RELU) { - for (i=0;i<N;i++) - output[i] = relu(input[i]); - } else if (activation == ACTIVATION_SOFTMAX) { -#ifdef SOFTMAX_HACK - OPUS_COPY(output, input, N); - /*for (i=0;i<N;i++) - output[i] = input[i];*/ -#else - float sum = 0; - softmax(output, input, N); - for (i=0;i<N;i++) { - sum += output[i]; - } - sum = 1.f/(sum+1e-30); - for (i=0;i<N;i++) - output[i] = sum*output[i]; -#endif - } else { - celt_assert(activation == ACTIVATION_LINEAR); - if (input != output) { - for (i=0;i<N;i++) - output[i] = input[i]; - } - } -} - void _lpcnet_compute_dense(const DenseLayer *layer, float *output, const float *input, int arch) { LinearLayer matrix; @@ -174,7 +122,7 @@ void _lpcnet_compute_dense(const DenseLayer *layer, float *output, const float * matrix.nb_outputs = layer->nb_neurons; matrix.scale = NULL; compute_linear(&matrix, output, input, arch); - compute_activation(output, output, layer->nb_neurons, layer->activation); + compute_activation(output, output, layer->nb_neurons, layer->activation, arch); } #ifdef USE_SU_BIAS @@ -242,7 +190,7 @@ void compute_generic_conv1d(const LinearLayer *layer, float *output, float *mem, OPUS_COPY(tmp, mem, layer->nb_inputs-input_size); OPUS_COPY(&tmp[layer->nb_inputs-input_size], input, input_size); compute_linear(layer, output, tmp, arch); - compute_activation(output, output, layer->nb_outputs, activation); + compute_activation(output, output, layer->nb_outputs, activation, arch); OPUS_COPY(mem, &tmp[input_size], layer->nb_inputs-input_size); } @@ -257,7 +205,7 @@ void compute_generic_conv1d_dilation(const LinearLayer *layer, float *output, fl else for (i=0;i<ksize-1;i++) OPUS_COPY(&tmp[i*input_size], &mem[i*input_size*dilation], input_size); OPUS_COPY(&tmp[layer->nb_inputs-input_size], input, input_size); compute_linear(layer, output, tmp, arch); - compute_activation(output, output, layer->nb_outputs, activation); + compute_activation(output, output, layer->nb_outputs, activation, arch); if (dilation==1) OPUS_COPY(mem, &tmp[input_size], layer->nb_inputs-input_size); else { OPUS_COPY(mem, &mem[input_size], input_size*dilation*(ksize-1)-input_size); @@ -325,7 +273,7 @@ static void conv2d_3x3_float(float *out, const float *weights, int in_channels, #define MAX_CONV2D_INPUTS 8192 -void compute_conv2d(const Conv2dLayer *conv, float *out, float *mem, const float *in, int height, int hstride, int activation) +void compute_conv2d(const Conv2dLayer *conv, float *out, float *mem, const float *in, int height, int hstride, int activation, int arch) { int i; const float *bias; @@ -349,6 +297,6 @@ void compute_conv2d(const Conv2dLayer *conv, float *out, float *mem, const float } } for (i=0;i<conv->out_channels;i++) { - compute_activation(&out[i*hstride], &out[i*hstride], height, activation); + compute_activation(&out[i*hstride], &out[i*hstride], height, activation, arch); } } |