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import os
import sys
import math
import glob
import uuid
import shutil
import random
import tempfile
import importlib
from pathlib import Path
import torch
import torchaudio
import numpy as np
from tqdm import tqdm
from tensorboardX import SummaryWriter
from torch.utils.data import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed import is_initialized, get_rank, get_world_size
from s3prl import hub
from s3prl.optimizers import get_optimizer
from s3prl.schedulers import get_scheduler
from s3prl.upstream.interfaces import Featurizer
from s3prl.utility.helper import is_leader_process, get_model_state, show, defaultdict
from huggingface_hub import HfApi, HfFolder, Repository
SAMPLE_RATE = 16000
MODEL_CARD_MARKDOWN = """---
datasets:
- superb
tags:
- library:s3prl
- benchmark:superb
- type:model
---
# Fine-tuned s3prl model
Upstream Model: {upstream_model}
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evaluation results
[More information needed]
"""
class ModelEntry:
def __init__(self, model, name, trainable, interfaces):
self.model = model
self.name = name
self.trainable = trainable
self.interfaces = interfaces
class Runner():
"""
Used to handle high-level concepts of a ML experiment
eg. training loop, evaluation loop, upstream propagation, optimization, logging, checkpoint saving
"""
def __init__(self, args, config):
self.args = args
self.config = config
self.init_ckpt = torch.load(self.args.init_ckpt, map_location='cpu') if self.args.init_ckpt else {}
self.upstream = self._get_upstream()
self.featurizer = self._get_featurizer()
self.downstream = self._get_downstream()
self.all_entries = [self.upstream, self.featurizer, self.downstream]
def _load_weight(self, model, name):
init_weight = self.init_ckpt.get(name)
if init_weight:
show(f'[Runner] - Loading {name} weights from the previous experiment')
model.load_state_dict(init_weight)
def _init_model(self, model, name, trainable, interfaces=None):
for interface in interfaces or []:
assert hasattr(model, interface), interface
self._load_weight(model, name)
if is_initialized() and trainable and any((p.requires_grad for p in model.parameters())):
model = DDP(model, device_ids=[self.args.local_rank], find_unused_parameters=True)
for interface in interfaces or []:
setattr(model, interface, getattr(model.module, interface))
return ModelEntry(model, name, trainable, interfaces)
def _get_upstream(self):
if "from_hf_hub" in self.args and self.args.from_hf_hub == True:
from huggingface_hub import snapshot_download
print(f'[Runner] - Downloading upstream model {self.args.upstream} from the Hugging Face Hub')
filepath = snapshot_download(self.args.upstream, self.args.upstream_revision, use_auth_token=True)
sys.path.append(filepath)
dependencies = (Path(filepath) / 'requirements.txt').resolve()
print("[Dependency] - The downloaded upstream model requires the following dependencies. Please make sure they are installed:")
for idx, line in enumerate((Path(filepath) / "requirements.txt").open().readlines()):
print(f"{idx}. {line.strip()}")
print(f"You can install them by:")
print()
print(f"pip install -r {dependencies}")
print()
from expert import UpstreamExpert
Upstream = UpstreamExpert
ckpt_path = os.path.join(filepath, self.args.upstream_model_name)
else:
Upstream = getattr(hub, self.args.upstream)
ckpt_path = self.args.upstream_ckpt
upstream_refresh = self.args.upstream_refresh
if is_initialized() and get_rank() > 0:
torch.distributed.barrier()
upstream_refresh = False
model = Upstream(
ckpt = ckpt_path,
model_config = self.args.upstream_model_config,
refresh = upstream_refresh,
).to(self.args.device)
if is_initialized() and get_rank() == 0:
torch.distributed.barrier()
return self._init_model(
model = model,
name = 'Upstream',
trainable = self.args.upstream_trainable,
interfaces = ["get_downsample_rates"]
)
def _get_featurizer(self):
model = Featurizer(
upstream = self.upstream.model,
feature_selection = self.args.upstream_feature_selection,
layer_selection = self.args.upstream_layer_selection,
upstream_device = self.args.device,
normalize = self.args.upstream_feature_normalize,
).to(self.args.device)
return self._init_model(
model = model,
name = 'Featurizer',
trainable = True,
interfaces = ['output_dim', 'downsample_rate']
)
def _get_downstream(self):
expert = importlib.import_module(f"s3prl.downstream.{self.args.downstream}.expert")
