WIP: PLC prediction

dnn/Makefile.am

@@ -16,6 +16,7 @@ noinst_HEADERS = arch.h  \
 		 lpcnet_private.h \
 		 opus_types.h  \
 		 nnet_data.h  \
+		 plc_data.h  \
 		 nnet.h  \
 		 pitch.h  \
 		 tansig_table.h \
@@ -31,6 +32,7 @@ liblpcnet_la_SOURCES = \
 	lpcnet_enc.c \
 	nnet.c \
 	nnet_data.c \
+	plc_data.c \
 	ceps_codebooks.c \
 	pitch.c \
 	freq.c \

dnn/lpcnet_plc.c

@@ -30,9 +30,11 @@
 
 #include "lpcnet_private.h"
 #include "lpcnet.h"
+#include "plc_data.h"
 
 #define PLC_DUMP_FEATURES 0
 #define PLC_READ_FEATURES 0
+#define PLC_DNN_PRED 1
 
 LPCNET_EXPORT int lpcnet_plc_get_size() {
   return sizeof(LPCNetPLCState);
@@ -58,6 +60,15 @@ LPCNET_EXPORT void lpcnet_plc_destroy(LPCNetPLCState *st) {
   free(st);
 }
 
+static void compute_plc_pred(PLCNetState *net, float *out, const float *in) {
+  float zeros[1024] = {0};
+  float dense_out[PLC_DENSE1_OUT_SIZE];
+  _lpcnet_compute_dense(&plc_dense1, dense_out, in);
+  compute_gruB(&plc_gru1, zeros, net->plc_gru1_state, dense_out);
+  compute_gruB(&plc_gru2, zeros, net->plc_gru2_state, net->plc_gru1_state);
+  if (out != NULL) _lpcnet_compute_dense(&plc_out, out, net->plc_gru2_state);
+}
+
 LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
   int i;
   float x[FRAME_SIZE];
@@ -99,6 +110,9 @@ LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
     for (i=0;i<FRAME_SIZE;i++) st->pcm[PLC_BUF_SIZE+i] = pcm[i];
     RNN_COPY(output, &st->pcm[0], FRAME_SIZE);
     lpcnet_synthesize_impl(&st->lpcnet, st->enc.features[0], output, FRAME_SIZE, FRAME_SIZE);
+#if PLC_DNN_PRED
+    compute_plc_pred(&st->plc_net, NULL, st->enc.features[0]);
+#endif
 #if PLC_READ_FEATURES
     for (i=0;i<NB_FEATURES;i++) scanf("%f", &st->features[i]);
 #endif
@@ -106,7 +120,6 @@ LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
     for (i=0;i<NB_FEATURES;i++) printf("%f ", st->enc.features[0][i]);
     printf("1\n");
 #endif
-
     RNN_MOVE(st->pcm, &st->pcm[FRAME_SIZE], PLC_BUF_SIZE);
   }
   RNN_COPY(st->features, st->enc.features[0], NB_TOTAL_FEATURES);
@@ -118,6 +131,7 @@ LPCNET_EXPORT int lpcnet_plc_conceal(LPCNetPLCState *st, short *pcm) {
   int i;
 #endif
   short output[FRAME_SIZE];
+  float zeros[NB_FEATURES+1] = {0};
   st->enc.pcount = 0;
   /* If we concealed the previous frame, finish synthesizing the rest of the samples. */
   /* FIXME: Copy/predict features. */
@@ -126,6 +140,9 @@ LPCNET_EXPORT int lpcnet_plc_conceal(LPCNetPLCState *st, short *pcm) {
     int update_count;
     update_count = IMIN(st->pcm_fill, FRAME_SIZE);
     RNN_COPY(output, &st->pcm[0], update_count);
+#if PLC_DNN_PRED
+    compute_plc_pred(&st->plc_net, st->features, zeros);
+#endif
 #if PLC_READ_FEATURES
     for (i=0;i<NB_FEATURES;i++) scanf("%f", &st->features[i]);
 #endif
@@ -139,6 +156,9 @@ LPCNET_EXPORT int lpcnet_plc_conceal(LPCNetPLCState *st, short *pcm) {
     st->skip_analysis++;
   }
   lpcnet_synthesize_tail_impl(&st->lpcnet, pcm, FRAME_SIZE-TRAINING_OFFSET, 0);
+#if PLC_DNN_PRED
+    compute_plc_pred(&st->plc_net, st->features, zeros);
+#endif
 #if PLC_READ_FEATURES
   for (i=0;i<NB_FEATURES;i++) scanf("%f", &st->features[i]);
 #endif

