"""Abstract task module."""
import argparse
import functools
import logging
import os
import sys
from abc import ABC, abstractmethod
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import humanfriendly
import numpy as np
import torch
import torch.multiprocessing
import torch.nn
import torch.optim
import yaml
from packaging.version import parse as V
from torch.utils.data import DataLoader
from typeguard import typechecked
from espnet import __version__
from espnet2.iterators.abs_iter_factory import AbsIterFactory
from espnet2.iterators.category_iter_factory import CategoryIterFactory
from espnet2.iterators.chunk_iter_factory import ChunkIterFactory
from espnet2.iterators.multiple_iter_factory import MultipleIterFactory
from espnet2.iterators.sequence_iter_factory import SequenceIterFactory
from espnet2.layers.create_adapter import create_adapter
from espnet2.main_funcs.collect_stats import collect_stats
from espnet2.optimizers.optim_groups import configure_optimizer
from espnet2.optimizers.sgd import SGD
from espnet2.samplers.build_batch_sampler import BATCH_TYPES, build_batch_sampler
from espnet2.samplers.category_balanced_sampler import CategoryBalancedSampler
from espnet2.samplers.unsorted_batch_sampler import UnsortedBatchSampler
from espnet2.schedulers.cosine_anneal_warmup_restart import (
CosineAnnealingWarmupRestarts,
)
from espnet2.schedulers.noam_lr import NoamLR
from espnet2.schedulers.piecewise_linear_warmup_lr import PiecewiseLinearWarmupLR
from espnet2.schedulers.warmup_lr import WarmupLR
from espnet2.schedulers.warmup_reducelronplateau import WarmupReduceLROnPlateau
from espnet2.schedulers.warmup_step_lr import WarmupStepLR
from espnet2.torch_utils.load_pretrained_model import load_pretrained_model
from espnet2.torch_utils.model_summary import model_summary
from espnet2.torch_utils.pytorch_version import pytorch_cudnn_version
from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
from espnet2.train.abs_espnet_model import AbsESPnetModel
from espnet2.train.class_choices import ClassChoices
from espnet2.train.dataset import DATA_TYPES, AbsDataset, ESPnetDataset
from espnet2.train.distributed_utils import (
DistributedOption,
free_port,
get_master_port,
get_node_rank,
get_num_nodes,
resolve_distributed_mode,
)
from espnet2.train.iterable_dataset import IterableESPnetDataset
from espnet2.train.trainer import Trainer
from espnet2.utils import config_argparse
from espnet2.utils.build_dataclass import build_dataclass
from espnet2.utils.get_default_kwargs import get_default_kwargs
from espnet2.utils.nested_dict_action import NestedDictAction
from espnet2.utils.types import (
humanfriendly_parse_size_or_none,
int_or_none,
str2bool,
str2triple_str,
str_or_int,
str_or_none,
)
from espnet2.utils.yaml_no_alias_safe_dump import yaml_no_alias_safe_dump
from espnet.utils.cli_utils import get_commandline_args
try:
import wandb
except Exception:
wandb = None
if V(torch.__version__) >= V("1.5.0"):
from torch.multiprocessing.spawn import ProcessContext
else:
from torch.multiprocessing.spawn import SpawnContext as ProcessContext
optim_classes = dict(
adam=torch.optim.Adam,
adamw=torch.optim.AdamW,
sgd=SGD,
adadelta=torch.optim.Adadelta,
adagrad=torch.optim.Adagrad,
adamax=torch.optim.Adamax,
asgd=torch.optim.ASGD,
lbfgs=torch.optim.LBFGS,
rmsprop=torch.optim.RMSprop,
rprop=torch.optim.Rprop,
)
if V(torch.__version__) >= V("1.10.0"):
# From 1.10.0, RAdam is officially supported
optim_classes.update(
radam=torch.optim.RAdam,
)
try:
import torch_optimizer
optim_classes.update(
accagd=torch_optimizer.AccSGD,
adabound=torch_optimizer.AdaBound,
adamod=torch_optimizer.AdaMod,
diffgrad=torch_optimizer.DiffGrad,
lamb=torch_optimizer.Lamb,
novograd=torch_optimizer.NovoGrad,
pid=torch_optimizer.PID,
# torch_optimizer<=0.0.1a10 doesn't support
# qhadam=torch_optimizer.QHAdam,
qhm=torch_optimizer.QHM,
sgdw=torch_optimizer.SGDW,
yogi=torch_optimizer.Yogi,
)
if V(torch_optimizer.__version__) < V("0.2.0"):
# From 0.2.0, RAdam is dropped
optim_classes.update(
radam=torch_optimizer.RAdam,
)
del torch_optimizer
except ImportError:
pass
try:
import apex
optim_classes.update(
fusedadam=apex.optimizers.FusedAdam,
fusedlamb=apex.optimizers.FusedLAMB,
fusednovograd=apex.optimizers.FusedNovoGrad,
fusedsgd=apex.optimizers.FusedSGD,
)
del apex
except ImportError:
pass
try:
import fairscale
except ImportError:
fairscale = None
scheduler_classes = dict(
ReduceLROnPlateau=torch.optim.lr_scheduler.ReduceLROnPlateau,
lambdalr=torch.optim.lr_scheduler.LambdaLR,
steplr=torch.optim.lr_scheduler.StepLR,
multisteplr=torch.optim.lr_scheduler.MultiStepLR,
exponentiallr=torch.optim.lr_scheduler.ExponentialLR,
CosineAnnealingLR=torch.optim.lr_scheduler.CosineAnnealingLR,
noamlr=NoamLR,
warmuplr=WarmupLR,
piecewiselinearwarmuplr=PiecewiseLinearWarmupLR,
warmupsteplr=WarmupStepLR,
warmupReducelronplateau=WarmupReduceLROnPlateau,
cycliclr=torch.optim.lr_scheduler.CyclicLR,
onecyclelr=torch.optim.lr_scheduler.OneCycleLR,
CosineAnnealingWarmRestarts=torch.optim.lr_scheduler.CosineAnnealingWarmRestarts,
CosineAnnealingWarmupRestarts=CosineAnnealingWarmupRestarts,
)
# To lower keys
optim_classes = {k.lower(): v for k, v in optim_classes.items()}
scheduler_classes = {k.lower(): v for k, v in scheduler_classes.items()}
CONFIG_REPLACE_MAP = [
("text_cleaner", "cleaner"),
("g2p_type", "g2p"),
("aux_task_names", "aux_ctc_tasks"),
]
[docs]@dataclass
class IteratorOptions:
preprocess_fn: callable
collate_fn: callable
data_path_and_name_and_type: list
shape_files: list
batch_size: int
batch_bins: int
batch_type: str
max_cache_size: float
max_cache_fd: int
allow_multi_rates: bool
distributed: bool
num_batches: Optional[int]
num_iters_per_epoch: Optional[int]
train: bool
[docs]class AbsTask(ABC):
# Use @staticmethod, or @classmethod,
# instead of instance method to avoid God classes
# If you need more than one optimizers, change this value in inheritance
num_optimizers: int = 1
trainer = Trainer
class_choices_list: List[ClassChoices] = []
def __init__(self):
raise RuntimeError("This class can't be instantiated.")
[docs] @classmethod
@abstractmethod
def add_task_arguments(cls, parser: argparse.ArgumentParser):
pass
[docs] @classmethod
@abstractmethod
def build_collate_fn(
cls, args: argparse.Namespace, train: bool
) -> Callable[[Sequence[Dict[str, np.ndarray]]], Dict[str, torch.Tensor]]:
"""Return "collate_fn", which is a callable object and given to DataLoader.
>>> from torch.utils.data import DataLoader
>>> loader = DataLoader(collate_fn=cls.build_collate_fn(args, train=True), ...)
In many cases, you can use our common collate_fn.
