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import torch
from torch.nn import Parameter
from ..models.factory import create_model_from_config
def create_training_wrapper_from_config(model_config, model):
model_type = model_config.get('model_type', None)
assert model_type is not None, 'model_type must be specified in model config'
training_config = model_config.get('training', None)
assert training_config is not None, 'training config must be specified in model config'
if model_type == 'autoencoder':
from .autoencoders import AutoencoderTrainingWrapper
ema_copy = None
if training_config.get("use_ema", False):
ema_copy = create_model_from_config(model_config)
ema_copy = create_model_from_config(model_config) # I don't know why this needs to be called twice but it broke when I called it once
# Copy each weight to the ema copy
for name, param in model.state_dict().items():
if isinstance(param, Parameter):
# backwards compatibility for serialized parameters
param = param.data
ema_copy.state_dict()[name].copy_(param)
use_ema = training_config.get("use_ema", False)
latent_mask_ratio = training_config.get("latent_mask_ratio", 0.0)
teacher_model = training_config.get("teacher_model", None)
if teacher_model is not None:
teacher_model = create_model_from_config(teacher_model)
teacher_model = teacher_model.eval().requires_grad_(False)
teacher_model_ckpt = training_config.get("teacher_model_ckpt", None)
if teacher_model_ckpt is not None:
teacher_model.load_state_dict(torch.load(teacher_model_ckpt)["state_dict"])
else:
raise ValueError("teacher_model_ckpt must be specified if teacher_model is specified")
return AutoencoderTrainingWrapper(
model,
lr=training_config["learning_rate"],
warmup_steps=training_config.get("warmup_steps", 0),
encoder_freeze_on_warmup=training_config.get("encoder_freeze_on_warmup", False),
sample_rate=model_config["sample_rate"],
loss_config=training_config.get("loss_configs", None),
optimizer_configs=training_config.get("optimizer_configs", None),
use_ema=use_ema,
ema_copy=ema_copy if use_ema else None,
force_input_mono=training_config.get("force_input_mono", False),
latent_mask_ratio=latent_mask_ratio,
teacher_model=teacher_model
)
elif model_type == 'diffusion_uncond':
from .diffusion import DiffusionUncondTrainingWrapper
return DiffusionUncondTrainingWrapper(
model,
lr=training_config["learning_rate"],
pre_encoded=training_config.get("pre_encoded", False),
)
elif model_type == 'diffusion_cond':
print("Creating Diffusion Condition Training Wrapper")
from .diffusion import DiffusionCondTrainingWrapper
return DiffusionCondTrainingWrapper(
model,
lr=training_config.get("learning_rate", None),
mask_padding=training_config.get("mask_padding", False),
mask_padding_dropout=training_config.get("mask_padding_dropout", 0.0),
use_ema = training_config.get("use_ema", True),
log_loss_info=training_config.get("log_loss_info", False),
optimizer_configs=training_config.get("optimizer_configs", None),
pre_encoded=training_config.get("pre_encoded", False),
cfg_dropout_prob = training_config.get("cfg_dropout_prob", 0.1),
timestep_sampler = training_config.get("timestep_sampler", "uniform")
)
elif model_type == 'diffusion_prior':
from .diffusion import DiffusionPriorTrainingWrapper
from ..models.diffusion_prior import PriorType
ema_copy = create_model_from_config(model_config)
# Copy each weight to the ema copy
for name, param in model.state_dict().items():
if isinstance(param, Parameter):
# backwards compatibility for serialized parameters
param = param.data
ema_copy.state_dict()[name].copy_(param)
prior_type = training_config.get("prior_type", "mono_stereo")
if prior_type == "mono_stereo":
prior_type_enum = PriorType.MonoToStereo
else:
raise ValueError(f"Unknown prior type: {prior_type}")
return DiffusionPriorTrainingWrapper(
model,
lr=training_config["learning_rate"],
ema_copy=ema_copy,
prior_type=prior_type_enum,
log_loss_info=training_config.get("log_loss_info", False),
use_reconstruction_loss=training_config.get("use_reconstruction_loss", False),
)
elif model_type == 'diffusion_cond_inpaint':
from .diffusion import DiffusionCondInpaintTrainingWrapper
return DiffusionCondInpaintTrainingWrapper(
model,
lr=training_config.get("learning_rate", None),
max_mask_segments = training_config.get("max_mask_segments", 10),
log_loss_info=training_config.get("log_loss_info", False),
optimizer_configs=training_config.get("optimizer_configs", None),
