ref: 60f151b87d4a2a4e14eff2c7208c96f8618e6f22
dir: /dnn/torch/rdovae/export_rdovae_weights.py/
"""
/* Copyright (c) 2022 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
sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange'))
parser = argparse.ArgumentParser()
parser.add_argument('checkpoint', type=str, help='rdovae model checkpoint')
parser.add_argument('output_dir', type=str, help='output folder')
parser.add_argument('--format', choices=['C', 'numpy'], help='output format, default: C', default='C')
args = parser.parse_args()
import torch
import numpy as np
from rdovae import RDOVAE
from wexchange.torch import dump_torch_weights
from wexchange.c_export import CWriter, print_vector
def dump_statistical_model(writer, qembedding):
w = qembedding.weight.detach()
levels, dim = w.shape
N = dim // 6
print("printing statistical model")
quant_scales = torch.nn.functional.softplus(w[:, : N]).numpy()
dead_zone = 0.05 * torch.nn.functional.softplus(w[:, N : 2 * N]).numpy()
r = torch.sigmoid(w[:, 5 * N : 6 * N]).numpy()
p0 = torch.sigmoid(w[:, 4 * N : 5 * N]).numpy()
p0 = 1 - r ** (0.5 + 0.5 * p0)
quant_scales_q8 = np.round(quant_scales * 2**8).astype(np.uint16)
dead_zone_q10 = np.round(dead_zone * 2**10).astype(np.uint16)
r_q15 = np.round(r * 2**15).astype(np.uint16)
p0_q15 = np.round(p0 * 2**15).astype(np.uint16)
print_vector(writer.source, quant_scales_q8, 'dred_quant_scales_q8', dtype='opus_uint16', static=False)
print_vector(writer.source, dead_zone_q10, 'dred_dead_zone_q10', dtype='opus_uint16', static=False)
print_vector(writer.source, r_q15, 'dred_r_q15', dtype='opus_uint16', static=False)
print_vector(writer.source, p0_q15, 'dred_p0_q15', dtype='opus_uint16', static=False)
writer.header.write(
f"""
extern const opus_uint16 dred_quant_scales_q8[{levels * N}];
extern const opus_uint16 dred_dead_zone_q10[{levels * N}];
extern const opus_uint16 dred_r_q15[{levels * N}];
extern const opus_uint16 dred_p0_q15[{levels * N}];
"""
)
def c_export(args, model):
message = f"Auto generated from checkpoint {os.path.basename(args.checkpoint)}"
enc_writer = CWriter(os.path.join(args.output_dir, "dred_rdovae_enc_data"), message=message, model_struct_name='RDOVAEEnc')
dec_writer = CWriter(os.path.join(args.output_dir, "dred_rdovae_dec_data"), message=message, model_struct_name='RDOVAEDec')
stats_writer = CWriter(os.path.join(args.output_dir, "dred_rdovae_stats_data"), message=message, enable_binary_blob=False)
constants_writer = CWriter(os.path.join(args.output_dir, "dred_rdovae_constants"), message=message, header_only=True, enable_binary_blob=False)
# some custom includes
for writer in [enc_writer, dec_writer]:
writer.header.write(
f"""
#include "opus_types.h"
#include "dred_rdovae.h"
#include "dred_rdovae_constants.h"
"""
)
stats_writer.header.write(
f"""
#include "opus_types.h"
#include "dred_rdovae_constants.h"
"""
)
# encoder
encoder_dense_layers = [
('core_encoder.module.dense_1' , 'enc_dense1', 'TANH'),
('core_encoder.module.z_dense' , 'enc_zdense', 'LINEAR'),
('core_encoder.module.state_dense_1' , 'gdense1' , 'TANH'),
('core_encoder.module.state_dense_2' , 'gdense2' , 'TANH')
]
for name, export_name, _ in encoder_dense_layers:
layer = model.get_submodule(name)
dump_torch_weights(enc_writer, layer, name=export_name, verbose=True)
encoder_gru_layers = [
('core_encoder.module.gru1' , 'enc_gru1', 'TANH'),
('core_encoder.module.gru2' , 'enc_gru2', 'TANH'),
('core_encoder.module.gru3' , 'enc_gru3', 'TANH'),
('core_encoder.module.gru4' , 'enc_gru4', 'TANH'),
('core_encoder.module.gru5' , 'enc_gru5', 'TANH'),
]
enc_max_rnn_units = max([dump_torch_weights(enc_writer, model.get_submodule(name), export_name, verbose=True, input_sparse=True, quantize=True)
for name, export_name, _ in encoder_gru_layers])
encoder_conv_layers = [
('core_encoder.module.conv1.conv' , 'enc_conv1', 'TANH'),
('core_encoder.module.conv2.conv' , 'enc_conv2', 'TANH'),
('core_encoder.module.conv3.conv' , 'enc_conv3', 'TANH'),
('core_encoder.module.conv4.conv' , 'enc_conv4', 'TANH'),
('core_encoder.module.conv5.conv' , 'enc_conv5', 'TANH'),
]
enc_max_conv_inputs = max([dump_torch_weights(enc_writer, model.get_submodule(name), export_name, verbose=True, quantize=False) for name, export_name, _ in encoder_conv_layers])
del enc_writer
# decoder
decoder_dense_layers = [
('core_decoder.module.dense_1' , 'dec_dense1', 'TANH'),
('core_decoder.module.output' , 'dec_output', 'LINEAR'),