"""
raise NotImplementedError
[docs] @classmethod
@abstractmethod
def build_preprocess_fn(
cls, args: argparse.Namespace, train: bool
) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]:
raise NotImplementedError
[docs] @classmethod
@abstractmethod
def required_data_names(
cls, train: bool = True, inference: bool = False
) -> Tuple[str, ...]:
"""Define the required names by Task
This function is used by
>>> cls.check_task_requirements()
If your model is defined as following,
>>> from espnet2.train.abs_espnet_model import AbsESPnetModel
>>> class Model(AbsESPnetModel):
... def forward(self, input, output, opt=None): pass
then "required_data_names" should be as
>>> required_data_names = ('input', 'output')
"""
raise NotImplementedError
[docs] @classmethod
@abstractmethod
def optional_data_names(
cls, train: bool = True, inference: bool = False
) -> Tuple[str, ...]:
"""Define the optional names by Task
This function is used by
>>> cls.check_task_requirements()
If your model is defined as follows,
>>> from espnet2.train.abs_espnet_model import AbsESPnetModel
>>> class Model(AbsESPnetModel):
... def forward(self, input, output, opt=None): pass
then "optional_data_names" should be as
>>> optional_data_names = ('opt',)
"""
raise NotImplementedError
[docs] @classmethod
@abstractmethod
def build_model(cls, args: argparse.Namespace) -> AbsESPnetModel:
raise NotImplementedError
[docs] @classmethod
@typechecked
def get_parser(cls) -> config_argparse.ArgumentParser:
class ArgumentDefaultsRawTextHelpFormatter(
argparse.RawTextHelpFormatter,
argparse.ArgumentDefaultsHelpFormatter,
):
pass
parser = config_argparse.ArgumentParser(
description="base parser",
formatter_class=ArgumentDefaultsRawTextHelpFormatter,
)
# NOTE(kamo): Use '_' instead of '-' to avoid confusion.
# I think '-' looks really confusing if it's written in yaml.
# NOTE(kamo): add_arguments(..., required=True) can't be used
# to provide --print_config mode. Instead of it, do as
parser.set_defaults(required=["output_dir"])
group = parser.add_argument_group("Common configuration")
group.add_argument(
"--print_config",
action="store_true",
help="Print the config file and exit",
)
group.add_argument(
"--log_level",
type=lambda x: x.upper(),
default="INFO",
choices=("ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
help="The verbose level of logging",
)
group.add_argument(
"--drop_last_iter",
type=str2bool,
default=False,
help="Exclude the minibatch with leftovers.",
)
group.add_argument(
"--dry_run",
type=str2bool,
default=False,
help="Perform process without training",
)
group.add_argument(
"--iterator_type",
type=str,
choices=["sequence", "category", "chunk", "task", "none"],
default="sequence",
help="Specify iterator type",
)
group.add_argument(
"--valid_iterator_type",
type=str,
choices=["sequence", "category", "chunk", "task", "none"],
default=None,
help="Specify iterator type",
)
group.add_argument("--output_dir", type=str_or_none, default=None)
group.add_argument(
"--ngpu",
type=int,
default=0,
help="The number of gpus. 0 indicates CPU mode",
)
group.add_argument("--seed", type=int, default=0, help="Random seed")
group.add_argument(
"--num_workers",
type=int,
default=1,
help="The number of workers used for DataLoader",
)
group.add_argument(
"--num_att_plot",
type=int,
default=3,
help="The number images to plot the outputs from attention. "
"This option makes sense only when attention-based model. "
"We can also disable the attention plot by setting it 0",
)
group = parser.add_argument_group("distributed training related")
group.add_argument(
"--dist_backend",
default="nccl",
type=str,
help="distributed backend",
)
group.add_argument(
"--dist_init_method",
type=str,
default="env://",
help='if init_method="env://", env values of "MASTER_PORT", "MASTER_ADDR", '
'"WORLD_SIZE", and "RANK" are referred.',
)
group.add_argument(
"--dist_world_size",
default=None,
type=int_or_none,
help="number of nodes for distributed training",
)
group.add_argument(
"--dist_rank",
type=int_or_none,
default=None,
help="node rank for distributed training",
)
group.add_argument(
# Not starting with "dist_" for compatibility to launch.py
"--local_rank",
type=int_or_none,
default=None,
help="local rank for distributed training. This option is used if "
"--multiprocessing_distributed=false",
)
group.add_argument(
"--dist_master_addr",
default=None,
type=str_or_none,
help="The master address for distributed training. "
"This value is used when dist_init_method == 'env://'",
)
group.add_argument(
"--dist_master_port",
default=None,
type=int_or_none,
help="The master port for distributed training"
"This value is used when dist_init_method == 'env://'",
)
group.add_argument(
"--dist_launcher",
default=None,
type=str_or_none,
choices=["slurm", "mpi", None],
help="The launcher type for distributed training",
)
group.add_argument(
"--multiprocessing_distributed",
default=False,
type=str2bool,
help="Use multi-processing distributed training to launch "
"N processes per node, which has N GPUs. This is the "
"fastest way to use PyTorch for either single node or "
"multi node data parallel training",
)
group.add_argument(
"--unused_parameters",
type=str2bool,
default=False,
help="Whether to use the find_unused_parameters in "
"torch.nn.parallel.DistributedDataParallel ",
)
group.add_argument(
"--sharded_ddp",
default=False,
type=str2bool,
help="Enable sharded training provided by fairscale",
)
group = parser.add_argument_group("cudnn mode related")
group.add_argument(
"--cudnn_enabled",
type=str2bool,
default=torch.backends.cudnn.enabled,
help="Enable CUDNN",
)
group.add_argument(
"--cudnn_benchmark",
type=str2bool,
default=torch.backends.cudnn.benchmark,
help="Enable cudnn-benchmark mode",
)
group.add_argument(
"--cudnn_deterministic",
type=str2bool,
default=True,
help="Enable cudnn-deterministic mode",
)
group = parser.add_argument_group("collect stats mode related")
group.add_argument(
"--collect_stats",
type=str2bool,
default=False,
help='Perform on "collect stats" mode',
)
group.add_argument(
"--write_collected_feats",
type=str2bool,
default=False,
help='Write the output features from the model when "collect stats" mode',
)
group = parser.add_argument_group("Trainer related")
group.add_argument(
"--max_epoch",
type=int,
default=40,
help="The maximum number epoch to train",
)
group.add_argument(
"--patience",
type=int_or_none,
default=None,
help="Number of epochs to wait without improvement "
"before stopping the training",
)
group.add_argument(
"--val_scheduler_criterion",
type=str,
nargs=2,
default=("valid", "loss"),
help="The criterion used for the value given to the lr scheduler. "
'Give a pair referring the phase, "train" or "valid",'
'and the criterion name. The mode specifying "min" or "max" can '
"be changed by --scheduler_conf",
)
group.add_argument(
"--early_stopping_criterion",
type=str,
nargs=3,
default=("valid", "loss", "min"),
help="The criterion used for judging of early stopping. "
'Give a pair referring the phase, "train" or "valid",'
'the criterion name and the mode, "min" or "max", e.g. "acc,max".',
)
group.add_argument(
"--best_model_criterion",
type=str2triple_str,
nargs="+",
default=[
("train", "loss", "min"),
("valid", "loss", "min"),
