620 lines
17 KiB
Python
Executable File
620 lines
17 KiB
Python
Executable File
import copy
|
|
import diskcache
|
|
import h5py
|
|
import json
|
|
import os
|
|
import subprocess
|
|
import sys
|
|
import time
|
|
|
|
import torch
|
|
|
|
from dataclasses import asdict, dataclass
|
|
from dataclasses import dataclass, field
|
|
|
|
from functools import cached_property
|
|
from pathlib import Path
|
|
|
|
from omegaconf import OmegaConf
|
|
|
|
from .utils.distributed import world_size
|
|
|
|
@dataclass()
|
|
class _Config:
|
|
cfg_path: str | None = None
|
|
|
|
@property
|
|
def relpath(self):
|
|
return Path(self.cfg_path)
|
|
|
|
@property
|
|
def cache_dir(self):
|
|
return self.relpath / ".cache"
|
|
|
|
@property
|
|
def ckpt_dir(self):
|
|
return self.relpath / "ckpt"
|
|
|
|
@property
|
|
def log_dir(self):
|
|
return self.relpath / "logs" / str(self.start_time)
|
|
|
|
@cached_property
|
|
def start_time(self):
|
|
return int(time.time())
|
|
|
|
@cached_property
|
|
def git_commit(self):
|
|
try:
|
|
cmd = "git rev-parse HEAD"
|
|
return subprocess.check_output(cmd.split()).decode("utf8").strip()
|
|
except:
|
|
return ""
|
|
|
|
@cached_property
|
|
def git_status(self):
|
|
try:
|
|
cmd = "git status"
|
|
return subprocess.check_output(cmd.split()).decode("utf8").strip()
|
|
except:
|
|
return ""
|
|
|
|
def dumps(self):
|
|
data = {k: getattr(self, k) for k in dir(self) if not k.startswith("__")}
|
|
data = {k: v for k, v in data.items() if not callable(v)}
|
|
return json.dumps(data, indent=2, default=str)
|
|
|
|
def dump(self, path=None):
|
|
if path is None:
|
|
path = self.log_dir / "cfg.json"
|
|
path.parent.mkdir(parents=True, exist_ok=True)
|
|
with open(path, "w") as f:
|
|
f.write(self.dumps())
|
|
|
|
@staticmethod
|
|
def _is_cfg_argv(s):
|
|
return "=" in s and "--" not in s
|
|
|
|
@classmethod
|
|
def from_yaml( cls, yaml_path ):
|
|
return cls.from_cli( [f'yaml="{yaml_path}"'] )
|
|
|
|
@classmethod
|
|
def from_cli(cls, args=sys.argv):
|
|
cli_cfg = OmegaConf.from_cli([s for s in args if cls._is_cfg_argv(s)])
