File size: 10,565 Bytes
f1069cc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
#!/usr/bin/env python3
import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from t5x import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, T5FilmDecoder


MODEL = "base_with_context"


def load_notes_encoder(weights, model):
    model.token_embedder.weight = nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]))
    model.position_encoding.weight = nn.Parameter(
        torch.FloatTensor(weights["Embed_0"]["embedding"]), requires_grad=False
    )
    for lyr_num, lyr in enumerate(model.encoders):
        ly_weight = weights[f"layers_{lyr_num}"]
        lyr.layer[0].layer_norm.weight = nn.Parameter(
            torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"])
        )

        attention_weights = ly_weight["attention"]
        lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
        lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
        lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
        lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))

        lyr.layer[1].layer_norm.weight = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))

        lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
        lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
        lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))

    model.layer_norm.weight = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]))
    return model


def load_continuous_encoder(weights, model):
    model.input_proj.weight = nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T))

    model.position_encoding.weight = nn.Parameter(
        torch.FloatTensor(weights["Embed_0"]["embedding"]), requires_grad=False
    )

    for lyr_num, lyr in enumerate(model.encoders):
        ly_weight = weights[f"layers_{lyr_num}"]
        attention_weights = ly_weight["attention"]

        lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
        lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
        lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
        lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
        lyr.layer[0].layer_norm.weight = nn.Parameter(
            torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"])
        )

        lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
        lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
        lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))
        lyr.layer[1].layer_norm.weight = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))

    model.layer_norm.weight = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]))

    return model


def load_decoder(weights, model):
    model.conditioning_emb[0].weight = nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T))
    model.conditioning_emb[2].weight = nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T))

    model.position_encoding.weight = nn.Parameter(
        torch.FloatTensor(weights["Embed_0"]["embedding"]), requires_grad=False
    )

    model.continuous_inputs_projection.weight = nn.Parameter(
        torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T)
    )

    for lyr_num, lyr in enumerate(model.decoders):
        ly_weight = weights[f"layers_{lyr_num}"]
        lyr.layer[0].layer_norm.weight = nn.Parameter(
            torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"])
        )

        lyr.layer[0].FiLMLayer.scale_bias.weight = nn.Parameter(
            torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T)
        )

        attention_weights = ly_weight["self_attention"]
        lyr.layer[0].attention.to_q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
        lyr.layer[0].attention.to_k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
        lyr.layer[0].attention.to_v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
        lyr.layer[0].attention.to_out[0].weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))

        attention_weights = ly_weight["MultiHeadDotProductAttention_0"]
        lyr.layer[1].attention.to_q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
        lyr.layer[1].attention.to_k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
        lyr.layer[1].attention.to_v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
        lyr.layer[1].attention.to_out[0].weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
        lyr.layer[1].layer_norm.weight = nn.Parameter(
            torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"])
        )

        lyr.layer[2].layer_norm.weight = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
        lyr.layer[2].film.scale_bias.weight = nn.Parameter(
            torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T)
        )
        lyr.layer[2].DenseReluDense.wi_0.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
        lyr.layer[2].DenseReluDense.wi_1.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
        lyr.layer[2].DenseReluDense.wo.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))

    model.decoder_norm.weight = nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]))

    model.spec_out.weight = nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T))

    return model


def main(args):
    t5_checkpoint = checkpoints.load_t5x_checkpoint(args.checkpoint_path)
    t5_checkpoint = jnp.tree_util.tree_map(onp.array, t5_checkpoint)

    gin_overrides = [
        "from __gin__ import dynamic_registration",
        "from music_spectrogram_diffusion.models.diffusion import diffusion_utils",
        "diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0",
        "diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()",
    ]

    gin_file = os.path.join(args.checkpoint_path, "..", "config.gin")
    gin_config = inference.parse_training_gin_file(gin_file, gin_overrides)
    synth_model = inference.InferenceModel(args.checkpoint_path, gin_config)

    scheduler = DDPMScheduler(beta_schedule="squaredcos_cap_v2", variance_type="fixed_large")

    notes_encoder = SpectrogramNotesEncoder(
        max_length=synth_model.sequence_length["inputs"],
        vocab_size=synth_model.model.module.config.vocab_size,
        d_model=synth_model.model.module.config.emb_dim,
        dropout_rate=synth_model.model.module.config.dropout_rate,
        num_layers=synth_model.model.module.config.num_encoder_layers,
        num_heads=synth_model.model.module.config.num_heads,
        d_kv=synth_model.model.module.config.head_dim,
        d_ff=synth_model.model.module.config.mlp_dim,
        feed_forward_proj="gated-gelu",
    )

    continuous_encoder = SpectrogramContEncoder(
        input_dims=synth_model.audio_codec.n_dims,
        targets_context_length=synth_model.sequence_length["targets_context"],
        d_model=synth_model.model.module.config.emb_dim,
        dropout_rate=synth_model.model.module.config.dropout_rate,
        num_layers=synth_model.model.module.config.num_encoder_layers,
        num_heads=synth_model.model.module.config.num_heads,
        d_kv=synth_model.model.module.config.head_dim,
        d_ff=synth_model.model.module.config.mlp_dim,
        feed_forward_proj="gated-gelu",
    )

    decoder = T5FilmDecoder(
        input_dims=synth_model.audio_codec.n_dims,
        targets_length=synth_model.sequence_length["targets_context"],
        max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time,
        d_model=synth_model.model.module.config.emb_dim,
        num_layers=synth_model.model.module.config.num_decoder_layers,
        num_heads=synth_model.model.module.config.num_heads,
        d_kv=synth_model.model.module.config.head_dim,
        d_ff=synth_model.model.module.config.mlp_dim,
        dropout_rate=synth_model.model.module.config.dropout_rate,
    )

    notes_encoder = load_notes_encoder(t5_checkpoint["target"]["token_encoder"], notes_encoder)
    continuous_encoder = load_continuous_encoder(t5_checkpoint["target"]["continuous_encoder"], continuous_encoder)
    decoder = load_decoder(t5_checkpoint["target"]["decoder"], decoder)

    melgan = OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder")

    pipe = SpectrogramDiffusionPipeline(
        notes_encoder=notes_encoder,
        continuous_encoder=continuous_encoder,
        decoder=decoder,
        scheduler=scheduler,
        melgan=melgan,
    )
    if args.save:
        pipe.save_pretrained(args.output_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument("--output_path", default=None, type=str, required=True, help="Path to the converted model.")
    parser.add_argument(
        "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
    )
    parser.add_argument(
        "--checkpoint_path",
        default=f"{MODEL}/checkpoint_500000",
        type=str,
        required=False,
        help="Path to the original jax model checkpoint.",
    )
    args = parser.parse_args()

    main(args)