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1113 lines (923 loc) · 40.4 KB
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import gc
import json
import logging
import math
import os
import time
from collections import OrderedDict
from dataclasses import dataclass, field
from typing import Any, Dict, List, Mapping, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
from hydra.utils import instantiate
from iopath.common.file_io import g_pathmgr
from training.optimizer import construct_optimizer
from training.utils.checkpoint_utils import (
assert_skipped_parameters_are_frozen,
exclude_params_matching_unix_pattern,
load_state_dict_into_model,
with_check_parameter_frozen,
)
from training.utils.data_utils import BatchedVideoDatapoint
from training.utils.distributed import all_reduce_max, barrier, get_rank
from training.utils.logger import Logger, setup_logging
from training.utils.train_utils import (
AverageMeter,
collect_dict_keys,
DurationMeter,
get_amp_type,
get_machine_local_and_dist_rank,
get_resume_checkpoint,
human_readable_time,
is_dist_avail_and_initialized,
log_env_variables,
makedir,
MemMeter,
Phase,
ProgressMeter,
set_seeds,
setup_distributed_backend,
)
CORE_LOSS_KEY = "core_loss"
def unwrap_ddp_if_wrapped(model):
if isinstance(model, torch.nn.parallel.DistributedDataParallel):
return model.module
return model
@dataclass
class OptimAMPConf:
enabled: bool = False
amp_dtype: str = "float16"
@dataclass
class OptimConf:
optimizer: torch.optim.Optimizer = None
options: Optional[Dict[str, Any]] = None
param_group_modifiers: Optional[List] = None
amp: Optional[Dict[str, Any]] = None
gradient_clip: Any = None
gradient_logger: Any = None
def __post_init__(self):
# amp
if not isinstance(self.amp, OptimAMPConf):
if self.amp is None:
self.amp = {}
assert isinstance(self.amp, Mapping)
self.amp = OptimAMPConf(**self.amp)
@dataclass
class DistributedConf:
backend: Optional[str] = None # inferred from accelerator type
comms_dtype: Optional[str] = None
find_unused_parameters: bool = False
timeout_mins: int = 30
@dataclass
class CudaConf:
cudnn_deterministic: bool = False
cudnn_benchmark: bool = True
allow_tf32: bool = False
# if not None, `matmul_allow_tf32` key will override `allow_tf32` for matmul
matmul_allow_tf32: Optional[bool] = None
# if not None, `cudnn_allow_tf32` key will override `allow_tf32` for cudnn
cudnn_allow_tf32: Optional[bool] = None
@dataclass
class CheckpointConf:
save_dir: str
save_freq: int
save_list: List[int] = field(default_factory=list)
model_weight_initializer: Any = None
save_best_meters: List[str] = None
skip_saving_parameters: List[str] = field(default_factory=list)
initialize_after_preemption: Optional[bool] = None
# if not None, training will be resumed from this checkpoint
resume_from: Optional[str] = None
def infer_missing(self):
if self.initialize_after_preemption is None:
with_skip_saving = len(self.skip_saving_parameters) > 0
self.initialize_after_preemption = with_skip_saving
return self
@dataclass
class LoggingConf:
log_dir: str
log_freq: int # In iterations
tensorboard_writer: Any
log_level_primary: str = "INFO"
log_level_secondary: str = "ERROR"
log_scalar_frequency: int = 100
log_visual_frequency: int = 100
scalar_keys_to_log: Optional[Dict[str, Any]] = None
log_batch_stats: bool = False
class Trainer:
"""
Trainer supporting the DDP training strategies.
