1""" 2/* Copyright (c) 2022 Amazon 3 Written by Jan Buethe */ 4/* 5 Redistribution and use in source and binary forms, with or without 6 modification, are permitted provided that the following conditions 7 are met: 8 9 - Redistributions of source code must retain the above copyright 10 notice, this list of conditions and the following disclaimer. 11 12 - Redistributions in binary form must reproduce the above copyright 13 notice, this list of conditions and the following disclaimer in the 14 documentation and/or other materials provided with the distribution. 15 16 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 17 ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 18 LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 19 A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER 20 OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 21 EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 22 PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 23 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 24 LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 25 NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 26 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27*/ 28""" 29 30import os 31import argparse 32import sys 33 34sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange')) 35 36 37parser = argparse.ArgumentParser() 38 39parser.add_argument('checkpoint', type=str, help='model checkpoint') 40parser.add_argument('output_dir', type=str, help='output folder') 41 42args = parser.parse_args() 43 44import torch 45import numpy as np 46 47import lossgen 48from wexchange.torch import dump_torch_weights 49from wexchange.c_export import CWriter, print_vector 50 51def c_export(args, model): 52 53 message = f"Auto generated from checkpoint {os.path.basename(args.checkpoint)}" 54 55 writer = CWriter(os.path.join(args.output_dir, "lossgen_data"), message=message, model_struct_name='LossGen') 56 writer.header.write( 57f""" 58#include "opus_types.h" 59""" 60 ) 61 62 dense_layers = [ 63 ('dense_in', "lossgen_dense_in"), 64 ('dense_out', "lossgen_dense_out") 65 ] 66 67 68 for name, export_name in dense_layers: 69 layer = model.get_submodule(name) 70 dump_torch_weights(writer, layer, name=export_name, verbose=True, quantize=False, scale=None) 71 72 73 gru_layers = [ 74 ("gru1", "lossgen_gru1"), 75 ("gru2", "lossgen_gru2"), 76 ] 77 78 max_rnn_units = max([dump_torch_weights(writer, model.get_submodule(name), export_name, verbose=True, input_sparse=False, quantize=True, scale=None, recurrent_scale=None) 79 for name, export_name in gru_layers]) 80 81 writer.header.write( 82f""" 83 84#define LOSSGEN_MAX_RNN_UNITS {max_rnn_units} 85 86""" 87 ) 88 89 writer.close() 90 91 92if __name__ == "__main__": 93 94 os.makedirs(args.output_dir, exist_ok=True) 95 checkpoint = torch.load(args.checkpoint, map_location='cpu') 96 model = lossgen.LossGen(*checkpoint['model_args'], **checkpoint['model_kwargs']) 97 model.load_state_dict(checkpoint['state_dict'], strict=False) 98 #model = LossGen() 99 #checkpoint = torch.load(args.checkpoint, map_location='cpu') 100 #model.load_state_dict(checkpoint['state_dict']) 101 c_export(args, model) 102