#!/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. 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''' import lpcnet_plc import io 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): global array_list 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) if name not in array_list: array_list.append(name) f.write('#ifndef USE_WEIGHTS_FILE\n') f.write('#define WEIGHTS_{}_DEFINED\n'.format(name)) f.write('#define WEIGHTS_{}_TYPE WEIGHT_TYPE_{}\n'.format(name, dtype)) 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') f.write('#endif\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) 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)) model_struct.write(' SparseGRULayer {};\n'.format(name)); model_init.write(' if (sparse_gru_init(&model->{}, arrays, "{}_bias", "{}_subias", "{}_recurrent_weights_diag", "{}_recurrent_weights", "{}_recurrent_weights_idx", {}, ACTIVATION_{}, {})) return 1;\n' .format(name, name, name, name, name, name, weights[0].shape[1]//3, activation, reset_after)) 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) 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)) model_struct.write(' GRULayer {};\n'.format(name)); model_init.write(' if (gru_init(&model->{}, arrays, "{}_bias", "{}_subias", "{}_weights", "{}_weights_idx", "{}_recurrent_weights", {}, {}, ACTIVATION_{}, {})) return 1;\n' .format(name, name, name, name, name, name, weights[0].shape[0], weights[0].shape[1]//3, activation, reset_after)) 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') hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1])) model_struct.write(' DenseLayer {};\n'.format(name)); model_init.write(' if (dense_init(&model->{}, arrays, "{}_bias", "{}_weights", {}, {}, ACTIVATION_{})) return 1;\n' .format(name, name, name, weights.shape[0], weights.shape[1], activation)) 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]) 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)) model_struct.write(' Conv1DLayer {};\n'.format(name)); model_init.write(' if (conv1d_init(&model->{}, arrays, "{}_bias", "{}_weights", {}, {}, {}, ACTIVATION_{})) return 1;\n' .format(name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation)) 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') model_struct = io.StringIO() model_init = io.StringIO() model_struct.write('typedef struct {\n') model_init.write('#ifndef DUMP_BINARY_WEIGHTS\n') model_init.write('int init_plc_model(PLCModel *model, const WeightArray *arrays) {\n') array_list = [] 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) f.write('#ifndef USE_WEIGHTS_FILE\n') f.write('const WeightArray lpcnet_plc_arrays[] = {\n') for name in array_list: f.write('#ifdef WEIGHTS_{}_DEFINED\n'.format(name)) f.write(' {{"{}", WEIGHTS_{}_TYPE, sizeof({}), {}}},\n'.format(name, name, name, name)) f.write('#endif\n') f.write(' {NULL, 0, 0, NULL}\n};\n') f.write('#endif\n') model_init.write(' return 0;\n}\n') model_init.write('#endif\n') f.write(model_init.getvalue()) 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\n') model_struct.write('} PLCModel;\n\n') hf.write(model_struct.getvalue()) hf.write('int init_plc_model(PLCModel *model, const WeightArray *arrays);\n\n') hf.write('\n\n#endif\n') f.close() hf.close()