#!python
import os
import sys
from pathlib import Path
from tqdm import tqdm
import multiprocessing as mp
import numpy as np
import torch
import torch.nn as nn
import torch.distributed as dist
import torchvision.utils as tvu
import torchnet as tnt
import Levenshtein as Lev
from asr.utils.logger import logger
from asr.utils.misc import onehot2int, int2onehot, remove_duplicates, get_model_file_path
from asr.utils.adamw import AdamW
from asr.utils.lr_scheduler import CosineAnnealingWithRestartsLR
from asr.utils import params
from asr.kaldi.latgen import LatGenCTCDecoder
OPTIMIZER_TYPES = set([
"sgd",
"sgdr",
"adam",
"adamw",
"adamwr",
"rmsprop",
])
torch.backends.cudnn.enabled = True
torch.backends.cudnn.deterministic = False
torch.multiprocessing.freeze_support()
def init_distributed(use_cuda, backend="nccl", init="slurm", local_rank=-1):
#try:
# mp.set_start_method('spawn') # spawn, forkserver, and fork
#except RuntimeError:
# pass
try:
if local_rank == -1:
if init == "slurm":
rank = int(os.environ['SLURM_PROCID'])
world_size = int(os.environ['SLURM_NTASKS'])
local_rank = int(os.environ['SLURM_LOCALID'])
#maser_node = os.environ['SLURM_TOPOLOGY_ADDR']
#maser_port = '23456'
elif init == "ompi":
rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])
local_rank = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
if use_cuda:
device = local_rank % torch.cuda.device_count()
torch.cuda.set_device(device)
print(f"set cuda device to cuda:{device}")
master_node = os.environ["MASTER_ADDR"]
master_port = os.environ["MASTER_PORT"]
init_method = f"tcp://{master_node}:{master_port}"
#init_method = "env://"
dist.init_process_group(backend=backend, init_method=init_method, world_size=world_size, rank=rank)
print(f"initialized as {rank}/{world_size} via {init_method}")
else:
if use_cuda:
torch.cuda.set_device(local_rank)
print(f"set cuda device to cuda:{local_rank}")
dist.init_process_group(backend=backend, init_method="env://")
print(f"initialized as {dist.get_rank()}/{dist.get_world_size()} via env://")
except Exception as e:
print(f"initialized as single process")
def is_distributed():
try:
return (dist.get_world_size() > 1)
except:
return False
def get_rank():
try:
return dist.get_rank()
except:
return None
def set_seed(seed=None):
if seed is not None:
logger.info(f"set random seed to {seed}")
torch.manual_seed(seed)
np.random.seed(seed)
if args.use_cuda:
torch.cuda.manual_seed(seed)
def get_amp_handle(args):
if not args.use_cuda:
args.fp16 = False
if args.fp16:
from apex import amp
amp_handle = amp.init(enabled=True, enable_caching=True, verbose=False)
return amp_handle
else:
return None
class Trainer:
def __init__(self, model, amp_handle=None, init_lr=1e-2, max_norm=100, use_cuda=False,
fp16=False, log_dir='logs', model_prefix='model',
checkpoint=False, continue_from=None, opt_type=None,
*args, **kwargs):
if fp16:
import apex.parallel
from apex import amp
if not use_cuda:
raise RuntimeError
self.amp_handle = amp_handle
# training parameters
self.init_lr = init_lr
self.max_norm = max_norm
self.use_cuda = use_cuda
self.fp16 = fp16
self.log_dir = log_dir
self.model_prefix = model_prefix
self.checkpoint = checkpoint
self.opt_type = opt_type
self.epoch = 0
self.states = None
self.global_step = 0 # for tensorboard
# load from pre-trained model if needed
if continue_from is not None:
self.load(continue_from)
# setup model
self.model = model
if self.use_cuda:
logger.debug("using cuda")
self.model.cuda()
# setup loss
loss = kwargs.get('loss', None)
self.loss = (nn.CTCLoss(blank=0, reduction='mean') if loss is None
else loss)
# setup optimizer
self.optimizer = None
self.lr_scheduler = None
if opt_type is not None:
