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icefall/egs/librispeech/ASR/zipformer_ctc/train.py
Fangjun Kuang fba5e67d5e
Fix CI tests. (#1974) 
- Introduce unified AMP helpers (create_grad_scaler, torch_autocast) to handle 
  deprecations in PyTorch ≥2.3.0

- Replace direct uses of torch.cuda.amp.GradScaler and torch.cuda.amp.autocast 
  with the new utilities across all training and inference scripts

- Update all torch.load calls to include weights_only=False for compatibility with 
  newer PyTorch versions
2025-07-01 13:47:55 +08:00

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#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang,
# Wei Kang
# Mingshuang Luo)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Usage:
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./zipformer_ctc/train.py \
--exp-dir ./zipformer_ctc/exp \
--world-size 4 \
--full-libri 1 \
--max-duration 500 \
--num-epochs 30
"""
import argparse
import copy
import logging
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, Optional, Tuple, Union
import torch
import torch.multiprocessing as mp
import torch.nn as nn
from asr_datamodule import LibriSpeechAsrDataModule
from decoder import Decoder
from lhotse.cut import Cut
from lhotse.dataset.sampling.base import CutSampler
from lhotse.utils import fix_random_seed
from model import CTCModel
from optim import Eden, LRScheduler, ScaledAdam
from torch import Tensor
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.nn.utils import clip_grad_norm_
from torch.utils.tensorboard import SummaryWriter
from zipformer import Zipformer
from icefall import diagnostics
from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
from icefall.checkpoint import load_checkpoint, remove_checkpoints
from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
from icefall.checkpoint import (
save_checkpoint_with_global_batch_idx,
update_averaged_model,
)
from icefall.dist import cleanup_dist, setup_dist
from icefall.env import get_env_info
from icefall.err import raise_grad_scale_is_too_small_error
from icefall.hooks import register_inf_check_hooks
from icefall.lexicon import Lexicon
from icefall.utils import (
AttributeDict,
MetricsTracker,
create_grad_scaler,
setup_logger,
str2bool,
torch_autocast,
)
LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, LRScheduler]
def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None:
if isinstance(model, DDP):
# get underlying nn.Module
model = model.module
for module in model.modules():
if hasattr(module, "batch_count"):
module.batch_count = batch_count
def add_model_arguments(parser: argparse.ArgumentParser):
parser.add_argument(
"--num-encoder-layers",
type=str,
default="2,4,3,2,4",
help="Number of zipformer encoder layers, comma separated.",
)
parser.add_argument(
"--feedforward-dims",
type=str,
default="1024,1024,2048,2048,1024",
help="Feedforward dimension of the zipformer encoder layers, comma separated.",
)
parser.add_argument(
"--nhead",
type=str,
default="8,8,8,8,8",
help="Number of attention heads in the zipformer encoder layers.",
)
parser.add_argument(
"--encoder-dims",
type=str,
default="384,384,384,384,384",
help="Embedding dimension in the 2 blocks of zipformer encoder layers, comma separated",
)
parser.add_argument(
"--attention-dims",
type=str,
default="192,192,192,192,192",
help="""Attention dimension in the 2 blocks of zipformer encoder layers, comma separated;
not the same as embedding dimension.""",
)
parser.add_argument(
"--encoder-unmasked-dims",
type=str,
default="256,256,256,256,256",
help="Unmasked dimensions in the encoders, relates to augmentation during training. "
"Must be <= each of encoder_dims. Empirically, less than 256 seems to make performance "
" worse.",
)
parser.add_argument(
"--zipformer-downsampling-factors",
type=str,
default="1,2,4,8,2",
help="Downsampling factor for each stack of encoder layers.",
)
parser.add_argument(
"--cnn-module-kernels",
type=str,
default="31,31,31,31,31",
help="Sizes of kernels in convolution modules",
)
parser.add_argument(
"--num-decoder-layers",
type=int,
default=6,
help="""Number of decoder layer of transformer decoder.