"""
EPSILON = 1e-8
def __init__(
self,
*, # the order of these args can change at any time, so they are keyword-only
data: Dict[str, Any],
model: Dict[str, Any],
logging: Dict[str, Any],
checkpoint: Dict[str, Any],
max_epochs: int,
mode: str = "train",
accelerator: str = "cuda",
seed_value: int = 123,
val_epoch_freq: int = 1,
distributed: Dict[str, bool] = None,
cuda: Dict[str, bool] = None,
env_variables: Optional[Dict[str, Any]] = None,
optim: Optional[Dict[str, Any]] = None,
optim_overrides: Optional[List[Dict[str, Any]]] = None,
meters: Optional[Dict[str, Any]] = None,
loss: Optional[Dict[str, Any]] = None,
):
self._setup_env_variables(env_variables)
self._setup_timers()
self.data_conf = data
self.model_conf = model
self.logging_conf = LoggingConf(**logging)
self.checkpoint_conf = CheckpointConf(**checkpoint).infer_missing()
self.max_epochs = max_epochs
self.mode = mode
self.val_epoch_freq = val_epoch_freq
self.optim_conf = OptimConf(**optim) if optim is not None else None
self.meters_conf = meters
self.loss_conf = loss
distributed = DistributedConf(**distributed or {})
cuda = CudaConf(**cuda or {})
self.where = 0.0
self._infer_distributed_backend_if_none(distributed, accelerator)
self._setup_device(accelerator)
self._setup_torch_dist_and_backend(cuda, distributed)
makedir(self.logging_conf.log_dir)
setup_logging(
__name__,
output_dir=self.logging_conf.log_dir,
rank=self.rank,
log_level_primary=self.logging_conf.log_level_primary,
log_level_secondary=self.logging_conf.log_level_secondary,
)
set_seeds(seed_value, self.max_epochs, self.distributed_rank)
log_env_variables()
assert (
is_dist_avail_and_initialized()
), "Torch distributed needs to be initialized before calling the trainer."
self._setup_components() # Except Optimizer everything is setup here.
self._move_to_device()
self._construct_optimizers()
self._setup_dataloaders()
self.time_elapsed_meter = DurationMeter("Time Elapsed", self.device, ":.2f")
if self.checkpoint_conf.resume_from is not None:
assert os.path.exists(
self.checkpoint_conf.resume_from
), f"The 'resume_from' checkpoint {self.checkpoint_conf.resume_from} does not exist!"
dst = os.path.join(self.checkpoint_conf.save_dir, "checkpoint.pt")
if self.distributed_rank == 0 and not os.path.exists(dst):
# Copy the "resume_from" checkpoint to the checkpoint folder
# if there is not a checkpoint to resume from already there
makedir(self.checkpoint_conf.save_dir)
g_pathmgr.copy(self.checkpoint_conf.resume_from, dst)
barrier()
self.load_checkpoint()
self._setup_ddp_distributed_training(distributed, accelerator)
barrier()
def _setup_timers(self):
"""
Initializes counters for elapsed time and eta.
"""
self.start_time = time.time()
self.ckpt_time_elapsed = 0
self.est_epoch_time = dict.fromkeys([Phase.TRAIN, Phase.VAL], 0)
def _get_meters(self, phase_filters=None):
if self.meters is None:
return {}
meters = {}
for phase, phase_meters in self.meters.items():
if phase_filters is not None and phase not in phase_filters:
continue
for key, key_meters in phase_meters.items():
if key_meters is None:
continue
for name, meter in key_meters.items():
meters[f"{phase}_{key}/{name}"] = meter
return meters
def _infer_distributed_backend_if_none(self, distributed_conf, accelerator):
if distributed_conf.backend is None:
distributed_conf.backend = "nccl" if accelerator == "cuda" else "gloo"
def _setup_env_variables(self, env_variables_conf) -> None:
if env_variables_conf is not None:
for variable_name, value in env_variables_conf.items():
os.environ[variable_name] = value
def _setup_torch_dist_and_backend(self, cuda_conf, distributed_conf) -> None:
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = cuda_conf.cudnn_deterministic
torch.backends.cudnn.benchmark = cuda_conf.cudnn_benchmark
torch.backends.cuda.matmul.allow_tf32 = (
cuda_conf.matmul_allow_tf32
if cuda_conf.matmul_allow_tf32 is not None
else cuda_conf.allow_tf32
)
torch.backends.cudnn.allow_tf32 = (
cuda_conf.cudnn_allow_tf32
if cuda_conf.cudnn_allow_tf32 is not None
else cuda_conf.allow_tf32
)
self.rank = setup_distributed_backend(
distributed_conf.backend, distributed_conf.timeout_mins
)
def _setup_device(self, accelerator):
self.local_rank, self.distributed_rank = get_machine_local_and_dist_rank()
if accelerator == "cuda":
self.device = torch.device("cuda", self.local_rank)
torch.cuda.set_device(self.local_rank)
elif accelerator == "cpu":
self.device = torch.device("cpu")
else:
raise ValueError(f"Unsupported accelerator: {accelerator}")
def _setup_ddp_distributed_training(self, distributed_conf, accelerator):
assert isinstance(self.model, torch.nn.Module)
self.model = nn.parallel.DistributedDataParallel(
self.model,
device_ids=[self.local_rank] if accelerator == "cuda" else [],
find_unused_parameters=distributed_conf.find_unused_parameters,
)
if distributed_conf.comms_dtype is not None: # noqa
from torch.distributed.algorithms import ddp_comm_hooks
amp_type = get_amp_type(distributed_conf.comms_dtype)
if amp_type == torch.bfloat16:
hook = ddp_comm_hooks.default_hooks.bf16_compress_hook
logging.info("Enabling bfloat16 grad communication")
else:
hook = ddp_comm_hooks.default_hooks.fp16_compress_hook
logging.info("Enabling fp16 grad communication")
process_group = None
self.model.register_comm_hook(process_group, hook)
def _move_to_device(self):
logging.info(
f"Moving components to device {self.device} and local rank {self.local_rank}."
