""" /* 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 print_xml(xmlout, val, param, anchor, name): xmlout.write( f""" {param} values for {name} """) for k in range(val.shape[1]): xmlout.write(f" ") for j in range(val.shape[0]): xmlout.write(f"") xmlout.write("\n") xmlout.write( f"""
kQ0Q1Q2Q3Q4Q5Q6Q7Q8Q9Q10Q11Q12Q13Q14Q15
{k}{val[j][k]}
""") def dump_statistical_model(writer, w, name, xmlout): levels = w.shape[0] print("printing statistical model") quant_scales = torch.nn.functional.softplus(w[:, 0, :]).numpy() dead_zone = 0.05 * torch.nn.functional.softplus(w[:, 1, :]).numpy() r = torch.sigmoid(w[:, 5 , :]).numpy() p0 = torch.sigmoid(w[:, 4 , :]).numpy() p0 = 1 - r ** (0.5 + 0.5 * p0) scales_norm = 255./256./(1e-15+np.max(quant_scales,axis=0)) quant_scales = quant_scales*scales_norm quant_scales_q8 = np.round(quant_scales * 2**8).astype(np.uint16) dead_zone_q8 = np.clip(np.round(dead_zone * 2**8), 0, 255).astype(np.uint16) r_q8 = np.clip(np.round(r * 2**8), 0, 255).astype(np.uint8) p0_q8 = np.clip(np.round(p0 * 2**8), 0, 255).astype(np.uint16) mask = (np.max(r_q8,axis=0) > 0) * (np.min(p0_q8,axis=0) < 255) quant_scales_q8 = quant_scales_q8[:, mask] dead_zone_q8 = dead_zone_q8[:, mask] r_q8 = r_q8[:, mask] p0_q8 = p0_q8[:, mask] N = r_q8.shape[-1] print_vector(writer.source, quant_scales_q8, f'dred_{name}_quant_scales_q8', dtype='opus_uint8', static=False) print_vector(writer.source, dead_zone_q8, f'dred_{name}_dead_zone_q8', dtype='opus_uint8', static=False) print_vector(writer.source, r_q8, f'dred_{name}_r_q8', dtype='opus_uint8', static=False) print_vector(writer.source, p0_q8, f'dred_{name}_p0_q8', dtype='opus_uint8', static=False) print_xml(xmlout, quant_scales_q8, "Scale", "scale", name) print_xml(xmlout, dead_zone_q8, "Dead zone", "deadzone", name) print_xml(xmlout, r_q8, "Decay (r)", "decay", name) print_xml(xmlout, p0_q8, "P(0)", "p0", name) writer.header.write( f""" extern const opus_uint8 dred_{name}_quant_scales_q8[{levels * N}]; extern const opus_uint8 dred_{name}_dead_zone_q8[{levels * N}]; extern const opus_uint8 dred_{name}_r_q8[{levels * N}]; extern const opus_uint8 dred_{name}_p0_q8[{levels * N}]; """ ) return N, mask, torch.tensor(scales_norm[mask]) 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) xmlout = open("stats.xml", "w") # 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" """ ) latent_out = model.get_submodule('core_encoder.module.z_dense') state_out = model.get_submodule('core_encoder.module.state_dense_2') orig_latent_dim = latent_out.weight.shape[0] orig_state_dim = state_out.weight.shape[0] # statistical model qembedding = model.statistical_model.quant_embedding.weight.detach() levels = qembedding.shape[0] qembedding = torch.reshape(qembedding, (levels, 6, -1)) latent_dim, latent_mask, latent_scale = dump_statistical_model(stats_writer, qembedding[:, :, :orig_latent_dim], 'latent', xmlout) state_dim, state_mask, state_scale = dump_statistical_model(stats_writer, qembedding[:, :, orig_latent_dim:], 'state', xmlout) padded_latent_dim = (latent_dim+7)//8*8 latent_pad = padded_latent_dim - latent_dim; w = latent_out.weight[latent_mask,:] w = w/latent_scale[:, None] w = torch.cat([w, torch.zeros(latent_pad, w.shape[1])], dim=0) b = latent_out.bias[latent_mask] b = b/latent_scale b = torch.cat([b, torch.zeros(latent_pad)], dim=0) latent_out.weight = torch.nn.Parameter(w) latent_out.bias = torch.nn.Parameter(b) padded_state_dim = (state_dim+7)//8*8 state_pad = padded_state_dim - state_dim; w = state_out.weight[state_mask,:] w = w/state_scale[:, None] w = torch.cat([w, torch.zeros(state_pad, w.shape[1])], dim=0) b = state_out.bias[state_mask] b = b/state_scale b = torch.cat([b, torch.zeros(state_pad)], dim=0) state_out.weight = torch.nn.Parameter(w) state_out.bias = torch.nn.Parameter(b) latent_in = model.get_submodule('core_decoder.module.dense_1') state_in = model.get_submodule('core_decoder.module.hidden_init') latent_in.weight = torch.nn.Parameter(latent_in.weight[:,latent_mask]*latent_scale) state_in.weight = torch.nn.Parameter(state_in.weight[:,state_mask]*state_scale) # encoder