Setting this to 0 will not create the decoder at all (pure CTC model)
""",
)
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--world-size",
type=int,
default=1,
help="Number of GPUs for DDP training.",
)
parser.add_argument(
"--master-port",
type=int,
default=12354,
help="Master port to use for DDP training.",
)
parser.add_argument(
"--tensorboard",
type=str2bool,
default=True,
help="Should various information be logged in tensorboard.",
)
parser.add_argument(
"--num-epochs",
type=int,
default=30,
help="Number of epochs to train.",
)
parser.add_argument(
"--start-epoch",
type=int,
default=1,
help="""Resume training from this epoch. It should be positive.
If larger than 1, it will load checkpoint from
exp-dir/epoch-{start_epoch-1}.pt
""",
)
parser.add_argument(
"--start-batch",
type=int,
default=0,
help="""If positive, --start-epoch is ignored and
it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt
""",
)
parser.add_argument(
"--exp-dir",
type=str,
default="conformer_ctc/exp",
help="""The experiment dir.
It specifies the directory where all training related
files, e.g., checkpoints, log, etc, are saved
""",
)
parser.add_argument(
"--lang-dir",
type=str,
default="data/lang_bpe_500",
help="""The lang dir
It contains language related input files such as
"lexicon.txt"
""",
)
parser.add_argument(
"--base-lr", type=float, default=0.05, help="The base learning rate."
)
parser.add_argument(
"--lr-batches",
type=float,
default=5000,
help="""Number of steps that affects how rapidly the learning rate
decreases. We suggest not to change this.""",
)
parser.add_argument(
"--lr-epochs",
type=float,
default=3.5,
help="""Number of epochs that affects how rapidly the learning rate decreases.
""",
)
parser.add_argument(
"--att-rate",
type=float,
default=0.8,
help="""The attention rate.
The total loss is (1 - att_rate) * ctc_loss + att_rate * att_loss
""",
)
parser.add_argument(
"--seed",
type=int,
default=42,
help="The seed for random generators intended for reproducibility",
)
parser.add_argument(
"--print-diagnostics",
type=str2bool,
default=False,
help="Accumulate stats on activations, print them and exit.",
)
parser.add_argument(
"--inf-check",
type=str2bool,
default=False,
help="Add hooks to check for infinite module outputs and gradients.",
)
parser.add_argument(
"--save-every-n",
type=int,
default=2000,
help="""Save checkpoint after processing this number of batches"
periodically. We save checkpoint to exp-dir/ whenever
params.batch_idx_train % save_every_n == 0. The checkpoint filename
has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt'
Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the
end of each epoch where `xxx` is the epoch number counting from 0.
""",
)
parser.add_argument(
"--keep-last-k",
type=int,
default=30,
help="""Only keep this number of checkpoints on disk.
For instance, if it is 3, there are only 3 checkpoints
in the exp-dir with filenames `checkpoint-xxx.pt`.
It does not affect checkpoints with name `epoch-xxx.pt`.
""",
)
parser.add_argument(
"--average-period",
type=int,
default=200,
help="""Update the averaged model, namely `model_avg`, after processing
this number of batches. `model_avg` is a separate version of model,
in which each floating-point parameter is the average of all the
parameters from the start of training. Each time we take the average,
we do: `model_avg = model * (average_period / batch_idx_train) +
model_avg * ((batch_idx_train - average_period) / batch_idx_train)`.
""",
)
parser.add_argument(
"--use-fp16",
type=str2bool,
default=False,
help="Whether to use half precision training.",
)
add_model_arguments(parser)
return parser
def get_params() -> AttributeDict:
"""Return a dict containing training parameters.
All training related parameters that are not passed from the commandline
are saved in the variable `params`.
Commandline options are merged into `params` after they are parsed, so
you can also access them via `params`.
Explanation of options saved in `params`:
- best_train_loss: Best training loss so far. It is used to select
the model that has the lowest training loss. It is
updated during the training.
- best_valid_loss: Best validation loss so far. It is used to select
the model that has the lowest validation loss. It is
updated during the training.
- best_train_epoch: It is the epoch that has the best training loss.
- best_valid_epoch: It is the epoch that has the best validation loss.
- batch_idx_train: Used to writing statistics to tensorboard. It
contains number of batches trained so far across
epochs.
- log_interval: Print training loss if batch_idx % log_interval` is 0
- reset_interval: Reset statistics if batch_idx % reset_interval is 0
- valid_interval: Run validation if batch_idx % valid_interval is 0
- feature_dim: The model input dim. It has to match the one used
in computing features.
