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"""
/* 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()
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