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Remove train_phones.py and modify train.py instead

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This commit is contained in:
Piotr Żelasko 2022-01-21 17:08:53 -05:00
parent 8eb94fa4a0
commit 3d109b121d
3 changed files with 38 additions and 757 deletions
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56
egs/librispeech/ASR/conformer_ctc/train.py
View File

@ -38,6 +38,7 @@ from torch.utils.tensorboard import SummaryWriter
from transformer import Noam
from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
from icefall.graph_compiler import CtcTrainingGraphCompiler
from icefall.checkpoint import load_checkpoint
from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
from icefall.dist import cleanup_dist, setup_dist
@ -350,9 +351,15 @@ def compute_loss(
supervisions, subsampling_factor=params.subsampling_factor
)
token_ids = graph_compiler.texts_to_ids(texts)
decoding_graph = graph_compiler.compile(token_ids)
if isinstance(graph_compiler, BpeCtcTrainingGraphCompiler):
# Works with a BPE model
token_ids = graph_compiler.texts_to_ids(texts)
decoding_graph = graph_compiler.compile(token_ids)
elif isinstance(graph_compiler, CtcTrainingGraphCompiler):
# Works with a phone lexicon
decoding_graph = graph_compiler.compile(texts)
else:
raise ValueError(f"Unsupported type of graph compiler: {type(graph_compiler)}")
dense_fsa_vec = k2.DenseFsaVec(
nnet_output,
@ -379,9 +386,7 @@ def compute_loss(
#
# See https://github.com/k2-fsa/icefall/issues/97
# for more details
unsorted_token_ids = graph_compiler.texts_to_ids(
supervisions["text"]
)
unsorted_token_ids = graph_compiler.texts_to_ids(supervisions["text"])
att_loss = mmodel.decoder_forward(
encoder_memory,
memory_mask,
@ -514,9 +519,7 @@ def train_one_epoch(
loss_info.write_summary(
tb_writer, "train/current_", params.batch_idx_train
)
tot_loss.write_summary(
tb_writer, "train/tot_", params.batch_idx_train
)
tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
if batch_idx > 0 and batch_idx % params.valid_interval == 0:
logging.info("Computing validation loss")
@ -577,12 +580,27 @@ def run(rank, world_size, args):
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>",
)
if "lang_bpe" in params.lang_dir:
graph_compiler = BpeCtcTrainingGraphCompiler(
params.lang_dir,
device=device,
sos_token="<sos/eos>",
eos_token="<sos/eos>",
)
elif "lang_phone" in params.lang_dir:
graph_compiler = CtcTrainingGraphCompiler(
lexicon,
device=device,
)
# Manually add the sos/eos ID with their default values
# from the BPE recipe which we're adapting here.
graph_compiler.sos_id = 1
graph_compiler.eos_id = 1
else:
raise ValueError(
f"Unsupported type of lang dir (we expected it to have 'lang_bpe' or 'lang_phone' "
f"in its name): {params.lang_dir}"
)
logging.info("About to create model")
model = Conformer(
@ -600,7 +618,9 @@ def run(rank, world_size, args):
model.to(device)
if world_size > 1:
model = DDP(model, device_ids=[rank])
# Note: find_unused_parameters=True is needed in case we
# want to set params.att_rate = 0 (i.e. att decoder is not trained)
model = DDP(model, device_ids=[rank], find_unused_parameters=True)
optimizer = Noam(
model.parameters(),
@ -630,9 +650,7 @@ def run(rank, world_size, args):
cur_lr = optimizer._rate
if tb_writer is not None:
tb_writer.add_scalar(
"train/learning_rate", cur_lr, params.batch_idx_train
)
tb_writer.add_scalar("train/learning_rate", cur_lr, params.batch_idx_train)
tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
if rank == 0:

