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97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
"""
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/* Copyright (c) 2023 Amazon
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Written by Jan Buethe */
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/*
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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 COPYRIGHT OWNER
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OR 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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"""
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import os
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import argparse
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import sys
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import hashlib
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sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange'))
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import torch
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import wexchange.torch
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from wexchange.torch import dump_torch_weights
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from models import model_dict
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parser = argparse.ArgumentParser()
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parser.add_argument('checkpoint', type=str, help='LACE or NoLACE model checkpoint')
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parser.add_argument('output_dir', type=str, help='output folder')
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# auxiliary functions
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def sha1(filename):
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BUF_SIZE = 65536
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sha1 = hashlib.sha1()
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with open(filename, 'rb') as f:
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while True:
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data = f.read(BUF_SIZE)
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if not data:
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break
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sha1.update(data)
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return sha1.hexdigest()
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def export_name(name):
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return name.replace('.', '_')
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if __name__ == "__main__":
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args = parser.parse_args()
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checkpoint_path = args.checkpoint
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outdir = args.output_dir
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os.makedirs(outdir, exist_ok=True)
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# dump message
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message = f"Auto generated from checkpoint {os.path.basename(checkpoint_path)} (sha1: {sha1(checkpoint_path)})"
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# create model and load weights
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checkpoint = torch.load(checkpoint_path, map_location='cpu')
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model = model_dict[checkpoint['setup']['model']['name']](*checkpoint['setup']['model']['args'], **checkpoint['setup']['model']['kwargs'])
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# CWriter
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model_name = checkpoint['setup']['model']['name']
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cwriter = wexchange.c_export.CWriter(os.path.join(outdir, model_name + "_data"), message=message, model_struct_name=model_name.upper())
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# dump numbits_embedding parameters by hand
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numbits_embedding = model.get_submodule('numbits_embedding')
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weights = next(iter(numbits_embedding.parameters()))
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for i, c in enumerate(weights):
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cwriter.header.write(f"\nNUMBITS_COEF_{i} {float(c.detach())}f")
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cwriter.header.write("\n\n")
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# dump layers
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for name, module in model.named_modules():
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if isinstance(module, torch.nn.Linear) or isinstance(module, torch.nn.Conv1d) \
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or isinstance(module, torch.nn.ConvTranspose1d) or isinstance(module, torch.nn.Embedding):
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dump_torch_weights(cwriter, module, name=export_name(name), verbose=True)
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cwriter.close()
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