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189 lines
8.4 KiB
Python
189 lines
8.4 KiB
Python
'''Copyright (c) 2017-2018 Mozilla
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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- Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR
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CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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'''
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""" helper functions for dumping some Keras layers to C files """
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import numpy as np
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def printVector(f, vector, name, dtype='float', dotp=False, static=True):
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""" prints vector as one-dimensional C array """
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if dotp:
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vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8))
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vector = vector.transpose((2, 0, 3, 1))
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v = np.reshape(vector, (-1))
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if static:
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f.write('static const {} {}[{}] = {{\n '.format(dtype, name, len(v)))
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else:
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f.write('const {} {}[{}] = {{\n '.format(dtype, name, len(v)))
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for i in range(0, len(v)):
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f.write('{}'.format(v[i]))
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if (i!=len(v)-1):
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f.write(',')
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else:
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break;
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if (i%8==7):
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f.write("\n ")
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else:
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f.write(" ")
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f.write('\n};\n\n')
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return vector
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def printSparseVector(f, A, name, have_diag=True):
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N = A.shape[0]
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M = A.shape[1]
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W = np.zeros((0,), dtype='int')
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W0 = np.zeros((0,))
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if have_diag:
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diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])])
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A[:,:N] = A[:,:N] - np.diag(np.diag(A[:,:N]))
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A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N]))
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A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:]))
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printVector(f, diag, name + '_diag')
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AQ = np.minimum(127, np.maximum(-128, np.round(A*128))).astype('int')
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idx = np.zeros((0,), dtype='int')
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for i in range(M//8):
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pos = idx.shape[0]
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idx = np.append(idx, -1)
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nb_nonzero = 0
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for j in range(N//4):
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block = A[j*4:(j+1)*4, i*8:(i+1)*8]
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qblock = AQ[j*4:(j+1)*4, i*8:(i+1)*8]
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if np.sum(np.abs(block)) > 1e-10:
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nb_nonzero = nb_nonzero + 1
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idx = np.append(idx, j*4)
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vblock = qblock.transpose((1,0)).reshape((-1,))
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W0 = np.concatenate([W0, block.reshape((-1,))])
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W = np.concatenate([W, vblock])
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idx[pos] = nb_nonzero
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f.write('#ifdef DOT_PROD\n')
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printVector(f, W, name, dtype='qweight')
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f.write('#else /*DOT_PROD*/\n')
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printVector(f, W0, name, dtype='qweight')
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f.write('#endif /*DOT_PROD*/\n')
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printVector(f, idx, name + '_idx', dtype='int')
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return AQ
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def dump_sparse_gru(self, f, hf):
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name = 'sparse_' + self.name
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print("printing layer " + name + " of type sparse " + self.__class__.__name__)
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weights = self.get_weights()
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qweights = printSparseVector(f, weights[1], name + '_recurrent_weights')
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printVector(f, weights[-1], name + '_bias')
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subias = weights[-1].copy()
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subias[1,:] = subias[1,:] - np.sum(qweights*(1./128),axis=0)
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printVector(f, subias, name + '_subias')
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if hasattr(self, 'activation'):
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activation = self.activation.__name__.upper()
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else:
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activation = 'TANH'
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if hasattr(self, 'reset_after') and not self.reset_after:
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reset_after = 0
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else:
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reset_after = 1
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neurons = weights[0].shape[1]//3
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max_rnn_neurons = neurons
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f.write('const SparseGRULayer {} = {{\n {}_bias,\n {}_subias,\n {}_recurrent_weights_diag,\n {}_recurrent_weights,\n {}_recurrent_weights_idx,\n {}, ACTIVATION_{}, {}\n}};\n\n'
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.format(name, name, name, name, name, name, weights[0].shape[1]//3, activation, reset_after))
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hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
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hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
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hf.write('extern const SparseGRULayer {};\n\n'.format(name));
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return max_rnn_neurons
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def dump_gru_layer(self, f, hf, dotp=False, sparse=False):
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name = self.name
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print("printing layer " + name + " of type " + self.__class__.__name__)
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weights = self.get_weights()
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if sparse:
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qweight = printSparseVector(f, weights[0], name + '_weights', have_diag=False)
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else:
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qweight = printVector(f, weights[0], name + '_weights')
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if dotp:
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f.write('#ifdef DOT_PROD\n')
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qweight2 = np.clip(np.round(128.*weights[1]).astype('int'), -128, 127)
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printVector(f, qweight2, name + '_recurrent_weights', dotp=True, dtype='qweight')
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f.write('#else /*DOT_PROD*/\n')
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else:
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qweight2 = weights[1]
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printVector(f, weights[1], name + '_recurrent_weights')
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if dotp:
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f.write('#endif /*DOT_PROD*/\n')
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printVector(f, weights[-1], name + '_bias')
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subias = weights[-1].copy()
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subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0)
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subias[1,:] = subias[1,:] - np.sum(qweight2*(1./128.),axis=0)
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printVector(f, subias, name + '_subias')
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if hasattr(self, 'activation'):
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activation = self.activation.__name__.upper()
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else:
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activation = 'TANH'
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if hasattr(self, 'reset_after') and not self.reset_after:
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reset_after = 0
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else:
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reset_after = 1
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neurons = weights[0].shape[1]//3
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max_rnn_neurons = neurons
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f.write('const GRULayer {} = {{\n {}_bias,\n {}_subias,\n {}_weights,\n {},\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}, {}\n}};\n\n'
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.format(name, name, name, name, name + "_weights_idx" if sparse else "NULL", name, weights[0].shape[0], weights[0].shape[1]//3, activation, reset_after))
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hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
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hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
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hf.write('extern const GRULayer {};\n\n'.format(name));
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return max_rnn_neurons
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def dump_dense_layer_impl(name, weights, bias, activation, f, hf):
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printVector(f, weights, name + '_weights')
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printVector(f, bias, name + '_bias')
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f.write('const DenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n'
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.format(name, name, name, weights.shape[0], weights.shape[1], activation))
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hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1]))
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hf.write('extern const DenseLayer {};\n\n'.format(name));
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def dump_dense_layer(self, f, hf):
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name = self.name
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print("printing layer " + name + " of type " + self.__class__.__name__)
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weights = self.get_weights()
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activation = self.activation.__name__.upper()
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dump_dense_layer_impl(name, weights[0], weights[1], activation, f, hf)
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return False
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def dump_conv1d_layer(self, f, hf):
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name = self.name
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print("printing layer " + name + " of type " + self.__class__.__name__)
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weights = self.get_weights()
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printVector(f, weights[0], name + '_weights')
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printVector(f, weights[-1], name + '_bias')
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activation = self.activation.__name__.upper()
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max_conv_inputs = weights[0].shape[1]*weights[0].shape[0]
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f.write('const Conv1DLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, {}, ACTIVATION_{}\n}};\n\n'
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.format(name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))
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hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[2]))
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hf.write('#define {}_STATE_SIZE ({}*{})\n'.format(name.upper(), weights[0].shape[1], (weights[0].shape[0]-1)))
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hf.write('#define {}_DELAY {}\n'.format(name.upper(), (weights[0].shape[0]-1)//2))
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hf.write('extern const Conv1DLayer {};\n\n'.format(name));
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return max_conv_inputs
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