('core_decoder.module.hidden_init' , 'dec_hidden_init', 'TANH'),
('core_decoder.module.gru_init' , 'dec_gru_init', 'TANH'),
]
for name, export_name, _ in decoder_dense_layers:
layer = model.get_submodule(name)
dump_torch_weights(dec_writer, layer, name=export_name, verbose=True)
decoder_gru_layers = [
('core_decoder.module.gru1' , 'dec_gru1', 'TANH'),
('core_decoder.module.gru2' , 'dec_gru2', 'TANH'),
('core_decoder.module.gru3' , 'dec_gru3', 'TANH'),
('core_decoder.module.gru4' , 'dec_gru4', 'TANH'),
('core_decoder.module.gru5' , 'dec_gru5', 'TANH'),
]
dec_max_rnn_units = max([dump_torch_weights(dec_writer, model.get_submodule(name), export_name, verbose=True, input_sparse=True, quantize=True)
for name, export_name, _ in decoder_gru_layers])
decoder_conv_layers = [
('core_decoder.module.conv1.conv' , 'dec_conv1', 'TANH'),
('core_decoder.module.conv2.conv' , 'dec_conv2', 'TANH'),
('core_decoder.module.conv3.conv' , 'dec_conv3', 'TANH'),
('core_decoder.module.conv4.conv' , 'dec_conv4', 'TANH'),
('core_decoder.module.conv5.conv' , 'dec_conv5', 'TANH'),
]
dec_max_conv_inputs = max([dump_torch_weights(dec_writer, model.get_submodule(name), export_name, verbose=True, quantize=False) for name, export_name, _ in decoder_conv_layers])
del dec_writer
# statistical model
qembedding = model.statistical_model.quant_embedding
dump_statistical_model(stats_writer, qembedding)
del stats_writer
# constants
constants_writer.header.write(
f"""
#define DRED_NUM_FEATURES {model.feature_dim}
#define DRED_LATENT_DIM {model.latent_dim}
#define DRED_STATE_DIME {model.state_dim}
#define DRED_NUM_QUANTIZATION_LEVELS {model.quant_levels}
#define DRED_MAX_RNN_NEURONS {max(enc_max_rnn_units, dec_max_rnn_units)}
#define DRED_MAX_CONV_INPUTS {max(enc_max_conv_inputs, dec_max_conv_inputs)}
#define DRED_ENC_MAX_RNN_NEURONS {enc_max_conv_inputs}
#define DRED_ENC_MAX_CONV_INPUTS {enc_max_conv_inputs}
#define DRED_DEC_MAX_RNN_NEURONS {dec_max_rnn_units}
"""
)
del constants_writer
def numpy_export(args, model):
exchange_name_to_name = {
'encoder_stack_layer1_dense' : 'core_encoder.module.dense_1',
'encoder_stack_layer3_dense' : 'core_encoder.module.dense_2',
'encoder_stack_layer5_dense' : 'core_encoder.module.dense_3',
'encoder_stack_layer7_dense' : 'core_encoder.module.dense_4',
'encoder_stack_layer8_dense' : 'core_encoder.module.dense_5',
'encoder_state_layer1_dense' : 'core_encoder.module.state_dense_1',
'encoder_state_layer2_dense' : 'core_encoder.module.state_dense_2',
'encoder_stack_layer2_gru' : 'core_encoder.module.gru_1',
'encoder_stack_layer4_gru' : 'core_encoder.module.gru_2',
'encoder_stack_layer6_gru' : 'core_encoder.module.gru_3',
'encoder_stack_layer9_conv' : 'core_encoder.module.conv1',
'statistical_model_embedding' : 'statistical_model.quant_embedding',
'decoder_state1_dense' : 'core_decoder.module.gru_1_init',
'decoder_state2_dense' : 'core_decoder.module.gru_2_init',
'decoder_state3_dense' : 'core_decoder.module.gru_3_init',
'decoder_stack_layer1_dense' : 'core_decoder.module.dense_1',
'decoder_stack_layer3_dense' : 'core_decoder.module.dense_2',
'decoder_stack_layer5_dense' : 'core_decoder.module.dense_3',
'decoder_stack_layer7_dense' : 'core_decoder.module.dense_4',
'decoder_stack_layer8_dense' : 'core_decoder.module.dense_5',
'decoder_stack_layer9_dense' : 'core_decoder.module.output',
'decoder_stack_layer2_gru' : 'core_decoder.module.gru_1',
'decoder_stack_layer4_gru' : 'core_decoder.module.gru_2',
'decoder_stack_layer6_gru' : 'core_decoder.module.gru_3'
}
name_to_exchange_name = {value : key for key, value in exchange_name_to_name.items()}
for name, exchange_name in name_to_exchange_name.items():
print(f"printing layer {name}...")
dump_torch_weights(os.path.join(args.output_dir, exchange_name), model.get_submodule(name))
if __name__ == "__main__":
os.makedirs(args.output_dir, exist_ok=True)
# load model from checkpoint
checkpoint = torch.load(args.checkpoint, map_location='cpu')
model = RDOVAE(*checkpoint['model_args'], **checkpoint['model_kwargs'])
missing_keys, unmatched_keys = model.load_state_dict(checkpoint['state_dict'], strict=False)
if len(missing_keys) > 0:
raise ValueError(f"error: missing keys in state dict")
if len(unmatched_keys) > 0:
print(f"warning: the following keys were unmatched {unmatched_keys}")
if args.format == 'C':
c_export(args, model)
elif args.format == 'numpy':
numpy_export(args, model)
else:
raise ValueError(f'error: unknown export format {args.format}')