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Using the right name: s/gemm/sgemv/

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This commit is contained in:
Jean-Marc Valin 2018-11-30 10:56:44 -05:00
parent c395a68b7d
commit b05f950e38
1 changed files with 21 additions and 21 deletions
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42
dnn/nnet.c
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@ -133,7 +133,7 @@ static void vec_sigmoid(float *y, const float *x, int N)
}
}
static void gemm_accum16(float *out, const float *weights, int rows, int cols, int col_stride, const float *x)
static void sgemv_accum16(float *out, const float *weights, int rows, int cols, int col_stride, const float *x)
{
int i, j;
for (i=0;i<rows;i+=16)
@ -159,7 +159,7 @@ static void gemm_accum16(float *out, const float *weights, int rows, int cols, i
_mm256_storeu_ps (&y[8], vy8);
}
}
static void sparse_gemm_accum16(float *out, const float *weights, int rows, const int *idx, const float *x)
static void sparse_sgemv_accum16(float *out, const float *weights, int rows, const int *idx, const float *x)
{
int i, j;
for (i=0;i<rows;i+=16)
@ -277,7 +277,7 @@ static void vec_sigmoid(float *y, const float *x, int N)
}
}
static void gemm_accum16(float *out, const float *weights, int rows, int cols, int col_stride, const float *x)
static void sgemv_accum16(float *out, const float *weights, int rows, int cols, int col_stride, const float *x)
{
int i, j;
for (i=0;i<rows;i+=16)
@ -310,7 +310,7 @@ static void gemm_accum16(float *out, const float *weights, int rows, int cols, i
}
}
static void sparse_gemm_accum16(float *out, const float *w, int rows, const int *idx, const float *x)
static void sparse_sgemv_accum16(float *out, const float *w, int rows, const int *idx, const float *x)
{
int i, j;
for (i=0;i<rows;i+=16)
@ -353,12 +353,12 @@ static OPUS_INLINE float relu(float x)
}
static void gemm_accum(float *out, const float *weights, int rows, int cols, int col_stride, const float *x)
static void sgemv_accum(float *out, const float *weights, int rows, int cols, int col_stride, const float *x)
{
int i, j;
if (rows % 16 == 0)
{
gemm_accum16(out, weights, rows, cols, col_stride, x);
sgemv_accum16(out, weights, rows, cols, col_stride, x);
} else {
for (i=0;i<rows;i++)
{
@ -410,7 +410,7 @@ void compute_dense(const DenseLayer *layer, float *output, const float *input)
celt_assert(input != output);
for (i=0;i<N;i++)
output[i] = layer->bias[i];
gemm_accum(output, layer->input_weights, N, M, stride, input);
sgemv_accum(output, layer->input_weights, N, M, stride, input);
compute_activation(output, output, N, layer->activation);
}
@ -428,7 +428,7 @@ void compute_mdense(const MDenseLayer *layer, float *output, const float *input)
stride = N*C;
for (i=0;i<N*C;i++)
tmp[i] = layer->bias[i];
gemm_accum(tmp, layer->input_weights, N*C, M, stride, input);
sgemv_accum(tmp, layer->input_weights, N*C, M, stride, input);
compute_activation(tmp, tmp, N*C, ACTIVATION_TANH);
for (i=0;i<N;i++)
output[i] = 0;
@ -462,8 +462,8 @@ void compute_gru(const GRULayer *gru, float *state, const float *input)
for (i=0;i<N;i++)
z[i] += gru->bias[3*N + i];
}
gemm_accum(z, gru->input_weights, N, M, stride, input);
gemm_accum(z, gru->recurrent_weights, N, N, stride, state);
sgemv_accum(z, gru->input_weights, N, M, stride, input);
sgemv_accum(z, gru->recurrent_weights, N, N, stride, state);
compute_activation(z, z, N, ACTIVATION_SIGMOID);
/* Compute reset gate. */
@ -474,8 +474,8 @@ void compute_gru(const GRULayer *gru, float *state, const float *input)
for (i=0;i<N;i++)
r[i] += gru->bias[4*N + i];
}
gemm_accum(r, &gru->input_weights[N], N, M, stride, input);
gemm_accum(r, &gru->recurrent_weights[N], N, N, stride, state);
sgemv_accum(r, &gru->input_weights[N], N, M, stride, input);
sgemv_accum(r, &gru->recurrent_weights[N], N, N, stride, state);
compute_activation(r, r, N, ACTIVATION_SIGMOID);
/* Compute output. */
@ -485,15 +485,15 @@ void compute_gru(const GRULayer *gru, float *state, const float *input)
{
for (i=0;i<N;i++)
tmp[i] = gru->bias[5*N + i];
gemm_accum(tmp, &gru->recurrent_weights[2*N], N, N, stride, state);
sgemv_accum(tmp, &gru->recurrent_weights[2*N], N, N, stride, state);
for (i=0;i<N;i++)
h[i] += tmp[i] * r[i];
gemm_accum(h, &gru->input_weights[2*N], N, M, stride, input);
sgemv_accum(h, &gru->input_weights[2*N], N, M, stride, input);
} else {
for (i=0;i<N;i++)
tmp[i] = state[i] * r[i];
gemm_accum(h, &gru->input_weights[2*N], N, M, stride, input);
gemm_accum(h, &gru->recurrent_weights[2*N], N, N, stride, tmp);
sgemv_accum(h, &gru->input_weights[2*N], N, M, stride, input);
sgemv_accum(h, &gru->recurrent_weights[2*N], N, N, stride, tmp);
}
compute_activation(h, h, N, gru->activation);
for (i=0;i<N;i++)
@ -524,10 +524,10 @@ void compute_gru2(const GRULayer *gru, float *state, const float *input)
/* Compute update gate. */
for (i=0;i<3*N;i++)
zrh[i] = gru->bias[i];
gemm_accum(zrh, gru->input_weights, 3*N, M, stride, input);
sgemv_accum(zrh, gru->input_weights, 3*N, M, stride, input);
for (i=0;i<3*N;i++)
recur[i] = gru->bias[3*N + i];
gemm_accum(recur, gru->recurrent_weights, 3*N, N, stride, state);
sgemv_accum(recur, gru->recurrent_weights, 3*N, N, stride, state);
for (i=0;i<2*N;i++)
zrh[i] += recur[i];
compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID);
@ -561,7 +561,7 @@ void compute_gru3(const GRULayer *gru, float *state, const float *input)
RNN_COPY(zrh, input, 3*N);
for (i=0;i<3*N;i++)
recur[i] = gru->bias[3*N + i];
gemm_accum(recur, gru->recurrent_weights, 3*N, N, stride, state);
sgemv_accum(recur, gru->recurrent_weights, 3*N, N, stride, state);
for (i=0;i<2*N;i++)
zrh[i] += recur[i];
compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID);
@ -598,7 +598,7 @@ void compute_sparse_gru(const SparseGRULayer *gru, float *state, const float *in
for (i=0;i<N;i++)
recur[k*N + i] += gru->diag_weights[k*N + i]*state[i];
}
sparse_gemm_accum16(recur, gru->recurrent_weights, 3*N, gru->idx, state);
sparse_sgemv_accum16(recur, gru->recurrent_weights, 3*N, gru->idx, state);
for (i=0;i<2*N;i++)
zrh[i] += recur[i];
compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID);
@ -626,7 +626,7 @@ void compute_conv1d(const Conv1DLayer *layer, float *output, float *mem, const f
stride = N;
for (i=0;i<N;i++)
output[i] = layer->bias[i];
gemm_accum(output, layer->input_weights, N, M, stride, tmp);
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));
}