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convert_music_spectrogram_to_diffusers.py
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convert_music_spectrogram_to_diffusers.py
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#!/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.Tensor(weights["token_embedder"]["embedding"]))
model.position_encoding.weight = nn.Parameter(torch.Tensor(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.Tensor(ly_weight["pre_attention_layer_norm"]["scale"]))
attention_weights = ly_weight["attention"]
lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T))
lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T))
lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T))
lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T))
lyr.layer[1].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wo"]["kernel"].T))
model.layer_norm.weight = nn.Parameter(torch.Tensor(weights["encoder_norm"]["scale"]))
return model
def load_continuous_encoder(weights, model):
model.input_proj.weight = nn.Parameter(torch.Tensor(weights["input_proj"]["kernel"].T))
model.position_encoding.weight = nn.Parameter(torch.Tensor(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.Tensor(attention_weights["query"]["kernel"].T))
lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T))
lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T))
lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T))
lyr.layer[0].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_attention_layer_norm"]["scale"]))
lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wo"]["kernel"].T))
lyr.layer[1].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
model.layer_norm.weight = nn.Parameter(torch.Tensor(weights["encoder_norm"]["scale"]))
return model
def load_decoder(weights, model):
model.conditioning_emb[0].weight = nn.Parameter(torch.Tensor(weights["time_emb_dense0"]["kernel"].T))
model.conditioning_emb[2].weight = nn.Parameter(torch.Tensor(weights["time_emb_dense1"]["kernel"].T))
model.position_encoding.weight = nn.Parameter(torch.Tensor(weights["Embed_0"]["embedding"]), requires_grad=False)
model.continuous_inputs_projection.weight = nn.Parameter(
torch.Tensor(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.Tensor(ly_weight["pre_self_attention_layer_norm"]["scale"])
)
lyr.layer[0].FiLMLayer.scale_bias.weight = nn.Parameter(
torch.Tensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T)
)
attention_weights = ly_weight["self_attention"]
lyr.layer[0].attention.to_q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T))
lyr.layer[0].attention.to_k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T))
lyr.layer[0].attention.to_v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T))
lyr.layer[0].attention.to_out[0].weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T))
attention_weights = ly_weight["MultiHeadDotProductAttention_0"]
lyr.layer[1].attention.to_q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T))
lyr.layer[1].attention.to_k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T))
lyr.layer[1].attention.to_v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T))
lyr.layer[1].attention.to_out[0].weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T))
lyr.layer[1].layer_norm.weight = nn.Parameter(
torch.Tensor(ly_weight["pre_cross_attention_layer_norm"]["scale"])
)
lyr.layer[2].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
lyr.layer[2].film.scale_bias.weight = nn.Parameter(
torch.Tensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T)
)
lyr.layer[2].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
lyr.layer[2].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
lyr.layer[2].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wo"]["kernel"].T))
model.decoder_norm.weight = nn.Parameter(torch.Tensor(weights["decoder_norm"]["scale"]))
model.spec_out.weight = nn.Parameter(torch.Tensor(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)