("train", "acc", "max"),
("valid", "acc", "max"),
],
help="The criterion used for judging of the best model. "
'Give a pair referring the phase, "train" or "valid",'
'the criterion name, and the mode, "min" or "max", e.g. "acc,max".',
)
group.add_argument(
"--keep_nbest_models",
type=int,
nargs="+",
default=[10],
help="Remove previous snapshots excluding the n-best scored epochs",
)
group.add_argument(
"--nbest_averaging_interval",
type=int,
default=0,
help="The epoch interval to apply model averaging and save nbest models",
)
group.add_argument(
"--grad_clip",
type=float,
default=5.0,
help="Gradient norm threshold to clip",
)
group.add_argument(
"--grad_clip_type",
type=float,
default=2.0,
help="The type of the used p-norm for gradient clip. Can be inf",
)
group.add_argument(
"--grad_noise",
type=str2bool,
default=False,
help="The flag to switch to use noise injection to "
"gradients during training",
)
group.add_argument(
"--accum_grad",
type=int,
default=1,
help="The number of gradient accumulation",
)
group.add_argument(
"--no_forward_run",
type=str2bool,
default=False,
help="Just only iterating data loading without "
"model forwarding and training",
)
group.add_argument(
"--resume",
type=str2bool,
default=False,
help="Enable resuming if checkpoint is existing",
)
group.add_argument(
"--train_dtype",
default="float32",
choices=["float16", "float32", "float64"],
help="Data type for training.",
)
group.add_argument(
"--use_amp",
type=str2bool,
default=False,
help="Enable Automatic Mixed Precision. This feature requires pytorch>=1.6",
)
group.add_argument(
"--log_interval",
type=int_or_none,
default=None,
help="Show the logs every the number iterations in each epochs at the "
"training phase. If None is given, it is decided according the number "
"of training samples automatically .",
)
group.add_argument(
"--use_matplotlib",
type=str2bool,
default=True,
help="Enable matplotlib logging",
)
group.add_argument(
"--use_tensorboard",
type=str2bool,
default=True,
help="Enable tensorboard logging",
)
group.add_argument(
"--create_graph_in_tensorboard",
type=str2bool,
default=False,
help="Whether to create graph in tensorboard",
)
group.add_argument(
"--use_wandb",
type=str2bool,
default=False,
help="Enable wandb logging",
)
group.add_argument(
"--wandb_project",
type=str,
default=None,
help="Specify wandb project",
)
group.add_argument(
"--wandb_id",
type=str,
default=None,
help="Specify wandb id",
)
group.add_argument(
"--wandb_entity",
type=str,
default=None,
help="Specify wandb entity",
)
group.add_argument(
"--wandb_name",
type=str,
default=None,
help="Specify wandb run name",
)
group.add_argument(
"--wandb_model_log_interval",
type=int,
default=-1,
help="Set the model log period",
)
group.add_argument(
"--detect_anomaly",
type=str2bool,
default=False,
help="Set torch.autograd.set_detect_anomaly",
)
group.add_argument(
"--use_adapter",
type=str2bool,
default=False,
help="Enable efficient finetuning, see (https://arxiv.org/abs/2106.09685) "
"for large pre-trained foundation models, like Whisper and SSL models",
)
group.add_argument(
"--adapter",
type=str,
default="lora",
help="Adapter Name",
choices=["lora", "houlsby"],
)
group.add_argument(
"--save_strategy",
type=str,
default="all",
help="The strategy to save parameters. Default: 'all' \n"
"'all': save all parameters\n"
"'adapter_only': save only adapter parameters,"
" without other parameters like downstream model\n"
"'required_grad_only': save only parameters with requires_grad=True\n",
choices=["all", "adapter_only", "required_grad_only"],
)
group.add_argument(
"--adapter_conf",
action=NestedDictAction,
default=dict(),
help="Configuration for efficient finetuning",
)
group = parser.add_argument_group("Pretraining model related")
group.add_argument("--pretrain_path", help="This option is obsoleted")
group.add_argument(
"--init_param",
type=str,
default=[],
nargs="*",
help="Specify the file path used for initialization of parameters. "
"The format is '<file_path>:<src_key>:<dst_key>:<exclude_keys>', "
"where file_path is the model file path, "
"src_key specifies the key of model states to be used in the model file, "
"dst_key specifies the attribute of the model to be initialized, "
"and exclude_keys excludes keys of model states for the initialization."
"e.g.\n"
" # Load all parameters"
" --init_param some/where/model.pth\n"
" # Load only decoder parameters"
" --init_param some/where/model.pth:decoder:decoder\n"
" # Load only decoder parameters excluding decoder.embed"
" --init_param some/where/model.pth:decoder:decoder:decoder.embed\n"
" --init_param some/where/model.pth:decoder:decoder:decoder.embed\n",
)
group.add_argument(
"--ignore_init_mismatch",
type=str2bool,
default=False,
help="Ignore size mismatch when loading pre-trained model",
)
group.add_argument(
"--freeze_param",
type=str,
default=[],
nargs="*",
help="Freeze parameters",
)
group = parser.add_argument_group("BatchSampler related")
group.add_argument(
"--num_iters_per_epoch",
type=int_or_none,
default=None,
help="Restrict the number of iterations for training per epoch",
)
group.add_argument(
"--batch_size",
type=int,
default=20,
help="The mini-batch size used for training. Used if batch_type='unsorted',"
" 'sorted', or 'folded', or 'catbel'.",
)
group.add_argument(
"--valid_batch_size",
type=int_or_none,
default=None,
help="If not given, the value of --batch_size is used",
)
group.add_argument(
"--batch_bins",
type=int,
default=1000000,
help="The number of batch bins. Used if batch_type='length' or 'numel'",
)
group.add_argument(
"--valid_batch_bins",
type=int_or_none,
default=None,
help="If not given, the value of --batch_bins is used",
)
group.add_argument("--train_shape_file", type=str, action="append", default=[])
group.add_argument("--valid_shape_file", type=str, action="append", default=[])
group = parser.add_argument_group("Sequence iterator related")
_batch_type_help = ""
for key, value in BATCH_TYPES.items():
_batch_type_help += f'"{key}":\n{value}\n'
group.add_argument(
"--batch_type",
type=str,
default="folded",
choices=list(BATCH_TYPES),
help=_batch_type_help,
)
group.add_argument(
"--valid_batch_type",
type=str_or_none,
default=None,
choices=list(BATCH_TYPES) + [None],
help="If not given, the value of --batch_type is used",
)
group.add_argument("--fold_length", type=int, action="append", default=[])
group.add_argument(
"--sort_in_batch",
type=str,
default="descending",
choices=["descending", "ascending"],
help="Sort the samples in each mini-batches by the sample "
'lengths. To enable this, "shape_file" must have the length information.',
)
group.add_argument(
"--shuffle_within_batch",
type=str2bool,
default=False,
help="Shuffles wholes batches in sample-wise. Required for"
"Classification tasks normally.",
)
group.add_argument(
"--sort_batch",
type=str,
default="descending",
choices=["descending", "ascending"],
help="Sort mini-batches by the sample lengths",
)
group.add_argument(
"--multiple_iterator",
type=str2bool,
default=False,
help="Use multiple iterator mode",
)
group = parser.add_argument_group("Chunk iterator related")
group.add_argument(
"--chunk_length",
type=str_or_int,
default=500,
help="Specify chunk length. e.g. '300', '300,400,500', or '300-400'."