|
|
|
|
# Replace argv to ensure there are no omegaconf options, for compatibility with argparse.
|
|
sys.argv = [s for s in sys.argv if not cls._is_cfg_argv(s)]
|
|
|
|
if cli_cfg.get("help"):
|
|
print(f"Configurable hyperparameters with their default values:")
|
|
print(json.dumps(asdict(cls()), indent=2, default=str))
|
|
exit()
|
|
|
|
if "yaml" in cli_cfg:
|
|
yaml_cfg = OmegaConf.load(cli_cfg.yaml)
|
|
yaml_path = Path(cli_cfg.yaml).absolute()
|
|
cfg_path = Path(*yaml_path.relative_to(Path.cwd()).parts[:-1])
|
|
cfg_path = cfg_path.with_suffix("")
|
|
cfg_path = f'./{cfg_path}'
|
|
|
|
yaml_cfg.setdefault("cfg_path", cfg_path)
|
|
cli_cfg.pop("yaml")
|
|
else:
|
|
yaml_cfg = {}
|
|
merged = OmegaConf.merge(yaml_cfg, cli_cfg)
|
|
return cls(**dict(merged))
|
|
|
|
def __repr__(self):
|
|
return str(self)
|
|
|
|
def __str__(self):
|
|
return self.dumps()
|
|
|
|
@dataclass()
|
|
class Dataset:
|
|
training: list[Path] = field(default_factory=lambda: [])
|
|
validation: list[Path] = field(default_factory=lambda: [])
|
|
noise: list[Path] = field(default_factory=lambda: [])
|
|
|
|
temp: list[Path] = field(default_factory=lambda: [])
|
|
|
|
speaker_name_getter: str = "lambda p: f'{p.parts[-3]}_{p.parts[-2]}'"
|
|
speaker_group_getter: str = "lambda p: f'{p.parts[-3]}'"
|
|
|
|
speaker_languages: dict = field(default_factory=lambda: {}) # dict where keys are the language codes and values are the speaker groups
|
|
|
|
hdf5_name: str = "data.h5"
|
|
use_hdf5: bool = False
|
|
use_metadata: bool = False
|
|
hdf5_flag: str = "a"
|
|
validate: bool = True
|
|
workers: int = 8
|
|
cache: bool = True
|
|
|
|
phones_range: list[int] = field(default_factory=lambda: [4, 256])
|
|
duration_range: list[float] = field(default_factory=lambda: [1.0, 12.0])
|
|
min_utterances: int = 2
|
|
|
|
random_utterance: float = 1.0
|
|
max_prompts: int = 3
|
|
prompt_duration: float = 3.0
|
|
|
|
max_resps: int = 1
|
|
p_resp_append: float = 1.0
|
|
|
|
sample_type: str = "path" # path | speaker
|
|
|
|
tasks_list: list[str] = field(default_factory=lambda: ["tts"])
|
|
|
|
@property
|
|
def min_phones(self):
|
|
return self.phones_range[0]
|
|
@property
|
|
def max_phones(self):
|
|
return self.phones_range[1]
|
|
@property
|
|
def min_duration(self):
|
|
return self.duration_range[0]
|
|
@property
|
|
def max_duration(self):
|
|
return self.duration_range[1]
|
|
|
|
@dataclass()
|
|
class Model:
|
|
name: str = "" # vanity name for the model
|
|
version: int = 1 # 1 = old with MultiEmbedding, 2 = new with AudioEmbedding
|
|
size: str | dict = "full" # preset string or explicitly defined dimensionality
|
|
resp_levels: int = 1 # RVQ-bin levels this model targets for outputs
|
|
prom_levels: int = 8 # RVQ-bin levels this model accepts as an input prompt
|
|
tasks: int = 8 # ["tts", "ns", "sr", "tse", "cse", "nse"] and leaves two more for anything else I want (like "svc")
|
|
langs: int = 1 # defined languages
|
|
experts: int = 1
|
|
arch_type: str = "retnet" # or "transformer""
|
|
training: bool = True # unneeded now
|
|
interleave: bool = False # use an interleaved AR rather than a split AR + NAR (experimental, worse performance and results)
