DistilCodec/train_utils.py at main · IDEA-Emdoor-Lab/DistilCodec · GitHub

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import os

import pathlib

import glob

import re

import random

import functools

import itertools

import json

import copy

import numpy as np

import torch

from torch.utils.data import DistributedSampler, DataLoader

from torch.nn.parallel import DistributedDataParallel

from torch.distributed.fsdp import FullyShardedDataParallel as FSDP

from torch.distributed.fsdp import FullStateDictConfig, StateDictType

from torch.distributed.fsdp.fully_sharded_data_parallel import (

BackwardPrefetch,

MixedPrecision

)

from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy

from models import MultiScaleSTFTDiscriminator

from models import HiFiGANGenerator

from models import MultiPeriodDiscriminator

from models import MultiScaleDiscriminator

from models import ConvNeXtEncoder

from vector_quantization import DownsampleGRFSQ

from vector_quantization import DownsampleGRVQ

from code_distiller import MultiGroupDistillation

from models import MelDataset

class AttrDict(dict):

def __init__(self, *args, **kwargs):

super(AttrDict, self).__init__(*args, **kwargs)

self.__dict__ = self

def load_checkpoint(filepath, device):

assert os.path.isfile(filepath)

print("Loading '{}'".format(filepath))

checkpoint_dict = torch.load(filepath, map_location=torch.device('cpu'))

print("Complete.")

return checkpoint_dict

def save_checkpoint(filepath, obj, num_ckpt_keep=5):

name = re.match(r'(do|g)_\d+', pathlib.Path(filepath).name).group(1)

ckpts = sorted(pathlib.Path(filepath).parent.glob(f'{name}_*'))

if len(ckpts) > num_ckpt_keep:

[os.remove(c) for c in ckpts[:-num_ckpt_keep]]

print("Saving checkpoint to {}".format(filepath))

torch.save(obj, filepath)

print("Complete.")

def scan_checkpoint(cp_dir, prefix, target_steps: int = -1):

pattern = os.path.join(cp_dir, prefix + '????????')

cp_list = glob.glob(pattern)

if len(cp_list) == 0:

return None

if target_steps != -1:

for p in cp_list:

if p.endswith(str(target_steps)):

return p

return sorted(cp_list)[-1]

def seed_everything(seed, cudnn_deterministic=False):

"""

Function that sets seed for pseudo-random number generators in:

pytorch, numpy, python.random

Args:

seed: the integer value seed for global random state

"""

if seed is not None:

# print(f"Global seed set to {seed}")

random.seed(seed)

np.random.seed(seed)

torch.manual_seed(seed)

torch.cuda.manual_seed_all(seed)

bfSixteen = MixedPrecision(

param_dtype=torch.bfloat16,

# Gradient communication precision.

reduce_dtype=torch.bfloat16,

# Buffer precision.

buffer_dtype=torch.bfloat16,

)

my_auto_wrap_policy = functools.partial(

size_based_auto_wrap_policy,

min_num_params=20000)

def init_fsdp_model(model: torch.nn.Module):

fsdp_model = FSDP(model,

auto_wrap_policy=my_auto_wrap_policy,

device_id=torch.cuda.current_device(),

# mixed_precision=bfSixteen,

# sharding_strategy=ShardingStrategy.SHARD_GRAD_OP,

backward_prefetch=BackwardPrefetch.BACKWARD_PRE)

return fsdp_model

def fsdp_state_dict(model: torch.nn.Module):

print('fsdp state dict1')

save_policy = FullStateDictConfig(offload_to_cpu=True,

rank0_only=True)

with FSDP.state_dict_type(

model,

StateDictType.FULL_STATE_DICT,

save_policy):

cpu_state = model.state_dict()

print('fsdp state dict1')

return cpu_state

def is_startswith_filterd(param_name: str, filtered_params: list) -> bool:

for filtered in filtered_params:

if param_name.startswith(filtered):

return True

return False

def load_enocder_state_dict(encoder: torch.nn.Module,

state_dict_g,

mel_chs,

enc_input_chs) -> torch.nn.Module:

pretrained_enc_state_dict = state_dict_g["encoder"]

if mel_chs != enc_input_chs:

filtered_param_names = ["downsample_layers.0.0.weight", "downsample_layers.0.0.bias"]

print(f'Mel channels [{mel_chs}] is not equal to encoder input channels [{enc_input_chs}], so we will not load weights of {filtered_param_names}')

enc_state_dict = encoder.state_dict()

filtered_state_dict = {k: v for k, v in pretrained_enc_state_dict.items()