- subsampling_factor: The subsampling factor for the model.
- use_feat_batchnorm: Normalization for the input features, can be a
boolean indicating whether to do batch
normalization, or a float which means just scaling
the input features with this float value.
If given a float value, we will remove batchnorm
layer in `ConvolutionModule` as well.
- attention_dim: Hidden dim for multi-head attention model.
- head: Number of heads of multi-head attention model.
- num_decoder_layers: Number of decoder layer of transformer decoder.
- beam_size: It is used in k2.ctc_loss
- reduction: It is used in k2.ctc_loss
- use_double_scores: It is used in k2.ctc_loss
- weight_decay: The weight_decay for the optimizer.
- warm_step: The warm_step for Noam optimizer.
"""
params = AttributeDict(
{
"frame_shift_ms": 10.0,
"allowed_excess_duration_ratio": 0.1,
"best_train_loss": float("inf"),
"best_valid_loss": float("inf"),
"best_train_epoch": -1,
"best_valid_epoch": -1,
"batch_idx_train": 0,
"log_interval": 50,
"reset_interval": 200,
"valid_interval": 3000, # For the 100h subset, use 800
# parameters for zipformer
"feature_dim": 80,
"subsampling_factor": 4, # not passed in, this is fixed.
"warm_step": 2000,
"env_info": get_env_info(),
# parameters for loss
"beam_size": 10,
"reduction": "sum",
"use_double_scores": True,
# parameters for decoding
"search_beam": 20,
"output_beam": 8,
"min_active_states": 30,
"max_active_states": 10000,
}
)
return params
def get_encoder_model(params: AttributeDict) -> nn.Module:
# TODO: We can add an option to switch between Zipformer and Transformer
def to_int_tuple(s: str):
return tuple(map(int, s.split(",")))
encoder = Zipformer(
num_features=params.feature_dim,
output_downsampling_factor=2,
zipformer_downsampling_factors=to_int_tuple(
params.zipformer_downsampling_factors
),
encoder_dims=to_int_tuple(params.encoder_dims),
attention_dim=to_int_tuple(params.attention_dims),
encoder_unmasked_dims=to_int_tuple(params.encoder_unmasked_dims),
nhead=to_int_tuple(params.nhead),
feedforward_dim=to_int_tuple(params.feedforward_dims),
cnn_module_kernels=to_int_tuple(params.cnn_module_kernels),
num_encoder_layers=to_int_tuple(params.num_encoder_layers),
)
return encoder
def get_decoder_model(params: AttributeDict) -> nn.Module:
decoder = Decoder(
num_layers=params.num_decoder_layers,
num_classes=params.vocab_size,
d_model=int(params.encoder_dims.split(",")[-1]),
)
return decoder
def get_ctc_model(params: AttributeDict) -> nn.Module:
encoder = get_encoder_model(params)
decoder = get_decoder_model(params)
model = CTCModel(
encoder=encoder,
decoder=decoder,
encoder_dim=int(params.encoder_dims.split(",")[-1]),
vocab_size=params.vocab_size,
)
return model
def load_checkpoint_if_available(
params: AttributeDict,
model: nn.Module,
model_avg: nn.Module = None,
optimizer: Optional[torch.optim.Optimizer] = None,
scheduler: Optional[LRSchedulerType] = None,
) -> Optional[Dict[str, Any]]:
"""Load checkpoint from file.
If params.start_batch is positive, it will load the checkpoint from
`params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if
params.start_epoch is larger than 1, it will load the checkpoint from
`params.start_epoch - 1`.
Apart from loading state dict for `model` and `optimizer` it also updates
`best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
and `best_valid_loss` in `params`.
Args:
params:
The return value of :func:`get_params`.
model:
The training model.
model_avg:
The stored model averaged from the start of training.
optimizer:
The optimizer that we are using.
scheduler:
The scheduler that we are using.
Returns:
Return a dict containing previously saved training info.