|
|
p_ar_level: float | str = "auto" # determines odds of selecting the AR (level 0) when training, "auto" for default behavior
|
|
frozen_params: list[str] = field(default_factory=lambda: []) # frozen parameters that are not updated when training
|
|
|
|
@property
|
|
def full_name(self):
|
|
name = [ self.name ]
|
|
|
|
if self.size != "full" and isinstance(self.size, str):
|
|
name.append(self.size)
|
|
|
|
if self.arch_type != "transformer":
|
|
if self.experts > 1:
|
|
name.append(f'{self.experts}x'+self.arch_type.replace("/", "-"))
|
|
else:
|
|
name.append(self.arch_type.replace("/", "-"))
|
|
|
|
if self.interleave:
|
|
name.append("interleaved")
|
|
else:
|
|
name.append(f'{cfg.models.prom_levels}')
|
|
|
|
|
|
return "-".join(name)
|
|
|
|
@property
|
|
def tokens(self):
|
|
if isinstance(self.size, dict) and hasattr(self.size, "tokens"):
|
|
return self.size['tokens']
|
|
|
|
return 1024
|
|
|
|
@property
|
|
def dim(self):
|
|
if isinstance(self.size, dict) and hasattr(self.size, "dim"):
|
|
return self.size['dim']
|
|
|
|
if isinstance(self.size, float):
|
|
return math.floor(1024 * self.size)
|
|
|
|
if self.size == "quarter":
|
|
return 256
|
|
if self.size == "half":
|
|
return 512
|
|
return 1024
|
|
|
|
@property
|
|
def heads(self):
|
|
if isinstance(self.size, dict) and hasattr(self.size, "heads"):
|
|
return self.size['heads']
|
|
|
|
if isinstance(self.size, float):
|
|
return math.floor(16 * self.size)
|
|
|
|
if self.size == "quarter":
|
|
return 4
|
|
if self.size == "half":
|
|
return 8
|
|
return 16
|
|
|
|
@property
|
|
def layers(self):
|
|
if isinstance(self.size, dict) and hasattr(self.size, "layers"):
|
|
return self.size['layers']
|
|
|
|
if self.size == "double":
|
|
return 24
|
|
return 12
|
|
|
|
@property
|
|
def activation_checkpointing(self):
|
|
return cfg.trainer.activation_checkpointing
|
|
|
|
|
|
@dataclass()
|
|
class Models:
|
|
_max_levels: int = 0
|
|
_prom_levels: int = 1
|
|
|
|
_models: list[Model] = field(default_factory=lambda: [
|
|
Model(name="ar", resp_levels=1, prom_levels=8, tasks=8, langs=1, experts=1, training=True, interleave=False),
|
|
Model(name="nar", resp_levels=7, prom_levels=8, tasks=8, langs=1, experts=1, training=True, interleave=False),
|
|
])
|
|
|
|
def get(self, name=None):
|
|
if not name:
|
|
return [ Model(**model) for model in self._models ]
|
|
|
|
for model in self._models:
|
|
if model.name == name:
|
|
return model
|
|
|
|
raise ValueError
|
|
|
|
@property
|
|
def ar(self):
|
|
return self.get("ar")
|
|
|
|
@property
|
|
def ar_nar(self):
|
|
return self.get("ar+nar")
|
|
|
|
@property
|
|
def nar(self):
|
|
return self.get("nar")
|
|
|
|
@property
|
|
def prom_levels(self):
|
|
prom_levels = self._prom_levels
|
|
for model in self._models:
|
|
prom_levels = max(prom_levels, model.prom_levels)
|
|
return prom_levels
|
|
|
|
@property
|
|
def tasks(self):
|
|
tasks = 1
|
|
for model in self._models:
|
|
tasks = max(tasks, model.tasks)
|
|
return tasks
|
|
|
|
@property
|
|
def max_levels(self):
|
|
return self._max_levels if self._max_levels > 0 else self.prom_levels
|
|
|