"If multiple numbers separated by command are given, "
"one of them is selected randomly for each samples. "
"If two numbers are given with '-', it indicates the range of the choices. "
"Note that if the sequence length is shorter than the all chunk_lengths, "
"the sample is discarded. ",
)
group.add_argument(
"--chunk_shift_ratio",
type=float,
default=0.5,
help="Specify the shift width of chunks. If it's less than 1, "
"allows the overlapping and if bigger than 1, there are some gaps "
"between each chunk.",
)
group.add_argument(
"--num_cache_chunks",
type=int,
default=1024,
help="Shuffle in the specified number of chunks and generate mini-batches "
"More larger this value, more randomness can be obtained.",
)
group.add_argument(
"--chunk_excluded_key_prefixes",
type=str,
nargs="+",
default=[],
help="List of key prefixes. Keys that satisfy either condition below "
"will be excluded from the length consistency check in ChunkIterFactory:\n"
" - exactly match one of the prefixes in `chunk_excluded_key_prefixes`\n"
" - have one of the prefixes in `chunk_excluded_key_prefixes` and "
"end with numbers",
)
group.add_argument(
"--chunk_default_fs",
type=int_or_none,
default=None,
help="Default sampling rate used for the chunk length. Will be used to "
"adaptively adjust the chunk length for data of different sampling rates. "
"(If None, the chunk length will be fixed.)",
)
group = parser.add_argument_group("Dataset related")
_data_path_and_name_and_type_help = (
"Give three words splitted by comma. It's used for the training data. "
"e.g. '--train_data_path_and_name_and_type some/path/a.scp,foo,sound'. "
"The first value, some/path/a.scp, indicates the file path, "
"and the second, foo, is the key name used for the mini-batch data, "
"and the last, sound, decides the file type. "
"This option is repeatable, so you can input any number of features "
"for your task. Supported file types are as follows:\n\n"
)
for key, dic in DATA_TYPES.items():
_data_path_and_name_and_type_help += f'"{key}":\n{dic["help"]}\n\n'
group.add_argument(
"--train_data_path_and_name_and_type",
type=str2triple_str,
action="append",
default=[],
help=_data_path_and_name_and_type_help,
)
group.add_argument(
"--valid_data_path_and_name_and_type",
type=str2triple_str,
action="append",
default=[],
)
group.add_argument(
"--allow_variable_data_keys",
type=str2bool,
default=False,
help="Allow the arbitrary keys for mini-batch with ignoring "
"the task requirements",
)
group.add_argument(
"--max_cache_size",
type=humanfriendly.parse_size,
default=0.0,
help="The maximum cache size for data loader. e.g. 10MB, 20GB.",
)
group.add_argument(
"--max_cache_fd",
type=int,
default=32,
help="The maximum number of file descriptors to be kept "
"as opened for ark files. "
"This feature is only valid when data type is 'kaldi_ark'.",
)
group.add_argument(
"--allow_multi_rates",
type=str2bool,
default=False,
help="Whether to allow audios to have different sampling rates",
)
group.add_argument(
"--valid_max_cache_size",
type=humanfriendly_parse_size_or_none,
default=None,
help="The maximum cache size for validation data loader. e.g. 10MB, 20GB. "
"If None, the 5 percent size of --max_cache_size",
)
group = parser.add_argument_group("Optimizer related")
group.add_argument(
"--exclude_weight_decay",
type=str2bool,
default=False,
help="Exclude weight decay in optimizer for model bias, normalization, "
"or other special parameters",
)
group.add_argument(
"--exclude_weight_decay_conf",
action=NestedDictAction,
default=dict(),
help="The keyword arguments for configuring weight decay in optimizer. "
"e.g., 'bias_weight_decay': False will set zero weight decay for bias "
"params. See also espnet2.optimizers.optim_groups.configure_optimizer.",
)
for i in range(1, cls.num_optimizers + 1):
suf = "" if i == 1 else str(i)
group.add_argument(
f"--optim{suf}",
type=lambda x: x.lower(),
default="adadelta",
choices=list(optim_classes),
help="The optimizer type",
)
group.add_argument(
f"--optim{suf}_conf",
action=NestedDictAction,
default=dict(),
help="The keyword arguments for optimizer",
)
group.add_argument(
f"--scheduler{suf}",
type=lambda x: str_or_none(x.lower()),
default=None,
choices=list(scheduler_classes) + [None],
help="The lr scheduler type",
)
group.add_argument(
f"--scheduler{suf}_conf",
action=NestedDictAction,
default=dict(),
help="The keyword arguments for lr scheduler",
)
cls.trainer.add_arguments(parser)
cls.add_task_arguments(parser)
return parser
[docs] @classmethod
def build_optimizers(
cls,
args: argparse.Namespace,
model: torch.nn.Module,
) -> List[torch.optim.Optimizer]:
if cls.num_optimizers != 1:
raise RuntimeError(
"build_optimizers() must be overridden if num_optimizers != 1"
)
optim_class = optim_classes.get(args.optim)
if optim_class is None:
raise ValueError(f"must be one of {list(optim_classes)}: {args.optim}")
if args.sharded_ddp:
if fairscale is None:
raise RuntimeError("Requiring fairscale. Do 'pip install fairscale'")
optim = fairscale.optim.oss.OSS(
params=model.parameters(), optim=optim_class, **args.optim_conf
)
else:
if args.exclude_weight_decay:
optim = configure_optimizer(
model,
optim_class,
args.optim_conf,
args.exclude_weight_decay_conf,
)
else:
optim = optim_class(model.parameters(), **args.optim_conf)
optimizers = [optim]
return optimizers
[docs] @classmethod
def exclude_opts(cls) -> Tuple[str, ...]:
"""The options not to be shown by --print_config"""
return "required", "print_config", "config", "ngpu"
[docs] @classmethod
@typechecked
def get_default_config(cls) -> Dict[str, Any]:
"""Return the configuration as dict.
This method is used by print_config()
"""
def get_class_type(name: str, classes: dict):
_cls = classes.get(name)
if _cls is None:
raise ValueError(f"must be one of {list(classes)}: {name}")
return _cls
# This method is used only for --print_config
parser = cls.get_parser()
args, _ = parser.parse_known_args()
config = vars(args)
# Excludes the options not to be shown
for k in AbsTask.exclude_opts():
config.pop(k)
for i in range(1, cls.num_optimizers + 1):
suf = "" if i == 1 else str(i)
name = config[f"optim{suf}"]
optim_class = get_class_type(name, optim_classes)
conf = get_default_kwargs(optim_class)
# Overwrite the default by the arguments,
conf.update(config[f"optim{suf}_conf"])
# and set it again
config[f"optim{suf}_conf"] = conf
name = config[f"scheduler{suf}"]
if name is not None:
scheduler_class = get_class_type(name, scheduler_classes)
conf = get_default_kwargs(scheduler_class)
# Overwrite the default by the arguments,
conf.update(config[f"scheduler{suf}_conf"])
# and set it again
config[f"scheduler{suf}_conf"] = conf
for class_choices in cls.class_choices_list:
if getattr(args, class_choices.name) is not None:
class_obj = class_choices.get_class(getattr(args, class_choices.name))
conf = get_default_kwargs(class_obj)
# remove duplicate arguments
for k in CONFIG_REPLACE_MAP:
if k[0] in conf and k[1] in config:
conf.pop(k[0])
elif k[0] in conf:
conf[k[1]] = conf.pop(k[0])
remove_keys = []
for k in conf:
if k in config:
remove_keys.append(k)
for k in remove_keys:
conf.pop(k)
name = class_choices.name
# Overwrite the default by the arguments,
conf.update(config[f"{name}_conf"])
# and set it again
config[f"{name}_conf"] = conf
return config
[docs] @classmethod
@typechecked
def check_required_command_args(cls, args: argparse.Namespace):
for k in vars(args):
if "-" in k:
raise RuntimeError(f'Use "_" instead of "-": parser.get_parser("{k}")')
required = ", ".join(
f"--{a}" for a in args.required if getattr(args, a) is None
)
if len(required) != 0:
parser = cls.get_parser()
parser.print_help(file=sys.stderr)
p = Path(sys.argv[0]).name
print(file=sys.stderr)
print(
f"{p}: error: the following arguments are required: " f"{required}",
file=sys.stderr,
)
sys.exit(2)
