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Adding RTCD for DNN code

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Starting with compute_linear()
This commit is contained in:
Jean-Marc Valin 2023-11-13 18:26:31 -05:00
parent b0620c0bf9
commit 2e034f6f31
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GPG key ID: 531A52533318F00A
31 changed files with 539 additions and 165 deletions
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37
dnn/nnet.h
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@ -126,18 +126,18 @@ typedef struct {
int dim;
} EmbeddingLayer;
void compute_generic_dense(const LinearLayer *layer, float *output, const float *input, int activation);
void compute_generic_gru(const LinearLayer *input_weights, const LinearLayer *recurrent_weights, float *state, const float *in);
void compute_generic_conv1d(const LinearLayer *layer, float *output, float *mem, const float *input, int input_size, int activation);
void compute_generic_conv1d_dilation(const LinearLayer *layer, float *output, float *mem, const float *input, int input_size, int dilation, int activation);
void compute_glu(const LinearLayer *layer, float *output, const float *input);
void compute_gated_activation(const LinearLayer *layer, float *output, const float *input, int activation);
void compute_generic_dense(const LinearLayer *layer, float *output, const float *input, int activation, int arch);
void compute_generic_gru(const LinearLayer *input_weights, const LinearLayer *recurrent_weights, float *state, const float *in, int arch);
void compute_generic_conv1d(const LinearLayer *layer, float *output, float *mem, const float *input, int input_size, int activation, int arch);
void compute_generic_conv1d_dilation(const LinearLayer *layer, float *output, float *mem, const float *input, int input_size, int dilation, int activation, int arch);
void compute_glu(const LinearLayer *layer, float *output, const float *input, int arch);
void compute_gated_activation(const LinearLayer *layer, float *output, const float *input, int activation, int arch);
void compute_activation(float *output, const float *input, int N, int activation);
void _lpcnet_compute_dense(const DenseLayer *layer, float *output, const float *input);
void _lpcnet_compute_dense(const DenseLayer *layer, float *output, const float *input, int arch);
void compute_gruB(const GRULayer *gru, const float* gru_b_condition, float *state, const float *input);
void compute_gruB(const GRULayer *gru, const float* gru_b_condition, float *state, const float *input, int arch);
@ -189,4 +189,25 @@ int gru_init(GRULayer *layer, const WeightArray *arrays,
void compute_conv2d(const Conv2dLayer *conv, float *out, float *mem, const float *in, int height, int hstride, int activation);
void compute_linear_c(const LinearLayer *linear, float *out, const float *in);
#if defined(OPUS_X86_MAY_HAVE_SSE2)
#include "x86/dnn_x86.h"
#endif
#ifndef OVERRIDE_COMPUTE_LINEAR
#define compute_linear(linear, out, in, arch) ((void)(arch),compute_linear_c(linear, out, in))
#endif
#if defined(__x86_64__) && !defined(OPUS_X86_MAY_HAVE_SSE4_1) && !defined(OPUS_X86_MAY_HAVE_AVX2)
#if defined(_MSC_VER)
#pragma message ("Only SSE and SSE2 are available. On newer machines, enable SSSE3/AVX/AVX2 to get better performance")
#else
#warning "Only SSE and SSE2 are available. On newer machines, enable SSSE3/AVX/AVX2 using -march= to get better performance"
#endif
#endif
#endif /* NNET_H_ */