|
@dataclass()
|
|
class Hyperparameters:
|
|
batch_size: int = 8
|
|
gradient_accumulation_steps: int = 32
|
|
gradient_clipping: int = 100
|
|
|
|
optimizer: str = "Adamw"
|
|
torch_optimizer: bool = False
|
|
optimizer_params: dict = field(default_factory=lambda: {})
|
|
learning_rate: float = 3.25e-4
|
|
|
|
scheduler_type: str = ""
|
|
scheduler_params: dict = field(default_factory=lambda: {})
|
|
|
|
@dataclass()
|
|
class Evaluation:
|
|
batch_size: int = 64
|
|
frequency: int = 250
|
|
size: int = 64
|
|
|
|
steps: int = 500
|
|
ar_temperature: float = 1.0
|
|
nar_temperature: float = 0.2
|
|
|
|
load_disabled_engines: bool = True
|
|
|
|
@dataclass()
|
|
class DeepSpeed:
|
|
zero_optimization_level: int = 0
|
|
use_compression_training: bool = False
|
|
compression_bits: int = 8
|
|
inferencing: bool = False
|
|
|
|
@cached_property
|
|
def ds_cfg(self):
|
|
scheduler_params = {}
|
|
for k in cfg.hyperparameters.scheduler_params:
|
|
scheduler_params[k] = cfg.hyperparameters.scheduler_params[k]
|
|
|
|
if cfg.hyperparameters.scheduler_type == "WarmupDecayLR" and 'total_num_steps' not in scheduler_params:
|
|
scheduler_params['total_num_steps'] = cfg.trainer.iterations
|
|
|
|
ds_cfg = {
|
|
"train_micro_batch_size_per_gpu": cfg.hyperparameters.batch_size,
|
|
"gradient_accumulation_steps": cfg.hyperparameters.gradient_accumulation_steps,
|
|
"optimizer": {
|
|
"type": cfg.hyperparameters.optimizer,
|
|
"params": {
|
|
"lr": cfg.hyperparameters.learning_rate,
|
|
}
|
|
} if not cfg.hyperparameters.torch_optimizer else None,
|
|
"scheduler": {
|
|
"type": cfg.hyperparameters.scheduler_type,
|
|
"params": scheduler_params,
|
|
} if cfg.hyperparameters.scheduler_type != "" else None,
|
|
"gradient_clipping": cfg.hyperparameters.gradient_clipping,
|
|
"fp16": {
|
|
"enabled": True,
|
|
"auto_cast": True,
|
|
} if cfg.trainer.weight_dtype.lower() == "float16" and not cfg.trainer.amp else None,
|
|
"bf16": {
|
|
"enabled": cfg.trainer.weight_dtype.lower() == "bfloat16" and not cfg.trainer.amp
|
|
},
|
|
"compression_training": {
|
|
"weight_quantization": {
|
|
"shared_parameters":{
|
|
"enabled": True,
|
|
"quantizer_kernel": True,
|
|
"schedule_offset": 0,
|
|
"quantize_groups": 64,
|
|
"quantize_verbose": True,
|
|
"quantization_type": "symmetric",
|
|
"rounding": "nearest",
|
|
"quantize_weight_in_forward": cfg.trainer.weight_dtype.lower() != "float16", # MoQ (quantize in optimization step) weight quantization is only supported for FP16
|
|
"fp16_mixed_quantize":{
|
|
"enabled": False,
|
|
"quantize_change_ratio": 1
|
|
}
|
|
},
|
|
"different_groups": {
|
|
"wq1": {
|
|
"params": {
|
|
"start_bits": self.compression_bits,
|
|
"target_bits": self.compression_bits,
|
|
"quantization_period": 0
|
|
},
|
|
"modules": [
|
|
# "^.+?$"
|
|
"blocks", # for transformer-based models
|
|
"retnet", # for RetNets-based models
|
|
]
|
|
}
|
|
}
|
|
},
|
|
"activation_quantization": {
|
|
"shared_parameters":{
|
|
"enabled": True,
|
|
"quantizer_kernel": True,
|
|
"schedule_offset": 0,
|
|
"quantize_groups": 64,
|
|
"quantize_verbose": True,
|
|
"quantization_type": "symmetric",
|
|
"rounding": "nearest",
