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100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
"""
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/* Copyright (c) 2022 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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sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange'))
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parser = argparse.ArgumentParser()
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parser.add_argument('checkpoint', type=str, help='model checkpoint')
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parser.add_argument('output_dir', type=str, help='output folder')
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args = parser.parse_args()
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import torch
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import numpy as np
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import plc
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from wexchange.torch import dump_torch_weights
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from wexchange.c_export import CWriter, print_vector
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def c_export(args, model):
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message = f"Auto generated from checkpoint {os.path.basename(args.checkpoint)}"
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writer = CWriter(os.path.join(args.output_dir, "plc_data"), message=message, model_struct_name='PLCModel')
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writer.header.write(
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f"""
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#include "opus_types.h"
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"""
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)
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dense_layers = [
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('dense_in', "plc_dense_in"),
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('dense_out', "plc_dense_out")
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]
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for name, export_name in dense_layers:
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layer = model.get_submodule(name)
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dump_torch_weights(writer, layer, name=export_name, verbose=True, quantize=False, scale=None)
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gru_layers = [
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("gru1", "plc_gru1"),
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("gru2", "plc_gru2"),
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]
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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)
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for name, export_name in gru_layers])
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writer.header.write(
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f"""
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#define PLC_MAX_RNN_UNITS {max_rnn_units}
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"""
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)
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writer.close()
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if __name__ == "__main__":
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os.makedirs(args.output_dir, exist_ok=True)
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checkpoint = torch.load(args.checkpoint, map_location='cpu')
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model = plc.PLC(*checkpoint['model_args'], **checkpoint['model_kwargs'])
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model.load_state_dict(checkpoint['state_dict'], strict=False)
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#checkpoint = torch.load(args.checkpoint, map_location='cpu')
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#model.load_state_dict(checkpoint['state_dict'])
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c_export(args, model)
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