)
self.model.to(self.device)
logging.info(
f"Done moving components to device {self.device} and local rank {self.local_rank}."
)
def save_checkpoint(self, epoch, checkpoint_names=None):
checkpoint_folder = self.checkpoint_conf.save_dir
makedir(checkpoint_folder)
if checkpoint_names is None:
checkpoint_names = ["checkpoint"]
if (
self.checkpoint_conf.save_freq > 0
and (int(epoch) % self.checkpoint_conf.save_freq == 0)
) or int(epoch) in self.checkpoint_conf.save_list:
checkpoint_names.append(f"checkpoint_{int(epoch)}")
checkpoint_paths = []
for ckpt_name in checkpoint_names:
checkpoint_paths.append(os.path.join(checkpoint_folder, f"{ckpt_name}.pt"))
state_dict = unwrap_ddp_if_wrapped(self.model).state_dict()
state_dict = exclude_params_matching_unix_pattern(
patterns=self.checkpoint_conf.skip_saving_parameters, state_dict=state_dict
)
checkpoint = {
"model": state_dict,
"optimizer": self.optim.optimizer.state_dict(),
"epoch": epoch,
"loss": self.loss.state_dict(),
"steps": self.steps,
"time_elapsed": self.time_elapsed_meter.val,
"best_meter_values": self.best_meter_values,
}
if self.optim_conf.amp.enabled:
checkpoint["scaler"] = self.scaler.state_dict()
# DDP checkpoints are only saved on rank 0 (all workers are identical)
if self.distributed_rank != 0:
return
for checkpoint_path in checkpoint_paths:
self._save_checkpoint(checkpoint, checkpoint_path)
def _save_checkpoint(self, checkpoint, checkpoint_path):
"""
Save a checkpoint while guarding against the job being killed in the middle
of checkpoint saving (which corrupts the checkpoint file and ruins the
entire training since usually only the last checkpoint is kept per run).
We first save the new checkpoint to a temp file (with a '.tmp' suffix), and
and move it to overwrite the old checkpoint_path.
"""
checkpoint_path_tmp = f"{checkpoint_path}.tmp"
with g_pathmgr.open(checkpoint_path_tmp, "wb") as f:
torch.save(checkpoint, f)
# after torch.save is completed, replace the old checkpoint with the new one
if g_pathmgr.exists(checkpoint_path):
# remove the old checkpoint_path file first (otherwise g_pathmgr.mv fails)
g_pathmgr.rm(checkpoint_path)
success = g_pathmgr.mv(checkpoint_path_tmp, checkpoint_path)
assert success
def load_checkpoint(self):
ckpt_path = get_resume_checkpoint(self.checkpoint_conf.save_dir)
if ckpt_path is None:
self._init_model_state()
else:
if self.checkpoint_conf.initialize_after_preemption:
self._call_model_initializer()
self._load_resuming_checkpoint(ckpt_path)
def _init_model_state(self):
# Checking that parameters that won't be saved are indeed frozen
# We do this check here before even saving the model to catch errors
# are early as possible and not at the end of the first epoch
assert_skipped_parameters_are_frozen(
patterns=self.checkpoint_conf.skip_saving_parameters,
model=self.model,
)
# Checking that parameters that won't be saved are initialized from
# within the model definition, unless `initialize_after_preemption`
# is explicitly set to `True`. If not, this is a bug, and after
# preemption, the `skip_saving_parameters` will have random values
allow_init_skip_parameters = self.checkpoint_conf.initialize_after_preemption
with with_check_parameter_frozen(
patterns=self.checkpoint_conf.skip_saving_parameters,
model=self.model,
disabled=allow_init_skip_parameters,
):