if not is_startswith_filterd(k, filtered_param_names) and k in enc_state_dict}

enc_state_dict.update(filtered_state_dict)

encoder.load_state_dict(enc_state_dict)

else:

encoder.load_state_dict(pretrained_enc_state_dict, strict=True)

return encoder

def load_decoder_state_dict(generator: torch.nn.Module,

state_dict_g,

stu_configs,

tea_configs) -> torch.nn.Module:

pretrained_gen_state_dict = state_dict_g["generator"]

filtered_params = []

stu_ups = stu_configs['upsample_rates']

stu_up_kernals = stu_configs['upsample_kernel_sizes']

tea_ups = tea_configs['upsample_rates']

tea_up_kernals = tea_configs['upsample_kernel_sizes']

if len(stu_ups) != len(tea_ups):

filtered_params.append('conv_post')

for i, z_ups in enumerate(zip(stu_configs['upsample_rates'],

tea_configs['upsample_rates'])):

stu_ups, tea_ups = z_ups

if stu_ups != tea_ups or stu_up_kernals[i] != tea_up_kernals[i]:

filtered_params.append(f'ups.{i}')

print(f'Filterd parameters: {filtered_params}')

gen_state_dict = generator.state_dict()

filtered_state_dict = {k: v for k, v in pretrained_gen_state_dict.items()

if not is_startswith_filterd(k, filtered_params) and k in gen_state_dict}

gen_state_dict.update(filtered_state_dict)

generator.load_state_dict(gen_state_dict)

return generator

def load_pretrain_quantizer_state_dict(quantizer: torch.nn.Module,

state_dict_g) -> torch.nn.Module:

pretrained_quantizer_state_dict = state_dict_g["quantizer"]

quantizer_state_dict = quantizer.state_dict()

print(f'Loading downsample and upsample pretrained params...')

filtered_state_dict = {k: v for k, v in pretrained_quantizer_state_dict.items()

if k.startswith("downsample") and k in quantizer_state_dict} # or k.startswith("upsample")) and k in quantizer_state_dict}

quantizer_state_dict.update(filtered_state_dict)

quantizer.load_state_dict(quantizer_state_dict)

return quantizer

def init_model_and_optimizer(

model_config: AttrDict,

train_config: AttrDict,

world_rank,

local_rank,

device,

use_fsdp=False,

is_debug=False,

is_distill=False,

quantizer_transfer=False):

encoder_configs = model_config['encoder']

mel_chs = model_config['spec_transform']['num_mels']

stu_enc_input_chs = encoder_configs['input_channels']

if mel_chs != stu_enc_input_chs:

stu_encoder_configs = copy.deepcopy(encoder_configs)

stu_encoder_configs['input_channels'] = mel_chs

encoder = ConvNeXtEncoder(**stu_encoder_configs).to(device)

else:

encoder = ConvNeXtEncoder(**encoder_configs).to(device)

generator_configs = model_config['decoder']

generator = HiFiGANGenerator(**generator_configs).to(device)

quantizer_config = model_config['quantizer']

quantizer_type = quantizer_config['quantizer_type']

quantizer_config.pop('quantizer_type')

if quantizer_type == 'fsq':

quantizer_config.pop('codebook_size')

quantizer_config.pop('codebook_dim')

quantizer = DownsampleGRFSQ(**quantizer_config).to(device)

elif quantizer_type == 'grvq':

quantizer = DownsampleGRVQ(**quantizer_config).to(device)

else:

raise ValueError('Quantier current surpport [grfsq, grvq]')

teacher_quantizer_config = model_config['teacher_quantizer']

tea_quantizer_type = teacher_quantizer_config['quantizer_type']

teacher_quantizer_config.pop('quantizer_type')

if tea_quantizer_type == 'fsq':

teacher_quantizer_config.pop('codebook_size')

teacher_quantizer_config.pop('codebook_dim')

tea_quantizer = DownsampleGRFSQ(**teacher_quantizer_config).to(device)

elif tea_quantizer_type == 'grvq':

tea_quantizer = DownsampleGRVQ(**teacher_quantizer_config).to(device)

else:

raise ValueError('Quantier current surpport [grfsq, grvq]')

n_group = teacher_quantizer_config['n_groups']

group_in_dim = teacher_quantizer_config['codebook_dim']

group_out_dim = quantizer_config['codebook_dim'] // n_group

distiller = MultiGroupDistillation(n_group=n_group,

group_in_dim=group_in_dim,

group_out_dim=group_out_dim).to(device)

discriminator_configs = model_config['descriminators']

mpd = MultiPeriodDiscriminator(mpd_config=discriminator_configs['MultiPeriodDiscriminator']).to(device)

