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Using the right name: s/gemm/sgemv/

--- a/dnn/nnet.c

+++ b/dnn/nnet.c

@@ -133,7 +133,7 @@

     }

 }

-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 @@

       _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 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 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 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 @@

    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 @@

    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 @@

       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 @@

       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 @@

    {

       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 @@

    /* 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 @@

    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 @@

       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 @@

    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));

 }

-- 

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