self._call_model_initializer()
def _call_model_initializer(self):
model_weight_initializer = instantiate(
self.checkpoint_conf.model_weight_initializer
)
if model_weight_initializer is not None:
logging.info(
f"Loading pretrained checkpoint from {self.checkpoint_conf.model_weight_initializer}"
)
self.model = model_weight_initializer(model=self.model)
def _load_resuming_checkpoint(self, ckpt_path: str):
logging.info(f"Resuming training from {ckpt_path}")
with g_pathmgr.open(ckpt_path, "rb") as f:
checkpoint = torch.load(f, map_location="cpu")
load_state_dict_into_model(
model=self.model,
state_dict=checkpoint["model"],
ignore_missing_keys=self.checkpoint_conf.skip_saving_parameters,
)
self.optim.optimizer.load_state_dict(checkpoint["optimizer"])
self.loss.load_state_dict(checkpoint["loss"], strict=True)
self.epoch = checkpoint["epoch"]
self.steps = checkpoint["steps"]
self.ckpt_time_elapsed = checkpoint.get("time_elapsed")
if self.optim_conf.amp.enabled and "scaler" in checkpoint:
self.scaler.load_state_dict(checkpoint["scaler"])
self.best_meter_values = checkpoint.get("best_meter_values", {})
if "train_dataset" in checkpoint and self.train_dataset is not None:
self.train_dataset.load_checkpoint_state(checkpoint["train_dataset"])
def is_intermediate_val_epoch(self, epoch):
return epoch % self.val_epoch_freq == 0 and epoch < self.max_epochs - 1
def _step(
self,
batch: BatchedVideoDatapoint,
model: nn.Module,
phase: str,
):
outputs = model(batch)
targets = batch.masks
batch_size = len(batch.img_batch)
key = batch.dict_key # key for dataset
loss = self.loss[key](outputs, targets)
loss_str = f"Losses/{phase}_{key}_loss"
loss_log_str = os.path.join("Step_Losses", loss_str)
# loss contains multiple sub-components we wish to log
step_losses = {}
if isinstance(loss, dict):
step_losses.update(
{f"Losses/{phase}_{key}_{k}": v for k, v in loss.items()}
)
loss = self._log_loss_detailed_and_return_core_loss(
loss, loss_log_str, self.steps[phase]
)
if self.steps[phase] % self.logging_conf.log_scalar_frequency == 0:
self.logger.log(
loss_log_str,
loss,
self.steps[phase],
)
self.steps[phase] += 1
ret_tuple = {loss_str: loss}, batch_size, step_losses
if phase in self.meters and key in self.meters[phase]:
meters_dict = self.meters[phase][key]
if meters_dict is not None:
for _, meter in meters_dict.items():
meter.update(
find_stages=outputs,
find_metadatas=batch.metadata,
)
return ret_tuple
def run(self):
assert self.mode in ["train", "train_only", "val"]
if self.mode == "train":
if self.epoch > 0:
logging.info(f"Resuming training from epoch: {self.epoch}")
# resuming from a checkpoint
if self.is_intermediate_val_epoch(self.epoch - 1):
logging.info("Running previous val epoch")
self.epoch -= 1
self.run_val()
self.epoch += 1
self.run_train()
self.run_val()
elif self.mode == "val":
self.run_val()
elif self.mode == "train_only":
self.run_train()
def _setup_dataloaders(self):
self.train_dataset = None
self.val_dataset = None
if self.mode in ["train", "val"]:
self.val_dataset = instantiate(self.data_conf.get(Phase.VAL, None))
if self.mode in ["train", "train_only"]:
self.train_dataset = instantiate(self.data_conf.train)
def run_train(self):
while self.epoch < self.max_epochs:
dataloader = self.train_dataset.get_loader(epoch=int(self.epoch))
barrier()
outs = self.train_epoch(dataloader)