dnn/lpcnet_private.h

@@ -6,6 +6,7 @@
 #include "freq.h"
 #include "lpcnet.h"
 #include "nnet_data.h"
+#include "plc_data.h"
 #include "kiss99.h"
 
 #define BITS_PER_CHAR 8
@@ -74,6 +75,7 @@ struct LPCNetPLCState {
   int skip_analysis;
   int blend;
   float features[NB_TOTAL_FEATURES];
+  PLCNetState plc_net;
 };
 
 extern float ceps_codebook1[];

dnn/training_tf2/dump_plc.py

+#!/usr/bin/python3
+'''Copyright (c) 2021-2022 Amazon
+   Copyright (c) 2017-2018 Mozilla
+
+   Redistribution and use in source and binary forms, with or without
+   modification, are permitted provided that the following conditions
+   are met:
+
+   - Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+   - Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+
+   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+   ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+   A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE FOUNDATION OR
+   CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+   PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+'''
+
+import lpcnet_plc
+import sys
+import numpy as np
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.layers import Layer, GRU, Dense, Conv1D, Embedding
+import h5py
+import re
+
+# Flag for dumping e2e (differentiable lpc) network weights
+flag_e2e = False
+
+max_rnn_neurons = 1
+max_conv_inputs = 1
+
+def printVector(f, vector, name, dtype='float', dotp=False):
+    if dotp:
+        vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8))
+        vector = vector.transpose((2, 0, 3, 1))
+    v = np.reshape(vector, (-1));
+    #print('static const float ', name, '[', len(v), '] = \n', file=f)
+    f.write('static const {} {}[{}] = {{\n   '.format(dtype, name, len(v)))
+    for i in range(0, len(v)):
+        f.write('{}'.format(v[i]))
+        if (i!=len(v)-1):
+            f.write(',')
+        else:
+            break;
+        if (i%8==7):
+            f.write("\n   ")
+        else:
+            f.write(" ")
+    #print(v, file=f)
+    f.write('\n};\n\n')
+    return;
+
+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,))
+    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')
+    idx = np.zeros((0,), dtype='int')
+    for i in range(M//8):
+        pos = idx.shape[0]
+        idx = np.append(idx, -1)
+        nb_nonzero = 0
+        for j in range(N//4):
+            block = A[j*4:(j+1)*4, i*8:(i+1)*8]
+            qblock = AQ[j*4:(j+1)*4, i*8:(i+1)*8]
+            if np.sum(np.abs(block)) > 1e-10:
+                nb_nonzero = nb_nonzero + 1
+                idx = np.append(idx, j*4)
+                vblock = qblock.transpose((1,0)).reshape((-1,))
+                W0 = np.concatenate([W0, block.reshape((-1,))])
+                W = np.concatenate([W, vblock])
+        idx[pos] = nb_nonzero
+    f.write('#ifdef DOT_PROD\n')
+    printVector(f, W, name, dtype='qweight')
+    f.write('#else /*DOT_PROD*/\n')
+    printVector(f, W0, name, dtype='qweight')
+    f.write('#endif /*DOT_PROD*/\n')
+    #idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16)
+    printVector(f, idx, name + '_idx', dtype='int')
+    return AQ
+
+def dump_layer_ignore(self, f, hf):
+    print("ignoring layer " + self.name + " of type " + self.__class__.__name__)
+    return False
+Layer.dump_layer = dump_layer_ignore
+
+def dump_sparse_gru(self, f, hf):