[docs] @classmethod
@typechecked
def check_task_requirements(
cls,
dataset: Union[AbsDataset, IterableESPnetDataset],
allow_variable_data_keys: bool,
train: bool,
inference: bool = False,
) -> None:
"""Check if the dataset satisfy the requirement of current Task"""
mes = (
f"If you intend to use an additional input, modify "
f'"{cls.__name__}.required_data_names()" or '
f'"{cls.__name__}.optional_data_names()". '
f"Otherwise you need to set --allow_variable_data_keys true "
)
for k in cls.required_data_names(train, inference):
if not dataset.has_name(k):
raise RuntimeError(
f'"{cls.required_data_names(train, inference)}" are required for'
f' {cls.__name__}. but "{dataset.names()}" are input.\n{mes}'
)
if not allow_variable_data_keys:
task_keys = cls.required_data_names(
train, inference
) + cls.optional_data_names(train, inference)
for k in dataset.names():
if k not in task_keys:
raise RuntimeError(
f"The data-name must be one of {task_keys} "
f'for {cls.__name__}: "{k}" is not allowed.\n{mes}'
)
[docs] @classmethod
@typechecked
def print_config(cls, file=sys.stdout) -> None:
# Shows the config: e.g. python train.py asr --print_config
config = cls.get_default_config()
file.write(yaml_no_alias_safe_dump(config, indent=4, sort_keys=False))
[docs] @classmethod
@typechecked
def main(
cls,
args: Optional[argparse.Namespace] = None,
cmd: Optional[Sequence[str]] = None,
):
print(get_commandline_args(), file=sys.stderr)
if args is None:
parser = cls.get_parser()
args = parser.parse_args(cmd)
args.version = __version__
if args.pretrain_path is not None:
raise RuntimeError("--pretrain_path is deprecated. Use --init_param")
if args.print_config:
cls.print_config()
sys.exit(0)
cls.check_required_command_args(args)
# "distributed" is decided using the other command args
resolve_distributed_mode(args)
if not args.distributed or not args.multiprocessing_distributed:
cls.main_worker(args)
else:
assert args.ngpu > 1, args.ngpu
# Multi-processing distributed mode: e.g. 2node-4process-4GPU
# | Host1 | Host2 |
# | Process1 | Process2 | <= Spawn processes
# |Child1|Child2|Child1|Child2|
# |GPU1 |GPU2 |GPU1 |GPU2 |
# See also the following usage of --multiprocessing-distributed:
# https://github.com/pytorch/examples/blob/master/imagenet/main.py
num_nodes = get_num_nodes(args.dist_world_size, args.dist_launcher)
if num_nodes == 1:
args.dist_master_addr = "localhost"
args.dist_rank = 0
# Single node distributed training with multi-GPUs
if (
args.dist_init_method == "env://"
and get_master_port(args.dist_master_port) is None
):
# Get the unused port
args.dist_master_port = free_port()
# Assume that nodes use same number of GPUs each other
args.dist_world_size = args.ngpu * num_nodes
node_rank = get_node_rank(args.dist_rank, args.dist_launcher)
# The following block is copied from:
# https://github.com/pytorch/pytorch/blob/master/torch/multiprocessing/spawn.py
error_queues = []
processes = []
mp = torch.multiprocessing.get_context("spawn")
for i in range(args.ngpu):
# Copy args
local_args = argparse.Namespace(**vars(args))
local_args.local_rank = i
local_args.dist_rank = args.ngpu * node_rank + i
local_args.ngpu = 1
process = mp.Process(
target=cls.main_worker,
args=(local_args,),
daemon=False,
)
process.start()
processes.append(process)
error_queues.append(mp.SimpleQueue())
# Loop on join until it returns True or raises an exception.
while not ProcessContext(processes, error_queues).join():
pass
[docs] @classmethod
@typechecked
def main_worker(cls, args: argparse.Namespace):
# 0. Init distributed process
distributed_option = build_dataclass(DistributedOption, args)
# Setting distributed_option.dist_rank, etc.
distributed_option.init_options()
# NOTE(kamo): Don't use logging before invoking logging.basicConfig()
if not distributed_option.distributed or distributed_option.dist_rank == 0:
if not distributed_option.distributed:
_rank = ""
else:
_rank = (
f":{distributed_option.dist_rank}/"
f"{distributed_option.dist_world_size}"
)
# NOTE(kamo):
# logging.basicConfig() is invoked in main_worker() instead of main()
# because it can be invoked only once in a process.
# FIXME(kamo): Should we use logging.getLogger()?
logging.basicConfig(
level=args.log_level,
format=f"[{os.uname()[1].split('.')[0]}{_rank}]"
f" %(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
else:
# Suppress logging if RANK != 0
logging.basicConfig(
level="ERROR",
format=f"[{os.uname()[1].split('.')[0]}"
f":{distributed_option.dist_rank}/{distributed_option.dist_world_size}]"
f" %(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
# Invoking torch.distributed.init_process_group
distributed_option.init_torch_distributed()
# 1. Set random-seed
set_all_random_seed(args.seed)
torch.backends.cudnn.enabled = args.cudnn_enabled
torch.backends.cudnn.benchmark = args.cudnn_benchmark
torch.backends.cudnn.deterministic = args.cudnn_deterministic
if args.detect_anomaly:
logging.info("Invoking torch.autograd.set_detect_anomaly(True)")
torch.autograd.set_detect_anomaly(args.detect_anomaly)
if (
args.collect_stats
and getattr(args, "model_conf", None) is not None
and not args.model_conf.get("extract_feats_in_collect_stats", True)
):
model = None
logging.info("Skipping model building in collect_stats stage.")
else:
# 2. Build model
model = cls.build_model(args=args)
if not isinstance(model, AbsESPnetModel):
raise RuntimeError(
f"model must inherit {AbsESPnetModel.__name__},"
f" but got {type(model)}"
)
model = model.to(
dtype=getattr(torch, args.train_dtype),
device="cuda" if args.ngpu > 0 else "cpu",
)
for t in args.freeze_param:
for k, p in model.named_parameters():
if k.startswith(t + ".") or k == t:
logging.info(f"Setting {k}.requires_grad = False")
p.requires_grad = False
# Use adapter to finetune the large pre-trained foundation models
if getattr(args, "use_adapter", False):
create_adapter(model, args.adapter, args.adapter_conf)
# 3. Build optimizer
optimizers = cls.build_optimizers(args, model=model)
# 4. Build schedulers
schedulers = []
for i, optim in enumerate(optimizers, 1):
suf = "" if i == 1 else str(i)
name = getattr(args, f"scheduler{suf}")
conf = getattr(args, f"scheduler{suf}_conf")
if name is not None:
cls_ = scheduler_classes.get(name)
if cls_ is None:
raise ValueError(
f"must be one of {list(scheduler_classes)}: {name}"
)
scheduler = cls_(optim, **conf)
else:
scheduler = None
schedulers.append(scheduler)
logging.info(pytorch_cudnn_version())
logging.info(model_summary(model))
for i, (o, s) in enumerate(zip(optimizers, schedulers), 1):
suf = "" if i == 1 else str(i)
logging.info(f"Optimizer{suf}:\n{o}")
logging.info(f"Scheduler{suf}: {s}")
# 5. Dump "args" to config.yaml
# NOTE(kamo): "args" should be saved after object-buildings are done
# because they are allowed to modify "args".
output_dir = Path(args.output_dir)
if not distributed_option.distributed or distributed_option.dist_rank == 0:
output_dir.mkdir(parents=True, exist_ok=True)
with (output_dir / "config.yaml").open("w", encoding="utf-8") as f:
logging.info(
f'Saving the configuration in {output_dir / "config.yaml"}'
)
yaml_no_alias_safe_dump(vars(args), f, indent=4, sort_keys=False)
if args.dry_run:
pass
elif args.collect_stats:
# Perform on collect_stats mode. This mode has two roles
# - Derive the length and dimension of all input data
# - Accumulate feats, square values, and the length for whitening
logging.info(args)
if args.valid_batch_size is None:
args.valid_batch_size = args.batch_size
if len(args.train_shape_file) != 0:
train_key_file = args.train_shape_file[0]
else:
train_key_file = None
if len(args.valid_shape_file) != 0:
valid_key_file = args.valid_shape_file[0]
else:
valid_key_file = None
if model and not getattr(model, "extract_feats_in_collect_stats", True):
model = None
logging.info("Skipping collect_feats in collect_stats stage.")