"""
if params.start_batch > 0:
filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt"
elif params.start_epoch > 1:
filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
else:
return None
assert filename.is_file(), f"{filename} does not exist!"
saved_params = load_checkpoint(
filename,
model=model,
model_avg=model_avg,
optimizer=optimizer,
scheduler=scheduler,
)
keys = [
"best_train_epoch",
"best_valid_epoch",
"batch_idx_train",
"best_train_loss",
"best_valid_loss",
]
for k in keys:
params[k] = saved_params[k]
if params.start_batch > 0:
if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params
def save_checkpoint(
params: AttributeDict,
model: Union[nn.Module, DDP],
model_avg: Optional[nn.Module] = None,
optimizer: Optional[torch.optim.Optimizer] = None,
scheduler: Optional[LRSchedulerType] = None,
sampler: Optional[CutSampler] = None,
scaler: Optional["GradScaler"] = None,
rank: int = 0,
) -> None:
"""Save model, optimizer, scheduler and training stats to file.
Args:
params:
It is returned by :func:`get_params`.
model:
The training model.
model_avg:
The stored model averaged from the start of training.
optimizer:
The optimizer used in the training.
sampler:
The sampler for the training dataset.
scaler:
The scaler used for mix precision training.
"""
if rank != 0:
return
filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
save_checkpoint_impl(
filename=filename,
model=model,
model_avg=model_avg,
params=params,
optimizer=optimizer,
scheduler=scheduler,
sampler=sampler,
scaler=scaler,
rank=rank,
)
if params.best_train_epoch == params.cur_epoch:
best_train_filename = params.exp_dir / "best-train-loss.pt"
copyfile(src=filename, dst=best_train_filename)
if params.best_valid_epoch == params.cur_epoch:
best_valid_filename = params.exp_dir / "best-valid-loss.pt"
copyfile(src=filename, dst=best_valid_filename)
def compute_loss(
params: AttributeDict,
model: nn.Module,
batch: dict,
graph_compiler: BpeCtcTrainingGraphCompiler,
is_training: bool,
) -> Tuple[Tensor, MetricsTracker]:
"""
Compute CTC loss given the model and its inputs.
Args:
params:
Parameters for training. See :func:`get_params`.
model:
The model for training. It is an instance of Conformer in our case.
batch:
A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
for the content in it.
graph_compiler:
It is used to build a decoding graph from a ctc topo and training
transcript. The training transcript is contained in the given `batch`,
while the ctc topo is built when this compiler is instantiated.
is_training:
True for training. False for validation. When it is True, this
function enables autograd during computation; when it is False, it
disables autograd.
"""
device = graph_compiler.device
feature = batch["inputs"]
# at entry, feature is (N, T, C)
assert feature.ndim == 3
feature = feature.to(device)
supervisions = batch["supervisions"]
feature_lens = supervisions["num_frames"].to(device)
with torch.set_grad_enabled(is_training):
ctc_loss, att_loss, tot_frames = model(
feature,
feature_lens,
supervisions,
graph_compiler,
subsampling_factor=params.subsampling_factor,
beam_size=params.beam_size,
reduction=params.reduction,
use_double_scores=params.use_double_scores,
)
info = MetricsTracker()
info["frames"] = tot_frames
info["ctc_loss"] = ctc_loss.detach().cpu().item()
if params.att_rate != 0.0:
info["att_loss"] = att_loss.detach().cpu().item()
loss = (1.0 - params.att_rate) * ctc_loss + params.att_rate * att_loss
assert loss.requires_grad == is_training, f"{loss.requires_grad} != {is_training}"
info["loss"] = loss.detach().cpu().item()
# `utt_duration` and `utt_pad_proportion` would be normalized by `utterances` # noqa
info["utterances"] = feature.size(0)
# averaged input duration in frames over utterances
info["utt_duration"] = supervisions["num_frames"].sum().item()
# averaged padding proportion over utterances
info["utt_pad_proportion"] = (
((feature.size(1) - supervisions["num_frames"]) / feature.size(1)).sum().item()
)
return loss, info
def compute_validation_loss(
params: AttributeDict,
model: nn.Module,
graph_compiler: BpeCtcTrainingGraphCompiler,
valid_dl: torch.utils.data.DataLoader,
world_size: int = 1,
) -> MetricsTracker:
"""Run the validation process."""