msd = MultiScaleDiscriminator(msd_config=discriminator_configs['MultiScaleDiscriminator']).to(device)

mstftd = MultiScaleSTFTDiscriminator(msstft_config=discriminator_configs['MultiScaleSTFTDiscriminator']).to(device)

if world_rank == 0 and is_debug:

print(encoder)

print(quantizer)

print(generator)

is_distill_valid = False

if is_distill:

teacher_ckpt_path = train_config.distill['teacher_ckpt_path']

distill_cp_g = scan_checkpoint(teacher_ckpt_path, 'g_')

distill_cp_do = scan_checkpoint(teacher_ckpt_path, 'do_')

if (distill_cp_g is not None) and (distill_cp_do is not None):

print(f'Rank{world_rank} loading teacher ckpt...')

tea_state_dict_g = load_checkpoint(distill_cp_g, device)

tea_state_dict_do = load_checkpoint(distill_cp_do, device)

is_distill_valid = True

else:

raise ValueError(f'Teacher ckpt path [{teacher_ckpt_path}] has no checkpoint')

if 'load_path' in train_config:

stu_cp_g = scan_checkpoint(train_config.load_path, 'g_')

stu_cp_do = scan_checkpoint(train_config.load_path, 'do_')

else:

stu_cp_g = None

stu_cp_do = None

steps = 0

last_epoch = -1

min_mel_error = 100000.0

if stu_cp_g is None or stu_cp_do is None:

stu_state_dict_do = None

if is_distill_valid:

print(f'Rank{world_rank} loading teacher generator as initial model...')

if 'teacher_generator' not in model_config:

generator.load_state_dict(tea_state_dict_g['generator'])

else:

stu_gen_configs = model_config['decoder']

tea_gen_configs = model_config['teacher_generator']

generator = load_decoder_state_dict(generator=generator,

state_dict_g=tea_state_dict_g,

stu_configs=stu_gen_configs,

tea_configs=tea_gen_configs)

# print(f'Rank{world_rank} loading teacher discriminators as initial model...')

# mpd.load_state_dict(tea_state_dict_do['mpd'])

# msd.load_state_dict(tea_state_dict_do['msd'])

# mstftd.load_state_dict(tea_state_dict_do['mstftd'])

encoder = load_enocder_state_dict(

encoder=encoder,

state_dict_g=tea_state_dict_g,

mel_chs=mel_chs,

enc_input_chs=stu_enc_input_chs

)

if quantizer_transfer:

quantizer = load_pretrain_quantizer_state_dict(

quantizer=quantizer,

state_dict_g=tea_state_dict_g

)

else:

print(f'Rank{world_rank} loading "encoder" "generator" "quantizer" ckpt...')

stu_state_dict_g = load_checkpoint(stu_cp_g, device)

stu_state_dict_do = load_checkpoint(stu_cp_do, device)

generator.load_state_dict(stu_state_dict_g['generator'])

encoder = load_enocder_state_dict(

encoder=encoder,

state_dict_g=stu_state_dict_g,

mel_chs=mel_chs,

enc_input_chs=stu_enc_input_chs

)

quantizer.load_state_dict(stu_state_dict_g['quantizer'])

mpd.load_state_dict(stu_state_dict_do['mpd'])

msd.load_state_dict(stu_state_dict_do['msd'])

mstftd.load_state_dict(stu_state_dict_do['mstftd'])

if mel_chs == stu_enc_input_chs:

last_epoch = stu_state_dict_do['epoch']

steps = stu_state_dict_do['steps'] + 1

min_mel_error = stu_state_dict_do['min_mel_error'] if 'min_mel_error' in stu_state_dict_do else min_mel_error

if train_config.num_gpus > 1:

if not use_fsdp:

generator = DistributedDataParallel(

generator, device_ids=[local_rank]).to(device)

encoder = DistributedDataParallel(encoder, device_ids=[local_rank]).to(device)

quantizer = DistributedDataParallel(

quantizer, device_ids=[local_rank]).to(device)

mpd = DistributedDataParallel(mpd, device_ids=[local_rank]).to(device)

msd = DistributedDataParallel(msd, device_ids=[local_rank]).to(device)

mstftd = DistributedDataParallel(mstftd, device_ids=[local_rank]).to(device)

if is_distill:

distiller = DistributedDataParallel(distiller, device_ids=[local_rank]).to(device)

else:

print(f'Process-{world_rank} FSDP init...')