+    global max_rnn_neurons
+    name = 'sparse_' + self.name
+    print("printing layer " + name + " of type sparse " + self.__class__.__name__)
+    weights = self.get_weights()
+    qweights = printSparseVector(f, weights[1], name + '_recurrent_weights')
+    printVector(f, weights[-1], name + '_bias')
+    subias = weights[-1].copy()
+    subias[1,:] = subias[1,:] - np.sum(qweights*(1./128),axis=0)
+    printVector(f, subias, name + '_subias')
+    if hasattr(self, 'activation'):
+        activation = self.activation.__name__.upper()
+    else:
+        activation = 'TANH'
+    if hasattr(self, 'reset_after') and not self.reset_after:
+        reset_after = 0
+    else:
+        reset_after = 1
+    neurons = weights[0].shape[1]//3
+    max_rnn_neurons = max(max_rnn_neurons, neurons)
+    f.write('const SparseGRULayer {} = {{\n   {}_bias,\n   {}_subias,\n   {}_recurrent_weights_diag,\n   {}_recurrent_weights,\n   {}_recurrent_weights_idx,\n   {}, ACTIVATION_{}, {}\n}};\n\n'
+            .format(name, name, name, name, name, name, weights[0].shape[1]//3, activation, reset_after))
+    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
+    hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
+    hf.write('extern const SparseGRULayer {};\n\n'.format(name));
+    return True
+
+def dump_gru_layer(self, f, hf):
+    global max_rnn_neurons
+    name = self.name
+    print("printing layer " + name + " of type " + self.__class__.__name__)
+    weights = self.get_weights()
+    qweight = printSparseVector(f, weights[0], name + '_weights', have_diag=False)
+
+    f.write('#ifdef DOT_PROD\n')
+    qweight2 = np.clip(np.round(128.*weights[1]).astype('int'), -128, 127)
+    printVector(f, qweight2, name + '_recurrent_weights', dotp=True, dtype='qweight')
+    f.write('#else /*DOT_PROD*/\n')
+    printVector(f, weights[1], name + '_recurrent_weights')
+    f.write('#endif /*DOT_PROD*/\n')
+
+    printVector(f, weights[-1], name + '_bias')
+    subias = weights[-1].copy()
+    subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0)
+    subias[1,:] = subias[1,:] - np.sum(qweight2*(1./128.),axis=0)
+    printVector(f, subias, name + '_subias')
+    if hasattr(self, 'activation'):
+        activation = self.activation.__name__.upper()
+    else:
+        activation = 'TANH'
+    if hasattr(self, 'reset_after') and not self.reset_after:
+        reset_after = 0
+    else:
+        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   {}_weights_idx,\n   {}_recurrent_weights,\n   {}, {}, ACTIVATION_{}, {}\n}};\n\n'
+            .format(name, name, name, name, name, name, weights[0].shape[0], weights[0].shape[1]//3, activation, reset_after))
+    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
+    hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
+    hf.write('extern const GRULayer {};\n\n'.format(name));
+    return True
+GRU.dump_layer = dump_gru_layer
+
+def dump_gru_layer_dummy(self, f, hf):
+    name = self.name
+    weights = self.get_weights()
+    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
+    hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
+    return True;
+
+#GRU.dump_layer = dump_gru_layer_dummy
+
+def dump_dense_layer_impl(name, weights, bias, activation, f, hf):
+    printVector(f, weights, name + '_weights')
+    printVector(f, bias, name + '_bias')
