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Support for sparse GRU B input matrices

Only on the C side, no sparse GRU B training yet

--- a/dnn/nnet.c

+++ b/dnn/nnet.c

@@ -323,7 +323,7 @@

    for (i=0;i<3*N;i++)

       zrh[i] = gru->bias[i] + gru_b_condition[i];

 #endif

-   sgemv_accum8x4(zrh, gru->input_weights, 3*N, M, stride, input);

+   sparse_sgemv_accum8x4(zrh, gru->input_weights, 3*N, M, gru->input_weights_idx, input);

    for (i=0;i<3*N;i++)

       recur[i] = gru->bias[3*N + i];

    sgemv_accum(recur, gru->recurrent_weights, 3*N, N, stride, state);

--- a/dnn/nnet.h

+++ b/dnn/nnet.h

@@ -58,6 +58,7 @@

   const float *bias;

   const float *subias;

   const qweight *input_weights;

+  const int *input_weights_idx;

   const float *recurrent_weights;

   int nb_inputs;

   int nb_neurons;

--- a/dnn/training_tf2/dump_lpcnet.py

+++ b/dnn/training_tf2/dump_lpcnet.py

@@ -60,18 +60,20 @@

     f.write('\n};\n\n')

     return;

-def printSparseVector(f, A, name):

+def printSparseVector(f, A, name, have_diag=True):

     N = A.shape[0]

+    M = A.shape[1]

     W = np.zeros((0,), dtype='int')

     W0 = np.zeros((0,))

-    diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])])

-    A[:,:N] = A[:,:N] - np.diag(np.diag(A[:,:N]))

-    A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N]))

-    A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:]))

+    if have_diag:

+        diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])])

+        A[:,:N] = A[:,:N] - np.diag(np.diag(A[:,:N]))

+        A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N]))

+        A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:]))

+        printVector(f, diag, name + '_diag')

     AQ = np.minimum(127, np.maximum(-128, np.round(A*128))).astype('int')

-    printVector(f, diag, name + '_diag')

     idx = np.zeros((0,), dtype='int')

-    for i in range(3*N//8):

+    for i in range(M//8):

         pos = idx.shape[0]

         idx = np.append(idx, -1)

         nb_nonzero = 0

@@ -131,12 +133,7 @@

     name = self.name

     print("printing layer " + name + " of type " + self.__class__.__name__)

     weights = self.get_weights()

-    f.write('#ifdef DOT_PROD\n')

-    qweight = np.clip(np.round(128.*weights[0][:gru_a_size, :]).astype('int'), -128, 127)

-    printVector(f, qweight, name + '_weights', dotp=True, dtype='qweight')

-    f.write('#else /*DOT_PROD*/\n')

-    printVector(f, weights[0][:gru_a_size, :], name + '_weights')

-    f.write('#endif /*DOT_PROD*/\n')

+    qweight = printSparseVector(f, weights[0][:gru_a_size, :], name + '_weights', have_diag=False)

     printVector(f, weights[1], name + '_recurrent_weights')

     printVector(f, weights[-1], name + '_bias')

     subias = weights[-1].copy()

@@ -152,8 +149,8 @@

         reset_after = 1

     neurons = weights[0].shape[1]//3

     max_rnn_neurons = max(max_rnn_neurons, neurons)

-    f.write('const GRULayer {} = {{\n   {}_bias,\n   {}_subias,\n   {}_weights,\n   {}_recurrent_weights,\n   {}, {}, ACTIVATION_{}, {}\n}};\n\n'

-            .format(name, name, name, name, name, gru_a_size, weights[0].shape[1]//3, activation, reset_after))

+    f.write('const GRULayer {} = {{\n   {}_bias,\n   {}_subias,\n   {}_weights,\n   {}_weights_idx,\n   {}_recurrent_weights,\n   {}, {}, ACTIVATION_{}, {}\n}};\n\n'

+            .format(name, name, name, name, name, name, gru_a_size, weights[0].shape[1]//3, activation, reset_after))

     hf.write('extern const GRULayer {};\n\n'.format(name));

     return True

-- 

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