""" /* 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 argparse import sys import hashlib sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange')) import torch import wexchange.torch from wexchange.torch import dump_torch_weights from models import model_dict from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d from utils.layers.td_shaper import TDShaper from wexchange.torch import dump_torch_weights parser = argparse.ArgumentParser() parser.add_argument('checkpoint', type=str, help='LACE or NoLACE model checkpoint') parser.add_argument('output_dir', type=str, help='output folder') parser.add_argument('--quantize', action="store_true", help='quantization according to schedule') schedules = { 'nolace': [ ('pitch_embedding', dict()), ('feature_net.conv1', dict()), ('feature_net.conv2', dict(quantize=True, scale=None)), ('feature_net.tconv', dict(quantize=True, scale=None)), ('feature_net.gru', dict()), ('cf1', dict(quantize=True, scale=None)), ('cf2', dict(quantize=True, scale=None)), ('af1', dict(quantize=True, scale=None)), ('tdshape1', dict()), ('tdshape2', dict()), ('tdshape3', dict()), ('af2', dict(quantize=True, scale=None)), ('af3', dict(quantize=True, scale=None)), ('af4', dict(quantize=True, scale=None)), ('post_cf1', dict(quantize=True, scale=None)), ('post_cf2', dict(quantize=True, scale=None)), ('post_af1', dict(quantize=True, scale=None)), ('post_af2', dict(quantize=True, scale=None)), ('post_af3', dict(quantize=True, scale=None)) ], 'lace' : [ ('pitch_embedding', dict()), ('feature_net.conv1', dict()), ('feature_net.conv2', dict(quantize=True, scale=None)), ('feature_net.tconv', dict(quantize=True, scale=None)), ('feature_net.gru', dict()), ('cf1', dict(quantize=True, scale=None)), ('cf2', dict(quantize=True, scale=None)), ('af1', dict(quantize=True, scale=None)) ] } # auxiliary functions def sha1(filename): BUF_SIZE = 65536 sha1 = hashlib.sha1() with open(filename, 'rb') as f: while True: data = f.read(BUF_SIZE) if not data: break sha1.update(data) return sha1.hexdigest() def osce_dump_generic(writer, name, module): if isinstance(module, torch.nn.Linear) or isinstance(module, torch.nn.Conv1d) \ or isinstance(module, torch.nn.ConvTranspose1d) or isinstance(module, torch.nn.Embedding) \ or isinstance(module, LimitedAdaptiveConv1d) or isinstance(module, LimitedAdaptiveComb1d) \ or isinstance(module, TDShaper) or isinstance(module, torch.nn.GRU): dump_torch_weights(writer, module, name=name, verbose=True) else: for child_name, child in module.named_children(): osce_dump_generic(writer, (name + "_" + child_name).replace("feature_net", "fnet"), child) def export_name(name): name = name.replace('.', '_') name = name.replace('feature_net', 'fnet') return name def osce_scheduled_dump(writer, prefix, model, schedule): if not prefix.endswith('_'): prefix += '_' for name, kwargs in schedule: dump_torch_weights(writer, model.get_submodule(name), prefix + export_name(name), **kwargs, verbose=True) if __name__ == "__main__": args = parser.parse_args() checkpoint_path = args.checkpoint outdir = args.output_dir os.makedirs(outdir, exist_ok=True) # dump message message = f"Auto generated from checkpoint {os.path.basename(checkpoint_path)} (sha1: {sha1(checkpoint_path)})" # create model and load weights checkpoint = torch.load(checkpoint_path, map_location='cpu') model = model_dict[checkpoint['setup']['model']['name']](*checkpoint['setup']['model']['args'], **checkpoint['setup']['model']['kwargs']) model.load_state_dict(checkpoint['state_dict']) # CWriter model_name = checkpoint['setup']['model']['name'] cwriter = wexchange.c_export.CWriter(os.path.join(outdir, model_name + "_data"), message=message, model_struct_name=model_name.upper() + 'Layers', add_typedef=True) # Add custom includes and global parameters cwriter.header.write(f''' #define {model_name.upper()}_PREEMPH {model.preemph}f #define {model_name.upper()}_FRAME_SIZE {model.FRAME_SIZE} #define {model_name.upper()}_OVERLAP_SIZE 40 #define {model_name.upper()}_NUM_FEATURES {model.num_features} #define {model_name.upper()}_PITCH_MAX {model.pitch_max} #define {model_name.upper()}_PITCH_EMBEDDING_DIM {model.pitch_embedding_dim} #define {model_name.upper()}_NUMBITS_RANGE_LOW {model.numbits_range[0]} #define {model_name.upper()}_NUMBITS_RANGE_HIGH {model.numbits_range[1]} #define {model_name.upper()}_NUMBITS_EMBEDDING_DIM {model.numbits_embedding_dim} #define {model_name.upper()}_COND_DIM {model.cond_dim} #define {model_name.upper()}_HIDDEN_FEATURE_DIM {model.hidden_feature_dim} ''') for i, s in enumerate(model.numbits_embedding.scale_factors): cwriter.header.write(f"#define {model_name.upper()}_NUMBITS_SCALE_{i} {float(s.detach().cpu())}f\n") # dump layers if model_name in schedules and args.quantize: osce_scheduled_dump(cwriter, model_name, model, schedules[model_name]) else: osce_dump_generic(cwriter, model_name, model) cwriter.close()