encoder = init_fsdp_model(model=encoder)

quantizer = init_fsdp_model(model=quantizer)

generator = init_fsdp_model(model=generator)

mpd = init_fsdp_model(model=mpd)

msd = init_fsdp_model(model=msd)

mstftd = init_fsdp_model(model=mstftd)

if is_distill:

distiller = init_fsdp_model(model=distiller)

adam_config = train_config['adam']

params_list = [generator.parameters(), encoder.parameters(), quantizer.parameters(), distiller.parameters()]

optim_g = torch.optim.AdamW(

itertools.chain(*params_list),

adam_config['learning_rate'],

betas=[adam_config['adam_b1'],

adam_config['adam_b2']])

optim_d = torch.optim.AdamW(

itertools.chain(msd.parameters(),

mpd.parameters(),

mstftd.parameters()),

adam_config['learning_rate'],

betas=[adam_config['adam_b1'],

adam_config['adam_b2']])

if stu_state_dict_do is not None:

print(f'Rank{world_rank} loading "generaotr-adam" "discriminator-adam" ckpt...')

if mel_chs == stu_enc_input_chs:

optim_d.load_state_dict(stu_state_dict_do['optim_d'])

optim_g.load_state_dict(stu_state_dict_do['optim_g'])

scheduler_g = torch.optim.lr_scheduler.ExponentialLR(

optim_g,

gamma=adam_config['lr_decay'],

last_epoch=last_epoch)

scheduler_d = torch.optim.lr_scheduler.ExponentialLR(

optim_d,

gamma=adam_config['lr_decay'],

last_epoch=last_epoch)

ret_dict = {

"encoder": encoder,

"quantizer": quantizer,

"quantizer_type": quantizer_type,

"generator": generator,

"mpd": mpd,

"msd": msd,

"mstft": mstftd,

"steps": steps,

"last_epoch": last_epoch,

"min_mel_error": min_mel_error,

"optim_g": optim_g,

"optim_d": optim_d,

"scheduler_g": scheduler_g,

"scheduler_d": scheduler_d

}

ret_dict = AttrDict(ret_dict)

return ret_dict

def init_validation_dataset(valid_dataset: list, train_config: AttrDict):

valid_sampler = DistributedSampler(valid_dataset,

shuffle=False) if train_config.num_gpus > 1 else None

valid_loader = DataLoader(

valid_dataset,

num_workers=train_config.num_workers,

shuffle=False,

sampler=valid_sampler,

batch_size=1,

pin_memory=True,

drop_last=True)

return valid_loader, valid_sampler

def init_dataset(train_config: AttrDict, model_config: AttrDict, world_rank, device):

mel_config = model_config['spec_transform']

with open(train_config.training_filelist_path, 'r') as f:

print(f'Rank{world_rank} loading audio file list...')

training_filelist = json.load(fp=f)

print(f'Number of file: {len(training_filelist)}')

trainset = MelDataset(

training_filelist,

**mel_config,

n_cache_reuse=0,

shuffle=False if train_config.num_gpus > 1 else True,

device=device,

fine_tuning=train_config.fine_tuning,

base_mels_path=train_config.mels_path)

train_sampler = DistributedSampler(trainset) if train_config.num_gpus > 1 else None

train_loader = DataLoader(

trainset,

num_workers=train_config.num_workers,

shuffle=False,

sampler=train_sampler,

batch_size=train_config.batch_size,

pin_memory=True,

drop_last=True)

'''if world_rank == 0:

print(f'Rank{world_rank} loading validation files...')

with open(train_config.validation_filelist_path, 'r') as f:

validation_filelist = json.load(fp=f)

validset = MelDataset(

validation_filelist,

**mel_config,

split=False,

shuffle=False,

n_cache_reuse=0,

device=device,

fine_tuning=train_config.fine_tuning,

base_mels_path=train_config.mels_path)

validation_loader = DataLoader(

validset,

num_workers=1,

shuffle=False,

sampler=None,

batch_size=1,

pin_memory=True,

drop_last=True)

else:

validation_loader = None'''

print(f'Rank{world_rank} loading validation files...')

with open(train_config.validation_filelist_path, 'r') as f:

validation_filelist = json.load(fp=f)

validset = MelDataset(

validation_filelist,

**mel_config,

split=False,

shuffle=False,

n_cache_reuse=0,

device=device,

fine_tuning=train_config.fine_tuning,

base_mels_path=train_config.mels_path)

valid_dataloader, valid_sampler = init_validation_dataset(valid_dataset=validset, train_config=train_config)

return train_loader, train_sampler, valid_dataloader, valid_sampler

DistilCodec/train_utils.py at main · IDEA-Emdoor-Lab/DistilCodec · GitHub

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