""" /* Copyright (c) 2023 Amazon Written by Jan Buethe */ /* 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 COPYRIGHT OWNER 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 os import tensorflow as tf import numpy as np from wexchange.c_export import CWriter, print_gru_layer, print_dense_layer, print_conv1d_layer def dump_tf_gru_weights(where, gru, name='gru', input_sparse=False, recurrent_sparse=False, quantize=False, scale=1/128, recurrent_scale=1/128): assert gru.activation == tf.keras.activations.tanh assert gru.recurrent_activation == tf.keras.activations.sigmoid assert gru.reset_after == True w_ih = gru.weights[0].numpy().transpose().copy() w_hh = gru.weights[1].numpy().transpose().copy() b_ih = gru.weights[2].numpy()[0].copy() b_hh = gru.weights[2].numpy()[1].copy() if isinstance(where, CWriter): return print_gru_layer(where, name, w_ih, w_hh, b_ih, b_hh, format='tf', input_sparse=input_sparse, recurrent_sparse=recurrent_sparse, quantize=quantize, scale=scale, recurrent_scale=recurrent_scale) else: os.makedirs(where, exist_ok=True) # zrn => rzn N = w_ih.shape[0] // 3 for x in [w_ih, w_hh, b_ih, b_hh]: tmp = x[0:N].copy() x[0:N] = x[N:2*N] x[N:2*N] = tmp np.save(os.path.join(where, 'weight_ih_rzn.npy'), w_ih) np.save(os.path.join(where, 'weight_hh_rzn.npy'), w_hh) np.save(os.path.join(where, 'bias_ih_rzn.npy'), b_ih) np.save(os.path.join(where, 'bias_hh_rzn.npy'), b_hh) def load_tf_gru_weights(path, gru): assert gru.activation == tf.keras.activations.tanh assert gru.recurrent_activation == tf.keras.activations.sigmoid assert gru.reset_after == True w_ih = np.load(os.path.join(path, 'weight_ih_rzn.npy')) w_hh = np.load(os.path.join(path, 'weight_hh_rzn.npy')) b_ih = np.load(os.path.join(path, 'bias_ih_rzn.npy')) b_hh = np.load(os.path.join(path, 'bias_hh_rzn.npy')) # rzn => zrn N = w_ih.shape[0] // 3 for x in [w_ih, w_hh, b_ih, b_hh]: tmp = x[0:N].copy() x[0:N] = x[N:2*N] x[N:2*N] = tmp gru.weights[0].assign(tf.convert_to_tensor(w_ih.transpose())) gru.weights[1].assign(tf.convert_to_tensor(w_hh.transpose())) gru.weights[2].assign(tf.convert_to_tensor(np.vstack((b_ih, b_hh)))) def dump_tf_dense_weights(where, dense, name='dense', scale=1/128, sparse=False, diagonal=False, quantize=False): w = dense.weights[0].numpy() if dense.bias is None: b = np.zeros(dense.units, dtype=w.dtype) else: b = dense.bias.numpy() if isinstance(where, CWriter): return print_dense_layer(where, name, w, b, scale=scale, format='tf', sparse=sparse, diagonal=diagonal, quantize=quantize) else: os.makedirs(where, exist_ok=True) np.save(os.path.join(where, 'weight.npy'), w.transpose()) np.save(os.path.join(where, 'bias.npy'), b) def load_tf_dense_weights(path, dense): w = np.load(os.path.join(path, 'weight.npy')).transpose() b = np.load(os.path.join(path, 'bias.npy')) dense.weights[0].assign(tf.convert_to_tensor(w)) if dense.bias is not None: dense.weights[1].assign(tf.convert_to_tensor(b)) def dump_tf_conv1d_weights(where, conv, name='conv', scale=1/128, quantize=False): assert conv.data_format == 'channels_last' w = conv.weights[0].numpy().copy() if conv.bias is None: b = np.zeros(conv.filters, dtype=w.dtype) else: b = conv.bias.numpy() if isinstance(where, CWriter): return print_conv1d_layer(where, name, w, b, scale=scale, format='tf', quantize=quantize) else: os.makedirs(where, exist_ok=True) w = np.transpose(w, (2, 1, 0)) np.save(os.path.join(where, 'weight_oik.npy'), w) np.save(os.path.join(where, 'bias.npy'), b) def load_tf_conv1d_weights(path, conv): w = np.load(os.path.join(path, 'weight_oik.npy')) b = np.load(os.path.join(path, 'bias.npy')) w = np.transpose(w, (2, 1, 0)) conv.weights[0].assign(tf.convert_to_tensor(w)) if conv.bias is not None: conv.weights[1].assign(tf.convert_to_tensor(b)) def dump_tf_embedding_weights(path, emb): os.makedirs(path, exist_ok=True) w = emb.weights[0].numpy() np.save(os.path.join(path, 'weight.npy'), w) def load_tf_embedding_weights(path, emb): w = np.load(os.path.join(path, 'weight.npy')) emb.weights[0].assign(tf.convert_to_tensor(w)) def dump_tf_weights(path, module): if isinstance(module, tf.keras.layers.Dense): dump_tf_dense_weights(path, module) elif isinstance(module, tf.keras.layers.GRU): dump_tf_gru_weights(path, module) elif isinstance(module, tf.keras.layers.Conv1D): dump_tf_conv1d_weights(path, module) elif isinstance(module, tf.keras.layers.Embedding): dump_tf_embedding_weights(path, module) else: raise ValueError(f'dump_tf_weights: layer of type {type(module)} not supported') def load_tf_weights(path, module): if isinstance(module, tf.keras.layers.Dense): load_tf_dense_weights(path, module) elif isinstance(module, tf.keras.layers.GRU): load_tf_gru_weights(path, module) elif isinstance(module, tf.keras.layers.Conv1D): load_tf_conv1d_weights(path, module) elif isinstance(module, tf.keras.layers.Embedding): load_tf_embedding_weights(path, module) else: raise ValueError(f'dump_tf_weights: layer of type {type(module)} not supported')