encoder_dense_layers = [ ('core_encoder.module.dense_1' , 'enc_dense1', 'TANH', False,), ('core_encoder.module.z_dense' , 'enc_zdense', 'LINEAR', True,), ('core_encoder.module.state_dense_1' , 'gdense1' , 'TANH', True,), ('core_encoder.module.state_dense_2' , 'gdense2' , 'TANH', True) ] for name, export_name, _, quantize in encoder_dense_layers: layer = model.get_submodule(name) dump_torch_weights(enc_writer, layer, name=export_name, verbose=True, quantize=quantize, scale=None) encoder_gru_layers = [ ('core_encoder.module.gru1' , 'enc_gru1', 'TANH', True), ('core_encoder.module.gru2' , 'enc_gru2', 'TANH', True), ('core_encoder.module.gru3' , 'enc_gru3', 'TANH', True), ('core_encoder.module.gru4' , 'enc_gru4', 'TANH', True), ('core_encoder.module.gru5' , 'enc_gru5', 'TANH', True), ] enc_max_rnn_units = max([dump_torch_weights(enc_writer, model.get_submodule(name), export_name, verbose=True, input_sparse=True, quantize=quantize, scale=None, recurrent_scale=None) for name, export_name, _, quantize in encoder_gru_layers]) encoder_conv_layers = [ ('core_encoder.module.conv1.conv' , 'enc_conv1', 'TANH', True), ('core_encoder.module.conv2.conv' , 'enc_conv2', 'TANH', True), ('core_encoder.module.conv3.conv' , 'enc_conv3', 'TANH', True), ('core_encoder.module.conv4.conv' , 'enc_conv4', 'TANH', True), ('core_encoder.module.conv5.conv' , 'enc_conv5', 'TANH', True), ] enc_max_conv_inputs = max([dump_torch_weights(enc_writer, model.get_submodule(name), export_name, verbose=True, quantize=quantize, scale=None) for name, export_name, _, quantize in encoder_conv_layers]) del enc_writer # decoder decoder_dense_layers = [ ('core_decoder.module.dense_1' , 'dec_dense1', 'TANH', False), ('core_decoder.module.glu1.gate' , 'dec_glu1', 'TANH', True), ('core_decoder.module.glu2.gate' , 'dec_glu2', 'TANH', True), ('core_decoder.module.glu3.gate' , 'dec_glu3', 'TANH', True), ('core_decoder.module.glu4.gate' , 'dec_glu4', 'TANH', True), ('core_decoder.module.glu5.gate' , 'dec_glu5', 'TANH', True), ('core_decoder.module.output' , 'dec_output', 'LINEAR', True), ('core_decoder.module.hidden_init' , 'dec_hidden_init', 'TANH', False), ('core_decoder.module.gru_init' , 'dec_gru_init','TANH', True), ] for name, export_name, _, quantize in decoder_dense_layers: layer = model.get_submodule(name) dump_torch_weights(dec_writer, layer, name=export_name, verbose=True, quantize=quantize, scale=None) decoder_gru_layers = [ ('core_decoder.module.gru1' , 'dec_gru1', 'TANH', True), ('core_decoder.module.gru2' , 'dec_gru2', 'TANH', True), ('core_decoder.module.gru3' , 'dec_gru3', 'TANH', True), ('core_decoder.module.gru4' , 'dec_gru4', 'TANH', True), ('core_decoder.module.gru5' , 'dec_gru5', 'TANH', True), ] dec_max_rnn_units = max([dump_torch_weights(dec_writer, model.get_submodule(name), export_name, verbose=True, input_sparse=True, quantize=quantize, scale=None, recurrent_scale=None) for name, export_name, _, quantize in decoder_gru_layers]) decoder_conv_layers = [ ('core_decoder.module.conv1.conv' , 'dec_conv1', 'TANH', True), ('core_decoder.module.conv2.conv' , 'dec_conv2', 'TANH', True), ('core_decoder.module.conv3.conv' , 'dec_conv3', 'TANH', True), ('core_decoder.module.conv4.conv' , 'dec_conv4', 'TANH', True), ('core_decoder.module.conv5.conv' , 'dec_conv5', 'TANH', True), ] dec_max_conv_inputs = max([dump_torch_weights(dec_writer, model.get_submodule(name), export_name, verbose=True, quantize=quantize, scale=None) for name, export_name, _, quantize in decoder_conv_layers]) del dec_writer del stats_writer # constants constants_writer.header.write( f""" #define DRED_NUM_FEATURES {model.feature_dim} #define DRED_LATENT_DIM {latent_dim} #define DRED_STATE_DIM {state_dim} #define DRED_PADDED_LATENT_DIM {padded_latent_dim} #define DRED_PADDED_STATE_DIM {padded_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) def _remove_weight_norm(m): try: torch.nn.utils.remove_weight_norm(m) except ValueError: # this module didn't have weight norm return model.apply(_remove_weight_norm) 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}')