Downstream = getattr(expert, "DownstreamExpert")
model = Downstream(
upstream_dim = self.featurizer.model.output_dim,
upstream_rate = self.featurizer.model.downsample_rate,
**self.config,
**vars(self.args)
).to(self.args.device)
return self._init_model(
model = model,
name = 'Downstream',
trainable = True,
interfaces = ['get_dataloader', 'log_records']
)
def _get_optimizer(self, model_params):
optimizer = get_optimizer(
model_params,
self.config['runner']['total_steps'],
self.config['optimizer']
)
self._load_weight(optimizer, 'Optimizer')
return optimizer
def _get_scheduler(self, optimizer):
scheduler = get_scheduler(
optimizer,
self.config['runner']['total_steps'],
self.config['scheduler']
)
self._load_weight(scheduler, 'Scheduler')
return scheduler
def _create_model_card(self, path):
model_card = MODEL_CARD_MARKDOWN.format(upstream_model=self.args.upstream)
with open(os.path.join(path, "README.md"), "w") as f:
f.write(model_card)
def train(self):
# trainable parameters and train/eval mode
trainable_models = []
trainable_paras = []
for entry in self.all_entries:
if entry.trainable:
entry.model.train().to(self.args.device)
trainable_models.append(entry.model)
trainable_paras += list(entry.model.parameters())
else:
entry.model.eval()
# set amp
amp = self.config['runner'].get('fp16', False)
if amp:
print('[Runner] - Enabled fp16 training')
scaler = torch.cuda.amp.GradScaler()
# optimizer
optimizer = self._get_optimizer(trainable_models)
# scheduler
scheduler = None
if self.config.get('scheduler'):
scheduler = self._get_scheduler(optimizer)
# specaug
specaug = None
if self.config.get('specaug'):
from .specaug import SpecAug
specaug = SpecAug(**self.config["specaug"])
# progress bar
tqdm_file = sys.stderr if is_leader_process() else open(os.devnull, 'w')
pbar = tqdm(total=self.config['runner']['total_steps'], dynamic_ncols=True, desc='overall', file=tqdm_file)
init_step = self.init_ckpt.get('Step')
if init_step:
pbar.n = init_step
# Tensorboard logging
if is_leader_process():
logger = SummaryWriter(self.args.expdir)
batch_ids = []
backward_steps = 0
records = defaultdict(list)
epoch = self.init_ckpt.get('Epoch', 0)
train_split = self.config['runner'].get("train_dataloader", "train")
while pbar.n < pbar.total:
try:
dataloader = self.downstream.model.get_dataloader(train_split, epoch=epoch)
except TypeError as e:
if "unexpected keyword argument 'epoch'" in str(e):
dataloader = self.downstream.model.get_dataloader(train_split)
if hasattr(dataloader, "sampler") and isinstance(dataloader.sampler, DistributedSampler):
dataloader.sampler.set_epoch(epoch)
else:
raise
for batch_id, (wavs, *others) in enumerate(tqdm(dataloader, dynamic_ncols=True, desc='train', file=tqdm_file)):
# try/except block for forward/backward
try:
if pbar.n >= pbar.total:
break
global_step = pbar.n + 1
wavs = [torch.FloatTensor(wav).to(self.args.device) for wav in wavs]
with torch.cuda.amp.autocast(enabled=amp):
if self.upstream.trainable:
features = self.upstream.model(wavs)
else:
with torch.no_grad():
features = self.upstream.model(wavs)
features = self.featurizer.model(wavs, features)
if specaug:
features, _ = specaug(features)
loss = self.downstream.model(
train_split,
features, *others,
records = records,
)
batch_ids.append(batch_id)
gradient_accumulate_steps = self.config['runner'].get('gradient_accumulate_steps')
loss = (loss / gradient_accumulate_steps)
if amp:
scaler.scale(loss).backward()
else:
loss.backward()
del loss
except RuntimeError as e:
if 'CUDA out of memory' in str(e):
print(f'[Runner] - CUDA out of memory at step {global_step}')
if is_initialized():
raise
with torch.cuda.device(self.args.device):
torch.cuda.empty_cache()
optimizer.zero_grad()
continue
else:
raise
# whether to accumulate gradient
backward_steps += 1
if backward_steps % gradient_accumulate_steps > 0:
continue
# unscale
if amp:
scaler.unscale_(optimizer)
# gradient clipping
grad_norm = torch.nn.utils.clip_grad_norm_(
trainable_paras, self.config['runner']['gradient_clipping'])
# optimize
if amp:
scaler.step(optimizer)
scaler.update()
elif math.isnan(grad_norm):
print(f'[Runner] - grad norm is NaN at step {global_step}')
else:
optimizer.step()
optimizer.zero_grad()
# adjust learning rate
if scheduler:
scheduler.step()
if not is_leader_process():
batch_ids = []
records = defaultdict(list)
continue
# logging
if global_step % self.config['runner']['log_step'] == 0:
self.downstream.model.log_records(