735
egs/librispeech/ASR/conformer_ctc/train_phones.py
View File

@ -1,735 +0,0 @@
#!/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.
import argparse
import logging
from pathlib import Path
from shutil import copyfile
from typing import Optional, Tuple
import k2
import torch
import torch.multiprocessing as mp
import torch.nn as nn
from asr_datamodule import LibriSpeechAsrDataModule
from conformer import Conformer
from lhotse.utils import fix_random_seed
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 transformer import Noam
from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
from icefall.graph_compiler import CtcTrainingGraphCompiler
from icefall.checkpoint import load_checkpoint
from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
from icefall.dist import cleanup_dist, setup_dist
from icefall.env import get_env_info
from icefall.lexicon import Lexicon
from icefall.utils import (
AttributeDict,
MetricsTracker,
encode_supervisions,
setup_logger,
str2bool,
)
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=35,
help="Number of epochs to train.",
)
parser.add_argument(
"--start-epoch",
type=int,
default=0,
help="""Resume training from from this epoch.
If it is positive, it will load checkpoint from
conformer_ctc/exp/epoch-{start_epoch-1}.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_phones",
help="""The lang dir
It contains language related input files such as
"lexicon.txt"
""",
)
parser.add_argument(
"--att-rate",
type=float,
default=0,
help="""The attention rate.
The total loss is (1 - att_rate) * ctc_loss + att_rate * att_loss
(disabled by default; not supported yet)
""",
)
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: Whether to do batch normalization for the
input features.
- 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.
- lr_factor: The lr_factor for Noam optimizer.
- warm_step: The warm_step for Noam optimizer.
"""
params = AttributeDict(
{
"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,
# parameters for conformer
"feature_dim": 80,
"subsampling_factor": 4,
"use_feat_batchnorm": True,
"attention_dim": 512,
"nhead": 8,
"num_decoder_layers": 6,
# parameters for loss
"beam_size": 10,
"reduction": "sum",
"use_double_scores": True,
# parameters for Noam
"weight_decay": 1e-6,
"lr_factor": 3.0,
"warm_step": 80000,
"env_info": get_env_info(),
}
)
return params
def load_checkpoint_if_available(
params: AttributeDict,
model: nn.Module,
optimizer: Optional[torch.optim.Optimizer] = None,
scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
) -> None:
"""Load checkpoint from file.
If params.start_epoch is positive, it will load the checkpoint from
`params.start_epoch - 1`. Otherwise, this function does nothing.
Apart from loading state dict for `model`, `optimizer` and `scheduler`,
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.
optimizer:
The optimizer that we are using.
scheduler:
The learning rate scheduler we are using.
Returns:
Return None.
"""
if params.start_epoch <= 0:
return
filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
saved_params = load_checkpoint(
filename,
model=model,
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]
return saved_params
def save_checkpoint(
params: AttributeDict,
model: nn.Module,
optimizer: Optional[torch.optim.Optimizer] = None,
scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = 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.
"""
if rank != 0:
return
filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
save_checkpoint_impl(
filename=filename,
model=model,
params=params,
optimizer=optimizer,
scheduler=scheduler,
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"]
with torch.set_grad_enabled(is_training):
nnet_output, encoder_memory, memory_mask = model(feature, supervisions)
# nnet_output is (N, T, C)
# NOTE: We need `encode_supervisions` to sort sequences with
# different duration in decreasing order, required by
# `k2.intersect_dense` called in `k2.ctc_loss`
supervision_segments, texts = encode_supervisions(
supervisions, subsampling_factor=params.subsampling_factor
)
token_ids = graph_compiler.texts_to_ids(texts)
decoding_graph = graph_compiler.compile(texts)
dense_fsa_vec = k2.DenseFsaVec(
nnet_output,
supervision_segments,
allow_truncate=params.subsampling_factor - 1,
)
ctc_loss = k2.ctc_loss(
decoding_graph=decoding_graph,
dense_fsa_vec=dense_fsa_vec,
output_beam=params.beam_size,
reduction=params.reduction,
use_double_scores=params.use_double_scores,
)
if params.att_rate != 0.0:
with torch.set_grad_enabled(is_training):
mmodel = model.module if hasattr(model, "module") else model