assert opt_type in OPTIMIZER_TYPES
parameters = self.model.parameters()
if opt_type == "sgdr":
logger.debug("using SGDR")
self.optimizer = torch.optim.SGD(parameters, lr=self.init_lr, momentum=0.9, weight_decay=1e-4)
#self.lr_scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=1, gamma=0.5)
self.lr_scheduler = CosineAnnealingWithRestartsLR(self.optimizer, T_max=5, T_mult=2)
elif opt_type == "adamw":
logger.debug("using AdamW")
self.optimizer = AdamW(parameters, lr=self.init_lr, betas=(0.9, 0.999), eps=1e-8, weight_decay=1e-4, amsgrad=True)
elif opt_type == "adamwr":
logger.debug("using AdamWR")
self.optimizer = AdamW(parameters, lr=self.init_lr, betas=(0.9, 0.999), eps=1e-8, weight_decay=1e-4, amsgrad=True)
self.lr_scheduler = CosineAnnealingWithRestartsLR(self.optimizer, T_max=5, T_mult=2)
elif opt_type == "adam":
logger.debug("using Adam")
self.optimizer = torch.optim.Adam(parameters, lr=self.init_lr, betas=(0.9, 0.999), eps=1e-8, weight_decay=1e-4)
elif opt_type == "rmsprop":
logger.debug("using RMSprop")
self.optimizer = torch.optim.RMSprop(parameters, lr=self.init_lr, alpha=0.95, eps=1e-8, weight_decay=1e-4, centered=True)
# setup decoder for test
self.decoder = LatGenCTCDecoder()
self.labeler = self.decoder.labeler
# FP16 and distributed after load
if self.fp16:
#self.model = network_to_half(self.model)
#self.optimizer = FP16_Optimizer(self.optimizer, static_loss_scale=128.)
self.optimizer = self.amp_handle.wrap_optimizer(self.optimizer)
if is_distributed():
if self.use_cuda:
local_rank = torch.cuda.current_device()
if fp16:
self.model = apex.parallel.DistributedDataParallel(self.model)
else:
self.model = nn.parallel.DistributedDataParallel(self.model,
device_ids=[local_rank],
output_device=local_rank)
else:
self.model = nn.parallel.DistributedDataParallel(self.model)
if self.states is not None:
self.restore_state()
def __get_model_name(self, desc):
return str(get_model_file_path(self.log_dir, self.model_prefix, desc))
def __remove_ckpt_files(self, epoch):
for ckpt in Path(self.log_dir).rglob(f"*_epoch_{epoch:03d}_ckpt_*"):
ckpt.unlink()
def train_loop_before_hook(self):
pass
def train_loop_checkpoint_hook(self):
pass
def train_loop_after_hook(self):
pass
def unit_train(self, data):
raise NotImplementedError
def train_epoch(self, data_loader):
self.model.train()
meter_loss = tnt.meter.MovingAverageValueMeter(len(data_loader) // 100 + 1)
#meter_accuracy = tnt.meter.ClassErrorMeter(accuracy=True)
#meter_confusion = tnt.meter.ConfusionMeter(params.NUM_CTC_LABELS, normalized=True)
if self.lr_scheduler is not None:
self.lr_scheduler.step()
logger.debug(f"current lr = {self.optimizer.param_groups[0]['lr']:.3e}")
if is_distributed() and data_loader.sampler is not None:
data_loader.sampler.set_epoch(self.epoch)
ckpt_step = 0.1
ckpts = iter(len(data_loader) * np.arange(ckpt_step, 1 + ckpt_step, ckpt_step))
def plot_graphs(loss, data_iter=0, title="train", stats=False):
#if self.lr_scheduler is not None:
# self.lr_scheduler.step()
x = self.epoch + data_iter / len(data_loader)
self.global_step = int(x / ckpt_step)
if logger.visdom is not None:
opts = { 'xlabel': 'epoch', 'ylabel': 'loss', }
logger.visdom.add_point(title=title, x=x, y=loss, **opts)
if logger.tensorboard is not None:
#logger.tensorboard.add_graph(self.model, xs)
#xs_img = tvu.make_grid(xs[0, 0], normalize=True, scale_each=True)
#logger.tensorboard.add_image('xs', self.global_step, xs_img)
#ys_hat_img = tvu.make_grid(ys_hat[0].transpose(0, 1), normalize=True, scale_each=True)
#logger.tensorboard.add_image('ys_hat', self.global_step, ys_hat_img)
logger.tensorboard.add_scalars(title, self.global_step, { 'loss': loss, })
if stats:
for name, param in self.model.named_parameters():
logger.tensorboard.add_histogram(name, self.global_step, param.clone().cpu().data.numpy())
self.train_loop_before_hook()
ckpt = next(ckpts)
t = tqdm(enumerate(data_loader), total=len(data_loader), desc="training", ncols=params.NCOLS)
for i, (data) in t:
loss_value = self.unit_train(data)
if loss_value is not None:
meter_loss.add(loss_value)
t.set_description(f"training (loss: {meter_loss.value()[0]:.3f})")
t.refresh()
#self.meter_accuracy.add(ys_int, ys)
#self.meter_confusion.add(ys_int, ys)
if i > ckpt:
plot_graphs(meter_loss.value()[0], i)
if self.checkpoint:
logger.info(f"training loss at epoch_{self.epoch:03d}_ckpt_{i:07d}: "
f"{meter_loss.value()[0]:5.3f}")
if not is_distributed() or (is_distributed() and dist.get_rank() == 0):
self.save(self.__get_model_name(f"epoch_{self.epoch:03d}_ckpt_{i:07d}"))
self.train_loop_checkpoint_hook()
ckpt = next(ckpts)
self.epoch += 1
logger.info(f"epoch {self.epoch:03d}: "
f"training loss {meter_loss.value()[0]:5.3f} ")
#f"training accuracy {meter_accuracy.value()[0]:6.3f}")
if not is_distributed() or (is_distributed() and dist.get_rank() == 0):
self.save(self.__get_model_name(f"epoch_{self.epoch:03d}"))
self.__remove_ckpt_files(self.epoch-1)
plot_graphs(meter_loss.value()[0], stats=True)
self.train_loop_after_hook()
def unit_validate(self, data):
raise NotImplementedError
def validate(self, data_loader):
"validate with label error rate by the edit distance between hyps and refs"
self.model.eval()
with torch.no_grad():
N, D = 0, 0
t = tqdm(enumerate(data_loader), total=len(data_loader), desc="validating", ncols=params.NCOLS)
for i, (data) in t:
hyps, refs = self.unit_validate(data)
# calculate ler
N += self.edit_distance(refs, hyps)
D += sum(len(r) for r in refs)
ler = N * 100. / D
t.set_description(f"validating (LER: {ler:.2f} %)")
t.refresh()
logger.info(f"validating at epoch {self.epoch:03d}: LER {ler:.2f} %")
title = f"validate"
x = self.epoch - 1 + i / len(data_loader)
if logger.visdom is not None:
opts = { 'xlabel': 'epoch', 'ylabel': 'LER', }
logger.visdom.add_point(title=title, x=x, y=ler, **opts)
if logger.tensorboard is not None:
logger.tensorboard.add_scalars(title, self.global_step, { 'LER': ler, })
def unit_test(self, data):
raise NotImplementedError
def test(self, data_loader):
"test with word error rate by the edit distance between hyps and refs"
self.model.eval()
with torch.no_grad():
N, D = 0, 0
t = tqdm(enumerate(data_loader), total=len(data_loader), desc="testing", ncols=params.NCOLS)
for i, (data) in t:
hyps, refs = self.unit_test(data)
# calculate wer
N += self.edit_distance(refs, hyps)
D += sum(len(r) for r in refs)
wer = N * 100. / D
t.set_description(f"testing (WER: {wer:.2f} %)")
t.refresh()
logger.info(f"testing at epoch {self.epoch:03d}: WER {wer:.2f} %")
def edit_distance(self, refs, hyps):
assert len(refs) == len(hyps)
n = 0
for ref, hyp in zip(refs, hyps):
r = [chr(c) for c in ref]
h = [chr(c) for c in hyp]
n += Lev.distance(''.join(r), ''.join(h))
return n
def target_to_loglikes(self, ys, label_lens):
max_len = max(label_lens.tolist())
num_classes = self.labeler.get_num_labels()
ys_hat = [torch.cat((torch.zeros(1).int(), ys[s:s+l], torch.zeros(max_len-l).int()))
for s, l in zip([0]+label_lens[:-1].cumsum(0).tolist(), label_lens.tolist())]
ys_hat = [int2onehot(torch.IntTensor(z), num_classes, floor=1e-3) for z in ys_hat]
ys_hat = torch.stack(ys_hat)
ys_hat = torch.log(ys_hat)
return ys_hat
def save_hook(self):
pass
def save(self, file_path, **kwargs):
Path(file_path).parent.mkdir(mode=0o755, parents=True, exist_ok=True)
logger.debug(f"saving the model to {file_path}")
if self.states is None:
self.states = dict()
self.states.update(kwargs)
self.states["epoch"] = self.epoch
self.states["opt_type"] = self.opt_type
if is_distributed():
model_state_dict = self.model.state_dict()
strip_prefix = 9 if self.fp16 else 7
# remove "module.1." prefix from keys
self.states["model"] = {k[strip_prefix:]: v for k, v in model_state_dict.items()}
else:
self.states["model"] = self.model.state_dict()
self.states["optimizer"] = self.optimizer.state_dict()
if self.lr_scheduler is not None:
self.states["lr_scheduler"] = self.lr_scheduler.state_dict()
self.save_hook()
torch.save(self.states, file_path)
def load(self, file_path):
if isinstance(file_path, str):
file_path = Path(file_path)
if not file_path.exists():
logger.error(f"no such file {file_path} exists")
sys.exit(1)
logger.debug(f"loading the model from {file_path}")
to_device = f"cuda:{torch.cuda.current_device()}" if self.use_cuda else "cpu"
self.states = torch.load(file_path, map_location=to_device)
def restore_state(self):
self.epoch = self.states["epoch"]
self.global_step = self.epoch * 10
if is_distributed():
self.model.load_state_dict({f"module.{k}": v for k, v in self.states["model"].items()})
else:
self.model.load_state_dict(self.states["model"])
if "opt_type" in self.states and self.opt_type == self.states["opt_type"]:
self.optimizer.load_state_dict(self.states["optimizer"])
if self.lr_scheduler is not None and "lr_scheduler" in self.states:
self.lr_scheduler.load_state_dict(self.states["lr_scheduler"])
#for _ in range(self.epoch-1):
# self.lr_scheduler.step()
class NonSplitTrainer(Trainer):
"""training model for overall utterance spectrogram as a single image"""
def unit_train(self, data):
xs, ys, frame_lens, label_lens, filenames, _ = data
try:
batch_size = xs.size(0)
if self.use_cuda:
xs = xs.cuda(non_blocking=True)
ys_hat, frame_lens = self.model(xs, frame_lens)
if self.fp16:
ys_hat = ys_hat.float()
ys_hat = ys_hat.transpose(0, 1).contiguous() # TxNxH
#torch.set_printoptions(threshold=5000000)
#print(ys_hat.shape, frame_lens, ys.shape, label_lens)
#print(onehot2int(ys_hat).squeeze(), ys)
loss = self.loss(ys_hat, ys, frame_lens, label_lens)
if torch.isnan(loss) or loss.item() == float("inf") or loss.item() == -float("inf"):
logger.warning("received an nan/inf loss: probably frame_lens < label_lens or the learning rate is too high")
#raise RuntimeError
return None
if frame_lens.cpu().lt(2*label_lens).nonzero().numel():
logger.debug("the batch includes a data with frame_lens < 2*label_lens: set loss to zero")
loss.mul_(0)
loss_value = loss.item()
self.optimizer.zero_grad()
if self.fp16:
#self.optimizer.backward(loss)
#self.optimizer.clip_master_grads(self.max_norm)
with self.optimizer.scale_loss(loss) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), self.max_norm)
self.optimizer.step()
if self.use_cuda:
torch.cuda.synchronize()
del loss
return loss_value
except Exception as e:
print(e)
print(filenames, frame_lens, label_lens)
raise
def unit_validate(self, data):
xs, ys, frame_lens, label_lens, filenames, _ = data
if self.use_cuda:
xs = xs.cuda(non_blocking=True)
ys_hat, frame_lens = self.model(xs, frame_lens)
if self.fp16:
ys_hat = ys_hat.float()
# convert likes to ctc labels
hyps = [onehot2int(yh[:s]).squeeze() for yh, s in zip(ys_hat, frame_lens)]
hyps = [remove_duplicates(h, blank=0) for h in hyps]
# slice the targets
pos = torch.cat((torch.zeros((1, ), dtype=torch.long), torch.cumsum(label_lens, dim=0)))
refs = [ys[s:l] for s, l in zip(pos[:-1], pos[1:])]
return hyps, refs
def unit_test(self, data, target_test=False):
xs, ys, frame_lens, label_lens, filenames, texts = data
if not target_test:
if self.use_cuda:
xs = xs.cuda(non_blocking=True)