collect_stats(
model=model,
train_iter=cls.build_streaming_iterator(
data_path_and_name_and_type=args.train_data_path_and_name_and_type,
key_file=train_key_file,
batch_size=args.batch_size,
dtype=args.train_dtype,
num_workers=args.num_workers,
allow_variable_data_keys=args.allow_variable_data_keys,
ngpu=args.ngpu,
preprocess_fn=cls.build_preprocess_fn(args, train=False),
collate_fn=cls.build_collate_fn(args, train=False),
mode="train",
),
valid_iter=cls.build_streaming_iterator(
data_path_and_name_and_type=args.valid_data_path_and_name_and_type,
key_file=valid_key_file,
batch_size=args.valid_batch_size,
dtype=args.train_dtype,
num_workers=args.num_workers,
allow_variable_data_keys=args.allow_variable_data_keys,
ngpu=args.ngpu,
preprocess_fn=cls.build_preprocess_fn(args, train=False),
collate_fn=cls.build_collate_fn(args, train=False),
mode="valid",
),
output_dir=output_dir,
ngpu=args.ngpu,
log_interval=args.log_interval,
write_collected_feats=args.write_collected_feats,
)
else:
# 6. Loads pre-trained model
for p in args.init_param:
logging.info(f"Loading pretrained params from {p}")
load_pretrained_model(
model=model,
init_param=p,
ignore_init_mismatch=args.ignore_init_mismatch,
# NOTE(kamo): "cuda" for torch.load always indicates cuda:0
# in PyTorch<=1.4
map_location=(
f"cuda:{torch.cuda.current_device()}"
if args.ngpu > 0
else "cpu"
),
)
# 7. Build iterator factories
if args.multiple_iterator:
train_iter_factory = cls.build_multiple_iter_factory(
args=args,
distributed_option=distributed_option,
mode="train",
)
else:
train_iter_factory = cls.build_iter_factory(
args=args,
distributed_option=distributed_option,
mode="train",
)
valid_iter_factory = cls.build_iter_factory(
args=args,
distributed_option=distributed_option,
mode="valid",
)
if not args.use_matplotlib and args.num_att_plot != 0:
args.num_att_plot = 0
logging.info("--use_matplotlib false => Changing --num_att_plot to 0")
if args.num_att_plot != 0:
plot_attention_iter_factory = cls.build_iter_factory(
args=args,
distributed_option=distributed_option,
mode="plot_att",
)
else:
plot_attention_iter_factory = None
# 8. Start training
if args.use_wandb:
if wandb is None:
raise RuntimeError("Please install wandb")
try:
wandb.login()
except wandb.errors.UsageError:
logging.info("wandb not configured! run `wandb login` to enable")
args.use_wandb = False
if args.use_wandb:
if (
not distributed_option.distributed
or distributed_option.dist_rank == 0
):
if args.wandb_project is None:
project = "ESPnet_" + cls.__name__
else:
project = args.wandb_project
if args.wandb_name is None:
name = str(Path(".").resolve()).replace("/", "_")
else:
name = args.wandb_name
wandb.init(
entity=args.wandb_entity,
project=project,
name=name,
dir=str(output_dir),
id=args.wandb_id,
resume=args.resume,
)
wandb.config.update(args)
else:
# wandb also supports grouping for distributed training,
# but we only logs aggregated data,
# so it's enough to perform on rank0 node.
args.use_wandb = False
# Don't give args to trainer.run() directly!!!
# Instead of it, define "Options" object and build here.
trainer_options = cls.trainer.build_options(args)
cls.trainer.run(
model=model,
optimizers=optimizers,
schedulers=schedulers,
train_iter_factory=train_iter_factory,
valid_iter_factory=valid_iter_factory,
plot_attention_iter_factory=plot_attention_iter_factory,
trainer_options=trainer_options,
distributed_option=distributed_option,
)
if args.use_wandb and wandb.run:
wandb.finish()
[docs] @classmethod
def build_iter_options(
cls,
args: argparse.Namespace,
distributed_option: DistributedOption,
mode: str,
):
if mode == "train":
preprocess_fn = cls.build_preprocess_fn(args, train=True)
collate_fn = cls.build_collate_fn(args, train=True)
data_path_and_name_and_type = args.train_data_path_and_name_and_type
shape_files = args.train_shape_file
batch_size = args.batch_size
batch_bins = args.batch_bins
batch_type = args.batch_type
max_cache_size = args.max_cache_size
max_cache_fd = args.max_cache_fd
allow_multi_rates = args.allow_multi_rates
distributed = distributed_option.distributed
num_batches = None
num_iters_per_epoch = args.num_iters_per_epoch
train = True
elif mode == "valid":
preprocess_fn = cls.build_preprocess_fn(args, train=False)
collate_fn = cls.build_collate_fn(args, train=False)
data_path_and_name_and_type = args.valid_data_path_and_name_and_type
shape_files = args.valid_shape_file
if args.valid_batch_type is None:
batch_type = args.batch_type
else:
batch_type = args.valid_batch_type
if args.valid_batch_size is None:
batch_size = args.batch_size
else:
batch_size = args.valid_batch_size
if args.valid_batch_bins is None:
batch_bins = args.batch_bins
else:
batch_bins = args.valid_batch_bins
if args.valid_max_cache_size is None:
# Cache 5% of maximum size for validation loader
max_cache_size = 0.05 * args.max_cache_size
else:
max_cache_size = args.valid_max_cache_size
max_cache_fd = args.max_cache_fd
allow_multi_rates = args.allow_multi_rates
distributed = distributed_option.distributed
num_batches = None
num_iters_per_epoch = None
train = False
elif mode == "plot_att":
preprocess_fn = cls.build_preprocess_fn(args, train=False)
collate_fn = cls.build_collate_fn(args, train=False)
data_path_and_name_and_type = args.valid_data_path_and_name_and_type
shape_files = args.valid_shape_file
batch_type = "unsorted"
batch_size = 1
batch_bins = 0
num_batches = args.num_att_plot
max_cache_fd = args.max_cache_fd
allow_multi_rates = args.allow_multi_rates
# num_att_plot should be a few sample ~ 3, so cache all data.
max_cache_size = np.inf if args.max_cache_size != 0.0 else 0.0
# always False because plot_attention performs on RANK0
distributed = False
num_iters_per_epoch = None
train = False
else:
raise NotImplementedError(f"mode={mode}")
return IteratorOptions(
preprocess_fn=preprocess_fn,
collate_fn=collate_fn,
data_path_and_name_and_type=data_path_and_name_and_type,
shape_files=shape_files,
batch_type=batch_type,
batch_size=batch_size,
batch_bins=batch_bins,
num_batches=num_batches,
max_cache_size=max_cache_size,
max_cache_fd=max_cache_fd,
allow_multi_rates=allow_multi_rates,
distributed=distributed,
num_iters_per_epoch=num_iters_per_epoch,
train=train,
)
[docs] @classmethod
@typechecked
def build_iter_factory(
cls,
args: argparse.Namespace,
distributed_option: DistributedOption,
mode: str,
kwargs: Optional[dict] = None,
) -> AbsIterFactory:
"""Build a factory object of mini-batch iterator.
This object is invoked at every epochs to build the iterator for each epoch
as following:
>>> iter_factory = cls.build_iter_factory(...)
>>> for epoch in range(1, max_epoch):
... for keys, batch in iter_fatory.build_iter(epoch):
... model(**batch)
The mini-batches for each epochs are fully controlled by this class.
Note that the random seed used for shuffling is decided as "seed + epoch" and
the generated mini-batches can be reproduces when resuming.
Note that the definition of "epoch" doesn't always indicate
to run out of the whole training corpus.