# Note: We need to generate an unsorted version of token_ids
# `encode_supervisions()` called above sorts text, but
# encoder_memory and memory_mask are not sorted, so we
# use an unsorted version `supervisions["text"]` to regenerate
# the token_ids
#
# See https://github.com/k2-fsa/icefall/issues/97
# for more details
unsorted_token_ids = graph_compiler.texts_to_ids(
supervisions["text"]
)
att_loss = mmodel.decoder_forward(
encoder_memory,
memory_mask,
token_ids=unsorted_token_ids,
sos_id=graph_compiler.sos_id,
eos_id=graph_compiler.eos_id,
)
loss = (1.0 - params.att_rate) * ctc_loss + params.att_rate * att_loss
else:
loss = ctc_loss
att_loss = torch.tensor([0])
assert loss.requires_grad == is_training
info = MetricsTracker()
info["frames"] = supervision_segments[:, 2].sum().item()
info["ctc_loss"] = ctc_loss.detach().cpu().item()
if params.att_rate != 0.0:
info["att_loss"] = att_loss.detach().cpu().item()
info["loss"] = loss.detach().cpu().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: nn.Module,
optimizer: torch.optim.Optimizer,
graph_compiler: BpeCtcTrainingGraphCompiler,
train_dl: torch.utils.data.DataLoader,
valid_dl: torch.utils.data.DataLoader,
tb_writer: Optional[SummaryWriter] = None,
world_size: int = 1,
) -> 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"])
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.
optimizer.zero_grad()
loss.backward()
clip_grad_norm_(model.parameters(), 5.0, 2.0)
optimizer.step()
if batch_idx % params.log_interval == 0:
logging.info(
f"Epoch {params.cur_epoch}, "
f"batch {batch_idx}, loss[{loss_info}], "
f"tot_loss[{tot_loss}], batch size: {batch_size}"
)
if batch_idx % params.log_interval == 0:
if tb_writer is not None:
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 batch_idx > 0 and batch_idx % params.valid_interval == 0:
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}")
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(42)
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
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", rank)
graph_compiler = CtcTrainingGraphCompiler(
lexicon,
device=device,
)
# Manually add the sos/eos ID with their default values
# from the BPE recipe which we're adapting here.
graph_compiler.sos_id = 1
graph_compiler.eos_id = 1
logging.info("About to create model")
model = Conformer(
num_features=params.feature_dim,
nhead=params.nhead,
d_model=params.attention_dim,
num_classes=num_classes,
subsampling_factor=params.subsampling_factor,
num_decoder_layers=params.num_decoder_layers,
vgg_frontend=False,
use_feat_batchnorm=params.use_feat_batchnorm,
)
checkpoints = load_checkpoint_if_available(params=params, model=model)
model.to(device)
if world_size > 1:
# Note: find_unused_parameters=True is needed because we're
# not training decoder at all by default (for now).
model = DDP(model, device_ids=[rank], find_unused_parameters=True)
optimizer = Noam(
model.parameters(),
model_size=params.attention_dim,
factor=params.lr_factor,
warm_step=params.warm_step,
weight_decay=params.weight_decay,
)
if checkpoints:
optimizer.load_state_dict(checkpoints["optimizer"])
librispeech = LibriSpeechAsrDataModule(args)
train_dl = librispeech.train_dataloaders()
valid_dl = librispeech.valid_dataloaders()
scan_pessimistic_batches_for_oom(
model=model,
train_dl=train_dl,
optimizer=optimizer,
graph_compiler=graph_compiler,
params=params,
)
for epoch in range(params.start_epoch, params.num_epochs):
train_dl.sampler.set_epoch(epoch)
cur_lr = optimizer._rate
if tb_writer is not None:
tb_writer.add_scalar(
"train/learning_rate", cur_lr, params.batch_idx_train
)
tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
if rank == 0:
logging.info("epoch {}, learning rate {}".format(epoch, cur_lr))
params.cur_epoch = epoch
train_one_epoch(
params=params,
model=model,
optimizer=optimizer,
graph_compiler=graph_compiler,
train_dl=train_dl,
valid_dl=valid_dl,
tb_writer=tb_writer,
world_size=world_size,
)
save_checkpoint(
params=params,
model=model,
optimizer=optimizer,
rank=rank,
)
logging.info("Done!")
if world_size > 1:
torch.distributed.barrier()
cleanup_dist()
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)
# Workaround to see logs with some pytorch versions...
logging.info = logging.warning
torch.set_num_threads(1)
torch.set_num_interop_threads(1)
if __name__ == "__main__":
main()

4
icefall/graph_compiler.py
View File

@ -75,9 +75,7 @@ class CtcTrainingGraphCompiler(object):
# NOTE: k2.compose runs on CUDA only when treat_epsilons_specially
# is False, so we add epsilon self-loops here
fsa_with_self_loops = k2.remove_epsilon_and_add_self_loops(
transcript_fsa
)
fsa_with_self_loops = k2.remove_epsilon_and_add_self_loops(transcript_fsa)
fsa_with_self_loops = k2.arc_sort(fsa_with_self_loops)