model.eval()
tot_loss = MetricsTracker()
for batch_idx, batch in enumerate(valid_dl):
loss, loss_info = compute_loss(
params=params,
model=model,
batch=batch,
graph_compiler=graph_compiler,
is_training=False,
)
assert loss.requires_grad is False
tot_loss = tot_loss + loss_info
if world_size > 1:
tot_loss.reduce(loss.device)
loss_value = tot_loss["loss"] / tot_loss["frames"]
if loss_value < params.best_valid_loss:
params.best_valid_epoch = params.cur_epoch
params.best_valid_loss = loss_value
return tot_loss
def train_one_epoch(
params: AttributeDict,
model: Union[nn.Module, DDP],
optimizer: torch.optim.Optimizer,
scheduler: LRSchedulerType,
graph_compiler: BpeCtcTrainingGraphCompiler,
train_dl: torch.utils.data.DataLoader,
valid_dl: torch.utils.data.DataLoader,
scaler: "GradScaler",
model_avg: Optional[nn.Module] = None,
tb_writer: Optional[SummaryWriter] = None,
world_size: int = 1,
rank: int = 0,
) -> None:
"""Train the model for one epoch.
The training loss from the mean of all frames is saved in
`params.train_loss`. It runs the validation process every
`params.valid_interval` batches.
Args:
params:
It is returned by :func:`get_params`.
model:
The model for training.
optimizer:
The optimizer we are using.
graph_compiler:
It is used to convert transcripts to FSAs.
train_dl:
Dataloader for the training dataset.
valid_dl:
Dataloader for the validation dataset.
tb_writer:
Writer to write log messages to tensorboard.
world_size:
Number of nodes in DDP training. If it is 1, DDP is disabled.
"""
model.train()
tot_loss = MetricsTracker()
for batch_idx, batch in enumerate(train_dl):
params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"])
try:
with torch_autocast(enabled=params.use_fp16):
loss, loss_info = compute_loss(
params=params,
model=model,
batch=batch,
graph_compiler=graph_compiler,
is_training=True,
)
# summary stats
tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
# NOTE: We use reduction==sum and loss is computed over utterances
# in the batch and there is no normalization to it so far.
scaler.scale(loss).backward()
set_batch_count(model, params.batch_idx_train)
scheduler.step_batch(params.batch_idx_train)
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad()
except: # noqa
display_and_save_batch(batch, params=params, graph_compiler=graph_compiler)
raise
if params.print_diagnostics and batch_idx == 5:
return
if (
rank == 0
and params.batch_idx_train > 0
and params.batch_idx_train % params.average_period == 0
):
update_averaged_model(
params=params,
model_cur=model,
model_avg=model_avg,
)
if (
params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0
):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train,
model=model,
model_avg=model_avg,
params=params,
optimizer=optimizer,
scheduler=scheduler,
sampler=train_dl.sampler,
scaler=scaler,
rank=rank,
)
del params.cur_batch_idx
remove_checkpoints(
out_dir=params.exp_dir,
topk=params.keep_last_k,
rank=rank,
)
if batch_idx % 100 == 0 and params.use_fp16:
# If the grad scale was less than 1, try increasing it. The _growth_interval
# of the grad scaler is configurable, but we can't configure it to have different
# behavior depending on the current grad scale.
cur_grad_scale = scaler._scale.item()
if cur_grad_scale < 1.0 or (cur_grad_scale < 8.0 and batch_idx % 400 == 0):
scaler.update(cur_grad_scale * 2.0)
if cur_grad_scale < 0.01:
logging.warning(f"Grad scale is small: {cur_grad_scale}")
if cur_grad_scale < 1.0e-05:
raise_grad_scale_is_too_small_error(cur_grad_scale)
if batch_idx % params.log_interval == 0:
cur_lr = scheduler.get_last_lr()[0]
cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0
logging.info(
f"Epoch {params.cur_epoch}, "
f"batch {batch_idx}, loss[{loss_info}], "
f"tot_loss[{tot_loss}], batch size: {batch_size}, "
f"lr: {cur_lr:.2e}, "
+ (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
)
if tb_writer is not None:
tb_writer.add_scalar(
"train/learning_rate", cur_lr, params.batch_idx_train
)
loss_info.write_summary(
tb_writer, "train/current_", params.batch_idx_train
)
tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
if params.use_fp16:
tb_writer.add_scalar(
"train/grad_scale",
cur_grad_scale,
params.batch_idx_train,
)
if batch_idx % params.valid_interval == 0 and not params.print_diagnostics:
logging.info("Computing validation loss")
valid_info = compute_validation_loss(
params=params,
model=model,
graph_compiler=graph_compiler,
valid_dl=valid_dl,
world_size=world_size,
)
model.train()
logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
logging.info(
f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
)
if tb_writer is not None:
valid_info.write_summary(
tb_writer, "train/valid_", params.batch_idx_train
)
loss_value = tot_loss["loss"] / tot_loss["frames"]
params.train_loss = loss_value
if params.train_loss < params.best_train_loss:
params.best_train_epoch = params.cur_epoch
params.best_train_loss = params.train_loss
def run(rank, world_size, args):
"""
Args:
rank:
It is a value between 0 and `world_size-1`, which is
passed automatically by `mp.spawn()` in :func:`main`.
The node with rank 0 is responsible for saving checkpoint.
world_size:
Number of GPUs for DDP training.
args:
The return value of get_parser().parse_args()
"""
params = get_params()
params.update(vars(args))
fix_random_seed(params.seed)
if world_size > 1:
setup_dist(rank, world_size, params.master_port)
setup_logger(f"{params.exp_dir}/log/log-train")
logging.info("Training started")
logging.info(params)
if args.tensorboard and rank == 0:
tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
else:
tb_writer = None
lexicon = Lexicon(params.lang_dir)
max_token_id = max(lexicon.tokens)
num_classes = max_token_id + 1 # +1 for the blank
params.vocab_size = num_classes
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", rank)
graph_compiler = BpeCtcTrainingGraphCompiler(
params.lang_dir,
device=device,
sos_token="<sos/eos>",
eos_token="<sos/eos>",
)
logging.info("About to create model")
model = get_ctc_model(params)
num_param = sum([p.numel() for p in model.parameters()])
logging.info(f"Number of model parameters: {num_param}")
assert params.save_every_n >= params.average_period
model_avg: Optional[nn.Module] = None
if rank == 0:
# model_avg is only used with rank 0
model_avg = copy.deepcopy(model).to(torch.float64)
assert params.start_epoch > 0, params.start_epoch
checkpoints = load_checkpoint_if_available(
params=params, model=model, model_avg=model_avg
)
model.to(device)
if world_size > 1:
logging.info("Using DDP")
model = DDP(model, device_ids=[rank], find_unused_parameters=True)
parameters_names = []
parameters_names.append(
[name_param_pair[0] for name_param_pair in model.named_parameters()]
)
optimizer = ScaledAdam(
model.parameters(),
lr=params.base_lr,
clipping_scale=2.0,
parameters_names=parameters_names,
)
scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs)
if checkpoints and "optimizer" in checkpoints:
logging.info("Loading optimizer state dict")
optimizer.load_state_dict(checkpoints["optimizer"])
if (
checkpoints
and "scheduler" in checkpoints
and checkpoints["scheduler"] is not None
):
logging.info("Loading scheduler state dict")
scheduler.load_state_dict(checkpoints["scheduler"])
if params.print_diagnostics:
opts = diagnostics.TensorDiagnosticOptions(
512
) # allow 4 megabytes per sub-module
diagnostic = diagnostics.attach_diagnostics(model, opts)
if params.inf_check:
register_inf_check_hooks(model)
librispeech = LibriSpeechAsrDataModule(args)
if params.full_libri:
train_cuts = librispeech.train_all_shuf_cuts()
else:
train_cuts = librispeech.train_clean_100_cuts()
def remove_short_and_long_utt(c: Cut):
# Keep only utterances with duration between 1 second and 20 seconds
#
# Caution: There is a reason to select 20.0 here. Please see
# ../local/display_manifest_statistics.py
#
# You should use ../local/display_manifest_statistics.py to get
# an utterance duration distribution for your dataset to select
# the threshold
return 1.0 <= c.duration <= 25.0
train_cuts = train_cuts.filter(remove_short_and_long_utt)
train_dl = librispeech.train_dataloaders(train_cuts)
valid_cuts = librispeech.dev_clean_cuts()
valid_cuts += librispeech.dev_other_cuts()
valid_dl = librispeech.valid_dataloaders(valid_cuts)
scan_pessimistic_batches_for_oom(
model=model,
train_dl=train_dl,
optimizer=optimizer,
graph_compiler=graph_compiler,
params=params,
)
scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
if checkpoints and "grad_scaler" in checkpoints:
logging.info("Loading grad scaler state dict")
scaler.load_state_dict(checkpoints["grad_scaler"])
for epoch in range(params.start_epoch, params.num_epochs + 1):
scheduler.step_epoch(epoch - 1)
fix_random_seed(params.seed + epoch - 1)
train_dl.sampler.set_epoch(epoch - 1)
if tb_writer is not None:
tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
params.cur_epoch = epoch
train_one_epoch(
params=params,
model=model,
model_avg=model_avg,
optimizer=optimizer,
scheduler=scheduler,
graph_compiler=graph_compiler,
train_dl=train_dl,
valid_dl=valid_dl,
scaler=scaler,
tb_writer=tb_writer,
world_size=world_size,
rank=rank,
)
if params.print_diagnostics:
diagnostic.print_diagnostics()
break
save_checkpoint(
params=params,
model=model,
model_avg=model_avg,
optimizer=optimizer,
scheduler=scheduler,
sampler=train_dl.sampler,
scaler=scaler,
rank=rank,
)
logging.info("Done!")
if world_size > 1:
torch.distributed.barrier()
cleanup_dist()
def display_and_save_batch(
batch: dict,
params: AttributeDict,
graph_compiler: BpeCtcTrainingGraphCompiler,
) -> None:
"""Display the batch statistics and save the batch into disk.
Args:
batch:
A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
for the content in it.
params:
Parameters for training. See :func:`get_params`.
graph_compiler:
It is used to build a decoding graph from a ctc topo and training
transcript. The training transcript is contained in the given `batch`,
while the ctc topo is built when this compiler is instantiated.
"""
from lhotse.utils import uuid4
filename = f"{params.exp_dir}/batch-{uuid4()}.pt"
logging.info(f"Saving batch to {filename}")
torch.save(batch, filename)
supervisions = batch["supervisions"]
features = batch["inputs"]
logging.info(f"features shape: {features.shape}")
y = graph_compiler.texts_to_ids(supervisions["text"])
num_tokens = sum(len(i) for i in y)
logging.info(f"num tokens: {num_tokens}")
def scan_pessimistic_batches_for_oom(
model: nn.Module,
train_dl: torch.utils.data.DataLoader,
optimizer: torch.optim.Optimizer,
graph_compiler: BpeCtcTrainingGraphCompiler,
params: AttributeDict,
):
from lhotse.dataset import find_pessimistic_batches
logging.info(
"Sanity check -- see if any of the batches in epoch 0 would cause OOM."
)
batches, crit_values = find_pessimistic_batches(train_dl.sampler)
for criterion, cuts in batches.items():
batch = train_dl.dataset[cuts]
try:
optimizer.zero_grad()
loss, _ = compute_loss(
params=params,
model=model,
batch=batch,
graph_compiler=graph_compiler,
is_training=True,
)
loss.backward()
clip_grad_norm_(model.parameters(), 5.0, 2.0)
optimizer.step()
except RuntimeError as e:
if "CUDA out of memory" in str(e):
logging.error(
"Your GPU ran out of memory with the current "
"max_duration setting. We recommend decreasing "
"max_duration and trying again.\n"
f"Failing criterion: {criterion} "
f"(={crit_values[criterion]}) ..."
)
raise
def main():
parser = get_parser()
LibriSpeechAsrDataModule.add_arguments(parser)
args = parser.parse_args()
args.exp_dir = Path(args.exp_dir)
args.lang_dir = Path(args.lang_dir)
world_size = args.world_size
assert world_size >= 1
if world_size > 1:
mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
else:
run(rank=0, world_size=1, args=args)
torch.set_num_threads(1)
torch.set_num_interop_threads(1)
if __name__ == "__main__":
main()
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