|
|
"quantize_weight_in_forward": cfg.trainer.weight_dtype.lower() != "float16", # MoQ (quantize in optimization step) weight quantization is only supported for FP16
|
|
"fp16_mixed_quantize":{
|
|
"enabled": False,
|
|
"quantize_change_ratio": 1
|
|
}
|
|
},
|
|
"different_groups": {
|
|
"aq1": {
|
|
"params": {
|
|
"bits": self.compression_bits,
|
|
},
|
|
"modules": [
|
|
# "^.+?$"
|
|
"blocks", # for transformer-based models
|
|
"retnet", # for RetNets-based models
|
|
]
|
|
}
|
|
}
|
|
},
|
|
} if self.use_compression_training else None,
|
|
"zero_optimization": {
|
|
"stage": self.zero_optimization_level,
|
|
"contiguous_gradients": True,
|
|
"overlap_comm": True,
|
|
"reduce_scatter": True,
|
|
"reduce_bucket_size": 5e8,
|
|
"allgather_bucket_size": 5e8,
|
|
"sub_group_size": 5e8,
|
|
"round_robin_gradients": True,
|
|
"offload_optimizer": {
|
|
"device": "cpu",
|
|
"pin_memory": True
|
|
},
|
|
"offload_param": {
|
|
"device": "cpu",
|
|
"pin_memory": True
|
|
},
|
|
"zero_quantized_weights": self.use_compression_training,
|
|
"zero_hpz_partition_size": world_size(),
|
|
"zero_quantized_gradients": self.use_compression_training,
|
|
} if self.zero_optimization_level > 0 else None,
|
|
"comms_logger": {
|
|
"enabled": False
|
|
}
|
|
}
|
|
|
|
null_keys = [ k for k in ds_cfg if not ds_cfg[k] ]
|
|
for k in null_keys:
|
|
del ds_cfg[k]
|
|
|
|
if os.path.exists("./data/ds_config.json"):
|
|
ds_cfg.update(json.load(open("./data/ds_config.json", "r", encoding="utf-8")))
|
|
|
|
return ds_cfg
|
|
|
|
@dataclass()
|
|
class Trainer:
|
|
iterations: int = 100_000
|
|
|
|
save_tag: str = "step"
|
|
load_tag: str | None = None
|
|
|
|
save_on_oom: bool = True
|
|
save_on_quit: bool = True
|
|
|
|
export_on_save: bool = False
|
|
export_on_quit: bool = False
|
|
|
|
save_frequency: int = 100
|
|
|
|
keep_last_checkpoints: int = 0
|
|
|
|
load_state_dict: bool = False
|
|
load_states: bool = True
|
|
strict_loading: bool = True
|
|
load_module_only: bool = False
|
|
restart_step_count: bool = False
|
|
|
|
activation_checkpointing: bool = True
|
|
|
|
aggressive_optimizations: bool = False
|
|
check_for_oom: bool = True
|
|
gc_mode: str | None = None
|
|
load_disabled_engines: bool = False
|
|
|
|
weight_dtype: str = "float16"
|
|
amp: bool = False
|
|
|
|
load_webui: bool = False
|
|
|
|
backend: str = "local"
|
|
deepspeed: DeepSpeed = field(default_factory=lambda: DeepSpeed)
|
|
|
|
@cached_property
|
|
def dtype(self):
|
|
if self.weight_dtype == "float16":
|
|
return torch.float16
|
|
if self.weight_dtype == "bfloat16":
|
|
return torch.bfloat16
|
|
return torch.float32
|
|
|
|
|
|
@dataclass()
|
|
class Inference:
|
|
backend: str = "local"
|
|
weight_dtype: str = "float32"
|
|
amp: bool = False
|
|
|
|
normalize: bool = False # do NOT enable this unless you know exactly what you're doing
|
|
use_vocos: bool = True
|
|
|
|
recurrent_chunk_size: int = 0
|
|
recurrent_forward: bool = False
|
|
|
|
|
|
@cached_property
|
|
def dtype(self):
|
|
if self.weight_dtype == "float16":
|
|
return torch.float16
|
|
if self.weight_dtype == "bfloat16":
|
|
return torch.bfloat16
|
|