train_split,
records = records,
logger = logger,
global_step = global_step,
batch_ids = batch_ids,
total_batch_num = len(dataloader),
)
batch_ids = []
records = defaultdict(list)
# evaluation and save checkpoint
save_names = []
if global_step % self.config['runner']['eval_step'] == 0:
for split in self.config['runner']['eval_dataloaders']:
save_names += self.evaluate(split, logger, global_step)
if global_step % self.config['runner']['save_step'] == 0:
def check_ckpt_num(directory):
max_keep = self.config['runner']['max_keep']
ckpt_pths = glob.glob(f'{directory}/states-*.ckpt')
if len(ckpt_pths) >= max_keep:
ckpt_pths = sorted(ckpt_pths, key=lambda pth: int(pth.split('-')[-1].split('.')[0]))
for ckpt_pth in ckpt_pths[:len(ckpt_pths) - max_keep + 1]:
os.remove(ckpt_pth)
check_ckpt_num(self.args.expdir)
save_names.append(f'states-{global_step}.ckpt')
if len(save_names) > 0:
all_states = {
'Optimizer': optimizer.state_dict(),
'Step': global_step,
'Epoch': epoch,
'Args': self.args,
'Config': self.config,
}
for entry in self.all_entries:
if entry.trainable:
all_states[entry.name] = get_model_state(entry.model)
if scheduler:
all_states['Scheduler'] = scheduler.state_dict()
if is_initialized():
all_states['WorldSize'] = get_world_size()
save_paths = [os.path.join(self.args.expdir, name) for name in save_names]
tqdm.write(f'[Runner] - Save the checkpoint to:')
for i, path in enumerate(save_paths):
tqdm.write(f'{i + 1}. {path}')
torch.save(all_states, path)
pbar.update(1)
epoch += 1
pbar.close()
if self.args.push_to_hf_hub:
self.push_to_huggingface_hub()
if is_leader_process():
logger.close()
def evaluate(self, split=None, logger=None, global_step=0):
"""evaluate function will always be called on a single process even during distributed training"""
# When this member function is called directly by command line
not_during_training = split is None and logger is None and global_step == 0
if not_during_training:
split = self.args.evaluate_split
tempdir = tempfile.mkdtemp()
logger = SummaryWriter(tempdir)
# fix seed to guarantee the same evaluation protocol across steps
random.seed(self.args.seed)
np.random.seed(self.args.seed)
torch.manual_seed(self.args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(self.args.seed)
with torch.cuda.device(self.args.device):
torch.cuda.empty_cache()
# record original train/eval states and set all models to eval
trainings = []
for entry in self.all_entries:
trainings.append(entry.model.training)
entry.model.eval()
# prepare data
dataloader = self.downstream.model.get_dataloader(split)
evaluate_ratio = float(self.config["runner"].get("evaluate_ratio", 1))
evaluate_steps = round(len(dataloader) * evaluate_ratio)
batch_ids = []
records = defaultdict(list)
for batch_id, (wavs, *others) in enumerate(tqdm(dataloader, dynamic_ncols=True, desc=split, total=evaluate_steps)):
if batch_id > evaluate_steps:
break
wavs = [torch.FloatTensor(wav).to(self.args.device) for wav in wavs]
with torch.no_grad():
features = self.upstream.model(wavs)
features = self.featurizer.model(wavs, features)
self.downstream.model(
split,
features, *others,
records = records,
batch_id = batch_id,
)
batch_ids.append(batch_id)
save_names = self.downstream.model.log_records(
split,
records = records,
logger = logger,
global_step = global_step,
batch_ids = batch_ids,
total_batch_num = len(dataloader),
)
batch_ids = []
records = defaultdict(list)
# prepare back to training
if torch.cuda.is_available():
with torch.cuda.device(self.args.device):
torch.cuda.empty_cache()
for entry, training in zip(self.all_entries, trainings):
if training:
entry.model.train().to(self.args.device)
if not_during_training:
logger.close()
shutil.rmtree(tempdir)
return [] if type(save_names) is not list else save_names
def inference(self):
filepath = Path(self.args.evaluate_split)
assert filepath.is_file(), filepath
filename = filepath.stem
if hasattr(self.downstream.model, "load_audio"):
wav = self.downstream.model.load_audio(filepath)
else:
wav, sr = torchaudio.load(str(filepath))
assert sr == SAMPLE_RATE, sr
wavs = [wav.view(-1).to(self.args.device)]
for entry in self.all_entries:
entry.model.eval()
with torch.no_grad():
features = self.upstream.model(wavs)
features = self.featurizer.model(wavs, features)
self.downstream.model.inference(features, [filename])
def push_to_huggingface_hub(self):
"""Creates a downstream repository on the Hub and pushes training artifacts to it."""