use_ema=training_config.get("use_ema", True),
pre_encoded=training_config.get("pre_encoded", False),
cfg_dropout_prob = training_config.get("cfg_dropout_prob", 0.1),
timestep_sampler = training_config.get("timestep_sampler", "uniform")
)
elif model_type == 'diffusion_autoencoder':
from .diffusion import DiffusionAutoencoderTrainingWrapper
ema_copy = create_model_from_config(model_config)
# Copy each weight to the ema copy
for name, param in model.state_dict().items():
if isinstance(param, Parameter):
# backwards compatibility for serialized parameters
param = param.data
ema_copy.state_dict()[name].copy_(param)
return DiffusionAutoencoderTrainingWrapper(
model,
ema_copy=ema_copy,
lr=training_config["learning_rate"],
use_reconstruction_loss=training_config.get("use_reconstruction_loss", False)
)
elif model_type == 'lm':
from .lm import AudioLanguageModelTrainingWrapper
ema_copy = create_model_from_config(model_config)
for name, param in model.state_dict().items():
if isinstance(param, Parameter):
# backwards compatibility for serialized parameters
param = param.data
ema_copy.state_dict()[name].copy_(param)
return AudioLanguageModelTrainingWrapper(
model,
ema_copy=ema_copy,
lr=training_config.get("learning_rate", None),
use_ema=training_config.get("use_ema", False),
optimizer_configs=training_config.get("optimizer_configs", None),
pre_encoded=training_config.get("pre_encoded", False),
)
else:
raise NotImplementedError(f'Unknown model type: {model_type}')
def create_demo_callback_from_config(model_config, **kwargs):
model_type = model_config.get('model_type', None)
assert model_type is not None, 'model_type must be specified in model config'
training_config = model_config.get('training', None)
assert training_config is not None, 'training config must be specified in model config'
demo_config = training_config.get("demo", {})
if model_type == 'autoencoder':
from .autoencoders import AutoencoderDemoCallback
return AutoencoderDemoCallback(
demo_every=demo_config.get("demo_every", 2000),
sample_size=model_config["sample_size"],
sample_rate=model_config["sample_rate"],
**kwargs
)
elif model_type == 'diffusion_uncond':
from .diffusion import DiffusionUncondDemoCallback
return DiffusionUncondDemoCallback(
demo_every=demo_config.get("demo_every", 2000),
demo_steps=demo_config.get("demo_steps", 250),
sample_rate=model_config["sample_rate"]
)
elif model_type == "diffusion_autoencoder":
from .diffusion import DiffusionAutoencoderDemoCallback
return DiffusionAutoencoderDemoCallback(
demo_every=demo_config.get("demo_every", 2000),
demo_steps=demo_config.get("demo_steps", 250),
sample_size=model_config["sample_size"],
sample_rate=model_config["sample_rate"],
**kwargs
)
elif model_type == "diffusion_prior":
from .diffusion import DiffusionPriorDemoCallback
return DiffusionPriorDemoCallback(
demo_every=demo_config.get("demo_every", 2000),
demo_steps=demo_config.get("demo_steps", 250),
sample_size=model_config["sample_size"],
sample_rate=model_config["sample_rate"],
**kwargs
)
elif model_type == "diffusion_cond":
from .diffusion import DiffusionCondDemoCallback
return DiffusionCondDemoCallback(
demo_every=demo_config.get("demo_every", 2000),
sample_size=model_config["sample_size"],
sample_rate=model_config["sample_rate"],
demo_steps=demo_config.get("demo_steps", 250),
num_demos=demo_config["num_demos"],
demo_cfg_scales=demo_config["demo_cfg_scales"],
demo_conditioning=demo_config.get("demo_cond", {}),
demo_cond_from_batch=demo_config.get("demo_cond_from_batch", False),
display_audio_cond=demo_config.get("display_audio_cond", False),
)
elif model_type == "diffusion_cond_inpaint":
from .diffusion import DiffusionCondInpaintDemoCallback
return DiffusionCondInpaintDemoCallback(
demo_every=demo_config.get("demo_every", 2000),
sample_size=model_config["sample_size"],
sample_rate=model_config["sample_rate"],
demo_steps=demo_config.get("demo_steps", 250),
demo_cfg_scales=demo_config["demo_cfg_scales"],
**kwargs
)
elif model_type == "lm":
from .lm import AudioLanguageModelDemoCallback
return AudioLanguageModelDemoCallback(
demo_every=demo_config.get("demo_every", 2000),
sample_size=model_config["sample_size"],
sample_rate=model_config["sample_rate"],
demo_cfg_scales=demo_config.get("demo_cfg_scales", [1]),
demo_conditioning=demo_config.get("demo_cond", None),
num_demos=demo_config.get("num_demos", 8),
**kwargs
)
else:
raise NotImplementedError(f'Unknown model type: {model_type}') |