if self.weight_dtype == "int8":
|
|
return torch.int8
|
|
return torch.float32
|
|
|
|
@dataclass()
|
|
class BitsAndBytes:
|
|
enabled: bool = False
|
|
injects: bool = False
|
|
|
|
linear: bool = True
|
|
embedding: bool = True
|
|
|
|
@dataclass()
|
|
class Config(_Config):
|
|
device: str = "cuda"
|
|
mode: str = "training" # "inferencing"
|
|
experimental: bool = False # So I can stop commenting out things when committing
|
|
|
|
dataset: Dataset = field(default_factory=lambda: Dataset)
|
|
models: Models = field(default_factory=lambda: Models)
|
|
hyperparameters: Hyperparameters = field(default_factory=lambda: Hyperparameters)
|
|
evaluation: Evaluation = field(default_factory=lambda: Evaluation)
|
|
trainer: Trainer = field(default_factory=lambda: Trainer)
|
|
inference: Inference = field(default_factory=lambda: Inference)
|
|
bitsandbytes: BitsAndBytes = field(default_factory=lambda: BitsAndBytes)
|
|
|
|
@property
|
|
def sample_rate(self):
|
|
return 24_000
|
|
|
|
@property
|
|
def distributed(self):
|
|
return world_size() > 1
|
|
|
|
@cached_property
|
|
def get_spkr(self):
|
|
return eval(self.dataset.speaker_name_getter)
|
|
|
|
@cached_property
|
|
def get_spkr_group(self):
|
|
return eval(self.dataset.speaker_group_getter)
|
|
|
|
@cached_property
|
|
def diskcache(self):
|
|
if self.cfg_path is not None and self.dataset.cache:
|
|
return diskcache.Cache(self.cache_dir).memoize
|
|
return lambda: lambda x: x
|
|
|
|
def load_yaml( self, config_path ):
|
|
tmp = Config.from_yaml( config_path )
|
|
self.__dict__.update(tmp.__dict__)
|
|
|
|
def load_hdf5( self, write=False ):
|
|
if hasattr(self, 'hdf5'):
|
|
self.hdf5.close()
|
|
|
|
if self.distributed:
|
|
self.dataset.hdf5_flag = "r"
|
|
try:
|
|
self.hdf5 = h5py.File(f'{self.cfg_path}/{self.dataset.hdf5_name}', 'a' if write else self.dataset.hdf5_flag) # to-do, have an easy to set flag that determines if training or creating the dataset
|
|
except Exception as e:
|
|
print("Error while opening HDF5 file:", f'{self.cfg_path}/{self.dataset.hdf5_name}', str(e))
|
|
self.dataset.use_hdf5 = False
|
|
|
|
def format( self ):
|
|
self.dataset = Dataset(**self.dataset)
|
|
self.models = Models(**self.models)
|
|
self.hyperparameters = Hyperparameters(**self.hyperparameters)
|
|
self.evaluation = Evaluation(**self.evaluation)
|
|
self.trainer = Trainer(**self.trainer)
|
|
self.inference = Inference(**self.inference)
|
|
self.bitsandbytes = BitsAndBytes(**self.bitsandbytes)
|
|
|
|
self.trainer.deepspeed = DeepSpeed(**self.trainer.deepspeed)
|
|
|
|
self.dataset.training = [ Path(dir) for dir in self.dataset.training ]
|
|
self.dataset.validation = [ Path(dir) for dir in self.dataset.validation ]
|
|
self.dataset.noise = [ Path(dir) for dir in self.dataset.noise ]
|
|
|
|
|
|
cfg = Config.from_cli()
|
|
|
|
# OmegaConf might not coerce the dicts into the @dataclass decorated classes, so we (try to) coerce them ourselves
|
|
try:
|
|
cfg.format()
|
|
|
|
# cached_property stopped working...
|
|
if cfg.dataset.use_hdf5:
|
|
cfg.load_hdf5()
|
|
|
|
|
|
except Exception as e:
|
|
pass
|
|
|
|
|
|
if __name__ == "__main__":
|
|
print(cfg)
|