if self.args.hf_hub_org.lower() != "none":
organization = self.args.hf_hub_org
else:
organization = os.environ.get("HF_USERNAME")
huggingface_token = HfFolder.get_token()
print(f"[Runner] - Organisation to push fine-tuned model to: {organization}")
# Extract upstream repository metadata
if self.args.hub == "huggingface":
model_info = HfApi().model_info(self.args.upstream, token=huggingface_token)
downstream_model_id = model_info.sha
# Exclude "/" characters from downstream repo ID
upstream_model_id = model_info.modelId.replace("/", "__")
else:
upstream_model_id = self.args.upstream.replace("/", "__")
downstream_model_id = str(uuid.uuid4())[:8]
repo_name = f"{upstream_model_id}__{downstream_model_id}"
# Create downstream repo on the Hub
repo_url = HfApi().create_repo(
token=huggingface_token,
name=repo_name,
organization=organization,
exist_ok=True,
private=False,
)
print(f"[Runner] - Created Hub repo: {repo_url}")
# Download repo
HF_HUB_DIR = "hf_hub"
REPO_ROOT_DIR = os.path.join(self.args.expdir, HF_HUB_DIR, repo_name)
REPO_TASK_DIR = os.path.join(REPO_ROOT_DIR, self.args.downstream, self.args.expname)
print(f"[Runner] - Cloning Hub repo to {REPO_ROOT_DIR}")
model_repo = Repository(
local_dir=REPO_ROOT_DIR, clone_from=repo_url, use_auth_token=huggingface_token
)
# Pull latest changes if they exist
model_repo.git_pull()
# Copy checkpoints, tensorboard logs, and args / configs
# Note that this copies all files from the experiment directory,
# including those from multiple runs
shutil.copytree(self.args.expdir, REPO_TASK_DIR, dirs_exist_ok=True, ignore=shutil.ignore_patterns(HF_HUB_DIR))
# By default we use model.ckpt in the PreTrainedModel interface, so
# rename the best checkpoint to match this convention
checkpoints = list(Path(REPO_TASK_DIR).glob("*best*.ckpt"))
if len(checkpoints) == 0:
print("[Runner] - Did not find a best checkpoint! Using the final checkpoint instead ...")
CKPT_PATH = (
os.path.join(REPO_TASK_DIR, f"states-{self.config['runner']['total_steps']}.ckpt")
)
elif len(checkpoints) > 1:
print(f"[Runner] - More than one best checkpoint found! Using {checkpoints[0]} as default ...")
CKPT_PATH = checkpoints[0]
else:
print(f"[Runner] - Found best checkpoint {checkpoints[0]}!")
CKPT_PATH = checkpoints[0]
shutil.move(CKPT_PATH, os.path.join(REPO_TASK_DIR, "model.ckpt"))
model_repo.lfs_track("*.ckpt")
# Write model card
self._create_model_card(REPO_ROOT_DIR)
# Push everything to the Hub
print("[Runner] - Pushing model files to the Hub ...")
model_repo.push_to_hub()
print("[Runner] - Training run complete!")
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