+    f.write('const DenseLayer {} = {{\n   {}_bias,\n   {}_weights,\n   {}, {}, ACTIVATION_{}\n}};\n\n'
+            .format(name, name, name, weights.shape[0], weights.shape[1], activation))
+    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1]))
+    hf.write('extern const DenseLayer {};\n\n'.format(name));
+
+def dump_dense_layer(self, f, hf):
+    name = self.name
+    print("printing layer " + name + " of type " + self.__class__.__name__)
+    weights = self.get_weights()
+    activation = self.activation.__name__.upper()
+    dump_dense_layer_impl(name, weights[0], weights[1], activation, f, hf)
+    return False
+
+Dense.dump_layer = dump_dense_layer
+
+def dump_conv1d_layer(self, f, hf):
+    global max_conv_inputs
+    name = self.name
+    print("printing layer " + name + " of type " + self.__class__.__name__)
+    weights = self.get_weights()
+    printVector(f, weights[0], name + '_weights')
+    printVector(f, weights[-1], name + '_bias')
+    activation = self.activation.__name__.upper()
+    max_conv_inputs = max(max_conv_inputs, weights[0].shape[1]*weights[0].shape[0])
+    f.write('const Conv1DLayer {} = {{\n   {}_bias,\n   {}_weights,\n   {}, {}, {}, ACTIVATION_{}\n}};\n\n'
+            .format(name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))
+    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[2]))
+    hf.write('#define {}_STATE_SIZE ({}*{})\n'.format(name.upper(), weights[0].shape[1], (weights[0].shape[0]-1)))
+    hf.write('#define {}_DELAY {}\n'.format(name.upper(), (weights[0].shape[0]-1)//2))
+    hf.write('extern const Conv1DLayer {};\n\n'.format(name));
+    return True
+Conv1D.dump_layer = dump_conv1d_layer
+
+
+
+filename = sys.argv[1]
+with h5py.File(filename, "r") as f:
+    units = min(f['model_weights']['plc_gru1']['plc_gru1']['recurrent_kernel:0'].shape)
+    units2 = min(f['model_weights']['plc_gru2']['plc_gru2']['recurrent_kernel:0'].shape)
+    cond_size = f['model_weights']['plc_dense1']['plc_dense1']['kernel:0'].shape[1]
+
+model = lpcnet_plc.new_lpcnet_plc_model(rnn_units=units, cond_size=cond_size)
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
+#model.summary()
+
+model.load_weights(filename, by_name=True)
+
+if len(sys.argv) > 2:
+    cfile = sys.argv[2];
+    hfile = sys.argv[3];
+else:
+    cfile = 'plc_data.c'
+    hfile = 'plc_data.h'
+
+
+f = open(cfile, 'w')
+hf = open(hfile, 'w')
+
+
+f.write('/*This file is automatically generated from a Keras model*/\n')
+f.write('/*based on model {}*/\n\n'.format(sys.argv[1]))
+f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "nnet.h"\n#include "{}"\n\n'.format(hfile))
+
+hf.write('/*This file is automatically generated from a Keras model*/\n\n')
+hf.write('#ifndef PLC_DATA_H\n#define PLC_DATA_H\n\n#include "nnet.h"\n\n')
+
+layer_list = []
+for i, layer in enumerate(model.layers):
+    if layer.dump_layer(f, hf):
+        layer_list.append(layer.name)
+
+#dump_sparse_gru(model.get_layer('gru_a'), f, hf)
+
+hf.write('#define PLC_MAX_RNN_NEURONS {}\n\n'.format(max_rnn_neurons))
+#hf.write('#define PLC_MAX_CONV_INPUTS {}\n\n'.format(max_conv_inputs))
+
+hf.write('typedef struct {\n')
+for i, name in enumerate(layer_list):
+    hf.write('  float {}_state[{}_STATE_SIZE];\n'.format(name, name.upper())) 
+hf.write('} PLCNetState;\n')
+
+hf.write('\n\n#endif\n')
+
+f.close()
+hf.close()