"--num_iters_per_epoch" option restricts the number of iterations for each epoch
and the rest of samples for the originally epoch are left for the next epoch.
e.g. If The number of mini-batches equals to 4, the following two are same:
- 1 epoch without "--num_iters_per_epoch"
- 4 epoch with "--num_iters_per_epoch" == 1
"""
iter_options = cls.build_iter_options(args, distributed_option, mode)
# Overwrite iter_options if any kwargs is given
if kwargs is not None:
for k, v in kwargs.items():
setattr(iter_options, k, v)
if mode == "valid" and args.valid_iterator_type is not None:
iterator_type = args.valid_iterator_type
else:
iterator_type = args.iterator_type
if iterator_type == "sequence":
return cls.build_sequence_iter_factory(
args=args,
iter_options=iter_options,
mode=mode,
)
elif iterator_type == "category":
return cls.build_category_iter_factory(
args=args,
iter_options=iter_options,
mode=mode,
)
elif iterator_type == "chunk":
return cls.build_chunk_iter_factory(
args=args,
iter_options=iter_options,
mode=mode,
)
elif iterator_type == "task":
return cls.build_task_iter_factory(
args=args,
iter_options=iter_options,
mode=mode,
)
else:
raise RuntimeError(f"Not supported: iterator_type={iterator_type}")
[docs] @classmethod
@typechecked
def build_sequence_iter_factory(
cls, args: argparse.Namespace, iter_options: IteratorOptions, mode: str
) -> AbsIterFactory:
dataset = ESPnetDataset(
iter_options.data_path_and_name_and_type,
float_dtype=args.train_dtype,
preprocess=iter_options.preprocess_fn,
max_cache_size=iter_options.max_cache_size,
max_cache_fd=iter_options.max_cache_fd,
allow_multi_rates=iter_options.allow_multi_rates,
)
cls.check_task_requirements(
dataset, args.allow_variable_data_keys, train=iter_options.train
)
if Path(
Path(iter_options.data_path_and_name_and_type[0][0]).parent, "utt2category"
).exists():
utt2category_file = str(
Path(
Path(iter_options.data_path_and_name_and_type[0][0]).parent,
"utt2category",
)
)
logging.warning("Reading " + utt2category_file)
else:
utt2category_file = None
batch_sampler = build_batch_sampler(
type=iter_options.batch_type,
shape_files=iter_options.shape_files,
fold_lengths=args.fold_length,
batch_size=iter_options.batch_size,
batch_bins=iter_options.batch_bins,
sort_in_batch=args.sort_in_batch,
sort_batch=args.sort_batch,
drop_last=args.drop_last_iter,
min_batch_size=(
torch.distributed.get_world_size() if iter_options.distributed else 1
),
utt2category_file=utt2category_file,
)
batches = list(batch_sampler)
if iter_options.num_batches is not None:
batches = batches[: iter_options.num_batches]
bs_list = [len(batch) for batch in batches]
logging.info(f"[{mode}] dataset:\n{dataset}")
logging.info(f"[{mode}] Batch sampler: {batch_sampler}")
logging.info(
f"[{mode}] mini-batch sizes summary: N-batch={len(bs_list)}, "
f"mean={np.mean(bs_list):.1f}, min={np.min(bs_list)}, max={np.max(bs_list)}"
)
if iter_options.distributed:
world_size = torch.distributed.get_world_size()
rank = torch.distributed.get_rank()
for batch in batches:
if len(batch) < world_size:
raise RuntimeError(
f"The batch-size must be equal or more than world_size: "
f"{len(batch)} < {world_size}"
)
batches = [batch[rank::world_size] for batch in batches]
return SequenceIterFactory(
dataset=dataset,
batches=batches,
seed=args.seed,
num_iters_per_epoch=iter_options.num_iters_per_epoch,
shuffle=iter_options.train,
shuffle_within_batch=args.shuffle_within_batch,
num_workers=args.num_workers,
collate_fn=iter_options.collate_fn,
pin_memory=args.ngpu > 0,
)
[docs] @classmethod
@typechecked
def build_category_iter_factory(
cls, args: argparse.Namespace, iter_options: IteratorOptions, mode: str
) -> AbsIterFactory:
dataset = ESPnetDataset(
iter_options.data_path_and_name_and_type,
float_dtype=args.train_dtype,
preprocess=iter_options.preprocess_fn,
max_cache_size=iter_options.max_cache_size,
max_cache_fd=iter_options.max_cache_fd,
allow_multi_rates=iter_options.allow_multi_rates,
)
cls.check_task_requirements(
dataset, args.allow_variable_data_keys, train=iter_options.train
)
if Path(
Path(iter_options.data_path_and_name_and_type[0][0]).parent, "category2utt"
).exists():
category2utt_file = str(
Path(
Path(iter_options.data_path_and_name_and_type[0][0]).parent,
"category2utt",
)
)
logging.warning("Reading " + category2utt_file)
else:
category2utt_file = None
raise ValueError(
"category2utt mandatory for category iterator, but not found"
)
sampler_args = dict(
batch_size=iter_options.batch_size,
min_batch_size=(
torch.distributed.get_world_size() if iter_options.distributed else 1
),
drop_last=args.drop_last_iter,
category2utt_file=category2utt_file,
epoch=1,
num_batches=iter_options.num_batches,
distributed=iter_options.distributed,
)
batch_sampler = CategoryBalancedSampler(**sampler_args)
batches = list(batch_sampler)
if iter_options.num_batches is not None:
batches = batches[: iter_options.num_batches]
bs_list = [len(batch) for batch in batches]
logging.info(f"[{mode}] dataset:\n{dataset}")
logging.info(f"[{mode}] Batch sampler: {batch_sampler}")
logging.info(
f"[{mode}] mini-batch sizes summary: N-batch={len(bs_list)}, "
f"mean={np.mean(bs_list):.1f}, min={np.min(bs_list)}, max={np.max(bs_list)}"
)
if iter_options.distributed:
world_size = torch.distributed.get_world_size()
rank = torch.distributed.get_rank()
for batch in batches:
if len(batch) < world_size:
raise RuntimeError(
f"The batch-size must be equal or more than world_size: "
f"{len(batch)} < {world_size}"
)
batches = [batch[rank::world_size] for batch in batches]
return CategoryIterFactory(
dataset=dataset,
batches=batches,
seed=args.seed,
num_iters_per_epoch=iter_options.num_iters_per_epoch,
sampler_args=sampler_args,
shuffle=iter_options.train,
num_workers=args.num_workers,
collate_fn=iter_options.collate_fn,
pin_memory=args.ngpu > 0,
)
[docs] @classmethod
@typechecked
def build_chunk_iter_factory(
cls,
args: argparse.Namespace,
iter_options: IteratorOptions,
mode: str,
) -> AbsIterFactory:
dataset = ESPnetDataset(
iter_options.data_path_and_name_and_type,
float_dtype=args.train_dtype,
preprocess=iter_options.preprocess_fn,
max_cache_size=iter_options.max_cache_size,
max_cache_fd=iter_options.max_cache_fd,
allow_multi_rates=iter_options.allow_multi_rates,
)
cls.check_task_requirements(
dataset, args.allow_variable_data_keys, train=iter_options.train
)
if len(iter_options.shape_files) == 0:
key_file = iter_options.data_path_and_name_and_type[0][0]
else:
key_file = iter_options.shape_files[0]
batch_sampler = UnsortedBatchSampler(batch_size=1, key_file=key_file)
batches = list(batch_sampler)
if iter_options.num_batches is not None:
batches = batches[: iter_options.num_batches]
logging.info(f"[{mode}] dataset:\n{dataset}")
if iter_options.distributed:
world_size = torch.distributed.get_world_size()
rank = torch.distributed.get_rank()
if len(batches) < world_size:
raise RuntimeError("Number of samples is smaller than world_size")
if iter_options.batch_size < world_size:
raise RuntimeError("batch_size must be equal or more than world_size")
if rank < iter_options.batch_size % world_size:
batch_size = iter_options.batch_size // world_size + 1
else:
batch_size = iter_options.batch_size // world_size
num_cache_chunks = args.num_cache_chunks // world_size
# NOTE(kamo): Split whole corpus by sample numbers without considering
# each of the lengths, therefore the number of iteration counts are not
# always equal to each other and the iterations are limitted
# by the fewest iterations.
# i.e. the samples over the counts are discarded.
batches = batches[rank::world_size]
else:
batch_size = iter_options.batch_size
num_cache_chunks = args.num_cache_chunks
return ChunkIterFactory(
dataset=dataset,
batches=batches,
seed=args.seed,
batch_size=batch_size,
# For chunk iterator,
# --num_iters_per_epoch doesn't indicate the number of iterations,
# but indicates the number of samples.
num_samples_per_epoch=iter_options.num_iters_per_epoch,
shuffle=iter_options.train,
num_workers=args.num_workers,
collate_fn=iter_options.collate_fn,
pin_memory=args.ngpu > 0,
chunk_length=args.chunk_length,
chunk_shift_ratio=args.chunk_shift_ratio,
num_cache_chunks=num_cache_chunks,
excluded_key_prefixes=args.chunk_excluded_key_prefixes,
default_fs=args.chunk_default_fs,
)
# NOTE(kamo): Not abstract class
[docs] @classmethod
def build_task_iter_factory(
cls,
args: argparse.Namespace,
iter_options: IteratorOptions,
mode: str,
) -> AbsIterFactory:
"""Build task specific iterator factory
Example:
>>> class YourTask(AbsTask):
... @classmethod
... def add_task_arguments(cls, parser: argparse.ArgumentParser):
... parser.set_defaults(iterator_type="task")
...
... @classmethod
... def build_task_iter_factory(
... cls,
... args: argparse.Namespace,
... iter_options: IteratorOptions,
... mode: str,
... ):
... return FooIterFactory(...)
...
... @classmethod
... def build_iter_options(
.... args: argparse.Namespace,
... distributed_option: DistributedOption,
... mode: str
... ):
... # if you need to customize options object
"""
raise NotImplementedError
[docs] @classmethod
@typechecked
def build_multiple_iter_factory(
cls, args: argparse.Namespace, distributed_option: DistributedOption, mode: str
):
iter_options = cls.build_iter_options(args, distributed_option, mode)
assert len(iter_options.data_path_and_name_and_type) > 0, len(
iter_options.data_path_and_name_and_type
)
# 1. Sanity check
num_splits = None
for path in [
path for path, _, _ in iter_options.data_path_and_name_and_type
] + list(iter_options.shape_files):
if not Path(path).is_dir():
raise RuntimeError(f"{path} is not a directory")
p = Path(path) / "num_splits"
if not p.exists():
raise FileNotFoundError(f"{p} is not found")
with p.open() as f:
_num_splits = int(f.read())
if num_splits is not None and num_splits != _num_splits:
raise RuntimeError(
f"Number of splits are mismathed: "
f"{iter_options.data_path_and_name_and_type[0][0]} and {path}"
)
num_splits = _num_splits
for i in range(num_splits):
p = Path(path) / f"split.{i}"
if not p.exists():
raise FileNotFoundError(f"{p} is not found")
# 2. Create functions to build an iter factory for each splits
data_path_and_name_and_type_list = [
[
(str(Path(p) / f"split.{i}"), n, t)
for p, n, t in iter_options.data_path_and_name_and_type
]
for i in range(num_splits)
]
shape_files_list = [
[str(Path(s) / f"split.{i}") for s in iter_options.shape_files]
for i in range(num_splits)
]
num_iters_per_epoch_list = [
(
(iter_options.num_iters_per_epoch + i) // num_splits
if iter_options.num_iters_per_epoch is not None
else None
)
for i in range(num_splits)
]
max_cache_size = iter_options.max_cache_size / num_splits
# Note that iter-factories are built for each epoch at runtime lazily.
build_funcs = [
functools.partial(
cls.build_iter_factory,
args,
distributed_option,
mode,
kwargs=dict(
data_path_and_name_and_type=_data_path_and_name_and_type,
shape_files=_shape_files,
num_iters_per_epoch=_num_iters_per_epoch,
max_cache_size=max_cache_size,
),
)
for (
_data_path_and_name_and_type,
_shape_files,
_num_iters_per_epoch,
) in zip(
data_path_and_name_and_type_list,
shape_files_list,
num_iters_per_epoch_list,
)
]
# 3. Build MultipleIterFactory
return MultipleIterFactory(
build_funcs=build_funcs, shuffle=iter_options.train, seed=args.seed
)
[docs] @classmethod
@typechecked
def build_streaming_iterator(
cls,
data_path_and_name_and_type,
preprocess_fn,
collate_fn,
key_file: Optional[str] = None,
batch_size: int = 1,
dtype: str = np.float32,
num_workers: int = 1,
allow_variable_data_keys: bool = False,
ngpu: int = 0,
inference: bool = False,
mode: Optional[str] = None,
) -> DataLoader:
"""Build DataLoader using iterable dataset"""
# For backward compatibility for pytorch DataLoader
if collate_fn is not None:
kwargs = dict(collate_fn=collate_fn)
else:
kwargs = {}
dataset = IterableESPnetDataset(
data_path_and_name_and_type,
float_dtype=dtype,
preprocess=preprocess_fn,
key_file=key_file,
)
if dataset.apply_utt2category:
kwargs.update(batch_size=1)
else:
kwargs.update(batch_size=batch_size)
cls.check_task_requirements(
dataset, allow_variable_data_keys, train=False, inference=inference
)
return DataLoader(
dataset=dataset,
pin_memory=ngpu > 0,
num_workers=num_workers,
**kwargs,
)
# ~~~~~~~~~ The methods below are mainly used for inference ~~~~~~~~~
[docs] @classmethod
@typechecked
def build_model_from_file(
cls,
config_file: Optional[Union[Path, str]] = None,
model_file: Optional[Union[Path, str]] = None,
device: str = "cpu",
) -> Tuple[AbsESPnetModel, argparse.Namespace]:
"""Build model from the files.
This method is used for inference or fine-tuning.
Args:
config_file: The yaml file saved when training.
model_file: The model file saved when training.
device: Device type, "cpu", "cuda", or "cuda:N".
"""
if config_file is None:
assert model_file is not None, (
"The argument 'model_file' must be provided "
"if the argument 'config_file' is not specified."
)
config_file = Path(model_file).parent / "config.yaml"
else:
config_file = Path(config_file)
logging.info("config file: {}".format(config_file))
with config_file.open("r", encoding="utf-8") as f:
args = yaml.safe_load(f)
args = argparse.Namespace(**args)
model = cls.build_model(args)
if not isinstance(model, AbsESPnetModel):
raise RuntimeError(
f"model must inherit {AbsESPnetModel.__name__}, but got {type(model)}"
)
model.to(device)
# For finetuned model, create adapter
use_adapter = getattr(args, "use_adapter", False)
if use_adapter:
create_adapter(model, args.adapter, args.adapter_conf)
if model_file is not None:
if device == "cuda":
# NOTE(kamo): "cuda" for torch.load always indicates cuda:0
# in PyTorch<=1.4
device = f"cuda:{torch.cuda.current_device()}"
try:
model.load_state_dict(
torch.load(model_file, map_location=device),
strict=not use_adapter,
)
except RuntimeError:
# Note(simpleoier): the following part is to be compatible with
# pretrained model using earlier versions before `0a625088`
state_dict = torch.load(model_file, map_location=device)
if any(["frontend.upstream.model" in k for k in state_dict.keys()]):
if any(
[
"frontend.upstream.upstream.model" in k
for k in dict(model.named_parameters())
]
):
state_dict = {
k.replace(
"frontend.upstream.model",
"frontend.upstream.upstream.model",
): v
for k, v in state_dict.items()
}
model.load_state_dict(state_dict, strict=not use_adapter)
else:
if any(["postdecoder" in k for k in state_dict.keys()]):
model.load_state_dict(
state_dict,
strict=False,
)
else:
raise
else:
if any(["postdecoder" in k for k in state_dict.keys()]):
model.load_state_dict(
state_dict,
strict=False,
)
else:
raise
return model, args