ys_hat, frame_lens = self.model(xs, frame_lens)
if self.fp16:
ys_hat = ys_hat.float()
else:
ys_hat = self.target_to_loglikes(ys, label_lens)
# latgen decoding
if self.use_cuda:
ys_hat = ys_hat.cpu()
words, alignment, w_sizes, a_sizes = self.decoder(ys_hat, frame_lens)
w2i = self.labeler.word2idx
num_words = self.labeler.get_num_words()
words.masked_fill_(words.ge(num_words), w2i('<unk>'))
words.masked_fill_(words.lt(0), w2i('<unk>'))
hyps = [w[:s] for w, s in zip(words, w_sizes)]
# convert target texts to word indices
refs = [[w2i(w.strip()) for w in t.strip().split()] for t in texts]
return hyps, refs
class SplitTrainer(Trainer):
""" training model for splitting utterance into multiple images
single image stands for localized timing segment corresponding to frame output
"""
def unit_train(self, data):
xs, ys, frame_lens, label_lens, filenames, _ = data
try:
if self.use_cuda:
xs = xs.cuda(non_blocking=True)
ys_hat = self.model(xs)
if self.fp16:
ys_hat = ys_hat.float()
ys_hat = ys_hat.unsqueeze(dim=0).transpose(1, 2)
pos = torch.cat((torch.zeros((1, ), dtype=torch.long), torch.cumsum(frame_lens, dim=0)))
ys_hats = [ys_hat.narrow(2, p, l).clone() for p, l in zip(pos[:-1], frame_lens)]
max_len = torch.max(frame_lens)
ys_hats = [nn.ConstantPad1d((0, max_len-yh.size(2)), 0)(yh) for yh in ys_hats]
ys_hat = torch.cat(ys_hats).transpose(1, 2).transpose(0, 1)
loss = self.loss(ys_hat, ys, frame_lens, label_lens)
loss_value = loss.item()
self.optimizer.zero_grad()
if self.fp16:
#self.optimizer.backward(loss)
#self.optimizer.clip_master_grads(self.max_norm)
with self.optimizer.scale_loss(loss) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), self.max_norm)
self.optimizer.step()
del loss
except Exception as e:
print(filenames, frame_lens, label_lens)
raise
return loss_value
def unit_validate(self, data):
xs, ys, frame_lens, label_lens, filenames, _ = data
if self.use_cuda:
xs = xs.cuda(non_blocking=True)
ys_hat = self.model(xs)
if self.fp16:
ys_hat = ys_hat.float()
pos = torch.cat((torch.zeros((1, ), dtype=torch.long), torch.cumsum(frame_lens, dim=0)))
ys_hat = [ys_hat.narrow(0, p, l).clone() for p, l in zip(pos[:-1], frame_lens)]
# convert likes to ctc labels
hyps = [onehot2int(yh[:s]).squeeze() for yh, s in zip(ys_hat, frame_lens)]
hyps = [remove_duplicates(h, blank=0) for h in hyps]
# slice the targets
pos = torch.cat((torch.zeros((1, ), dtype=torch.long), torch.cumsum(label_lens, dim=0)))
refs = [ys[s:l] for s, l in zip(pos[:-1], pos[1:])]
return hyps, refs
def unit_test(self, data, target_test=False):
xs, ys, frame_lens, label_lens, filenames, texts = data
if not target_test:
if self.use_cuda:
xs = xs.cuda(non_blocking=True)
ys_hat = self.model(xs)
if self.fp16:
ys_hat = ys_hat.float()
ys_hat = ys_hat.unsqueeze(dim=0).transpose(1, 2)
pos = torch.cat((torch.zeros((1, ), dtype=torch.long), torch.cumsum(frame_lens, dim=0)))
ys_hats = [ys_hat.narrow(2, p, l).clone() for p, l in zip(pos[:-1], frame_lens)]
max_len = torch.max(frame_lens)
ys_hats = [nn.ConstantPad1d((0, max_len-yh.size(2)), 0)(yh) for yh in ys_hats]
ys_hat = torch.cat(ys_hats).transpose(1, 2)
else:
ys_hat = self.target_to_loglikes(ys, label_lens)
# latgen decoding
if self.use_cuda:
ys_hat = ys_hat.cpu()
words, alignment, w_sizes, a_sizes = self.decoder(ys_hat, frame_lens)
w2i = self.labeler.word2idx
num_words = self.labeler.get_num_words()
words.masked_fill_(words.ge(num_words), w2i('<unk>'))
words.masked_fill_(words.lt(0), w2i('<unk>'))
hyps = [w[:s] for w, s in zip(words, w_sizes)]
# convert target texts to word indices
refs = [[w2i(w.strip()) for w in t.strip().split()] for t in texts]
return hyps, refs
if __name__ == "__main__":
pass