self.logger.log_dict(outs, self.epoch) # Logged only on rank 0
# log train to text file.
if self.distributed_rank == 0:
with g_pathmgr.open(
os.path.join(self.logging_conf.log_dir, "train_stats.json"),
"a",
) as f:
f.write(json.dumps(outs) + "\n")
# Save checkpoint before validating
self.save_checkpoint(self.epoch + 1)
del dataloader
gc.collect()
# Run val, not running on last epoch since will run after the
# loop anyway
if self.is_intermediate_val_epoch(self.epoch):
self.run_val()
if self.distributed_rank == 0:
self.best_meter_values.update(self._get_trainer_state("train"))
with g_pathmgr.open(
os.path.join(self.logging_conf.log_dir, "best_stats.json"),
"a",
) as f:
f.write(json.dumps(self.best_meter_values) + "\n")
self.epoch += 1
# epoch was incremented in the loop but the val step runs out of the loop
self.epoch -= 1
def run_val(self):
if not self.val_dataset:
return
dataloader = self.val_dataset.get_loader(epoch=int(self.epoch))
outs = self.val_epoch(dataloader, phase=Phase.VAL)
del dataloader
gc.collect()
self.logger.log_dict(outs, self.epoch) # Logged only on rank 0
if self.distributed_rank == 0:
with g_pathmgr.open(
os.path.join(self.logging_conf.log_dir, "val_stats.json"),
"a",
) as f:
f.write(json.dumps(outs) + "\n")
def val_epoch(self, val_loader, phase):
batch_time = AverageMeter("Batch Time", self.device, ":.2f")
data_time = AverageMeter("Data Time", self.device, ":.2f")
mem = MemMeter("Mem (GB)", self.device, ":.2f")
iters_per_epoch = len(val_loader)
curr_phases = [phase]
curr_models = [self.model]
loss_names = []
for p in curr_phases:
for key in self.loss.keys():
loss_names.append(f"Losses/{p}_{key}_loss")
loss_mts = OrderedDict(
[(name, AverageMeter(name, self.device, ":.2e")) for name in loss_names]
)
extra_loss_mts = {}
for model in curr_models:
model.eval()
if hasattr(unwrap_ddp_if_wrapped(model), "on_validation_epoch_start"):
unwrap_ddp_if_wrapped(model).on_validation_epoch_start()
progress = ProgressMeter(
iters_per_epoch,
[batch_time, data_time, mem, self.time_elapsed_meter, *loss_mts.values()],
self._get_meters(curr_phases),
prefix="Val Epoch: [{}]".format(self.epoch),
)
end = time.time()
for data_iter, batch in enumerate(val_loader):
# measure data loading time
data_time.update(time.time() - end)
batch = batch.to(self.device, non_blocking=True)
# compute output
with torch.no_grad():
with torch.cuda.amp.autocast(
enabled=(self.optim_conf.amp.enabled if self.optim_conf else False),
dtype=(
get_amp_type(self.optim_conf.amp.amp_dtype)
if self.optim_conf
else None
),
):
for phase, model in zip(curr_phases, curr_models):
loss_dict, batch_size, extra_losses = self._step(
batch,
model,
phase,
)
assert len(loss_dict) == 1
loss_key, loss = loss_dict.popitem()
loss_mts[loss_key].update(loss.item(), batch_size)
for k, v in extra_losses.items():
if k not in extra_loss_mts:
extra_loss_mts[k] = AverageMeter(k, self.device, ":.2e")
extra_loss_mts[k].update(v.item(), batch_size)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
self.time_elapsed_meter.update(
time.time() - self.start_time + self.ckpt_time_elapsed
)
if torch.cuda.is_available():
mem.update(reset_peak_usage=True)
if data_iter % self.logging_conf.log_freq == 0:
progress.display(data_iter)
if data_iter % self.logging_conf.log_scalar_frequency == 0:
# Log progress meters.
for progress_meter in progress.meters:
self.logger.log(
os.path.join("Step_Stats", phase, progress_meter.name),
progress_meter.val,
self.steps[Phase.VAL],
)
if data_iter % 10 == 0:
dist.barrier()
self.est_epoch_time[phase] = batch_time.avg * iters_per_epoch
self._log_timers(phase)
for model in curr_models:
if hasattr(unwrap_ddp_if_wrapped(model), "on_validation_epoch_end"):
unwrap_ddp_if_wrapped(model).on_validation_epoch_end()
out_dict = self._log_meters_and_save_best_ckpts(curr_phases)
for k, v in loss_mts.items():
out_dict[k] = v.avg
for k, v in extra_loss_mts.items():
out_dict[k] = v.avg
for phase in curr_phases:
out_dict.update(self._get_trainer_state(phase))
self._reset_meters(curr_phases)
logging.info(f"Meters: {out_dict}")
return out_dict
def _get_trainer_state(self, phase):
return {
"Trainer/where": self.where,
"Trainer/epoch": self.epoch,
f"Trainer/steps_{phase}": self.steps[phase],
}
def train_epoch(self, train_loader):
# Init stat meters
batch_time_meter = AverageMeter("Batch Time", self.device, ":.2f")
data_time_meter = AverageMeter("Data Time", self.device, ":.2f")
mem_meter = MemMeter("Mem (GB)", self.device, ":.2f")
data_times = []
phase = Phase.TRAIN
iters_per_epoch = len(train_loader)
loss_names = []
for batch_key in self.loss.keys():
loss_names.append(f"Losses/{phase}_{batch_key}_loss")
loss_mts = OrderedDict(
[(name, AverageMeter(name, self.device, ":.2e")) for name in loss_names]
)
extra_loss_mts = {}
progress = ProgressMeter(
iters_per_epoch,
[
batch_time_meter,
data_time_meter,
mem_meter,
self.time_elapsed_meter,
*loss_mts.values(),
],
self._get_meters([phase]),
prefix="Train Epoch: [{}]".format(self.epoch),
)
# Model training loop
self.model.train()
end = time.time()
for data_iter, batch in enumerate(train_loader):
# measure data loading time
data_time_meter.update(time.time() - end)
data_times.append(data_time_meter.val)
batch = batch.to(
self.device, non_blocking=True
) # move tensors in a tensorclass
try:
self._run_step(batch, phase, loss_mts, extra_loss_mts)
# compute gradient and do optim step
exact_epoch = self.epoch + float(data_iter) / iters_per_epoch
self.where = float(exact_epoch) / self.max_epochs
assert self.where <= 1 + self.EPSILON
if self.where < 1.0:
self.optim.step_schedulers(
self.where, step=int(exact_epoch * iters_per_epoch)
)
else:
logging.warning(
f"Skipping scheduler update since the training is at the end, i.e, {self.where} of [0,1]."
)
# Log schedulers
if data_iter % self.logging_conf.log_scalar_frequency == 0:
for j, param_group in enumerate(self.optim.optimizer.param_groups):
for option in self.optim.schedulers[j]:
optim_prefix = (
"" + f"{j}_"
if len(self.optim.optimizer.param_groups) > 1
else ""
)
self.logger.log(
os.path.join("Optim", f"{optim_prefix}", option),
param_group[option],
self.steps[phase],
)
# Clipping gradients and detecting diverging gradients
if self.gradient_clipper is not None:
self.scaler.unscale_(self.optim.optimizer)
self.gradient_clipper(model=self.model)
if self.gradient_logger is not None:
self.gradient_logger(
self.model, rank=self.distributed_rank, where=self.where
)
# Optimizer step: the scaler will make sure gradients are not
# applied if the gradients are infinite
self.scaler.step(self.optim.optimizer)
self.scaler.update()
# measure elapsed time
batch_time_meter.update(time.time() - end)
end = time.time()
self.time_elapsed_meter.update(
time.time() - self.start_time + self.ckpt_time_elapsed
)
mem_meter.update(reset_peak_usage=True)
if data_iter % self.logging_conf.log_freq == 0:
progress.display(data_iter)
if data_iter % self.logging_conf.log_scalar_frequency == 0:
# Log progress meters.
for progress_meter in progress.meters:
self.logger.log(
os.path.join("Step_Stats", phase, progress_meter.name),
progress_meter.val,
self.steps[phase],
)
# Catching NaN/Inf errors in the loss
except FloatingPointError as e:
raise e
self.est_epoch_time[Phase.TRAIN] = batch_time_meter.avg * iters_per_epoch
self._log_timers(Phase.TRAIN)
self._log_sync_data_times(Phase.TRAIN, data_times)
out_dict = self._log_meters_and_save_best_ckpts([Phase.TRAIN])
for k, v in loss_mts.items():
out_dict[k] = v.avg
for k, v in extra_loss_mts.items():
out_dict[k] = v.avg
out_dict.update(self._get_trainer_state(phase))
logging.info(f"Losses and meters: {out_dict}")
self._reset_meters([phase])
return out_dict
def _log_sync_data_times(self, phase, data_times):
data_times = all_reduce_max(torch.tensor(data_times)).tolist()
steps = range(self.steps[phase] - len(data_times), self.steps[phase])
for step, data_time in zip(steps, data_times):
if step % self.logging_conf.log_scalar_frequency == 0:
self.logger.log(
os.path.join("Step_Stats", phase, "Data Time Synced"),
data_time,
step,
)
def _run_step(
self,
batch: BatchedVideoDatapoint,
phase: str,
loss_mts: Dict[str, AverageMeter],
extra_loss_mts: Dict[str, AverageMeter],
raise_on_error: bool = True,
):
"""
Run the forward / backward
"""
# it's important to set grads to None, especially with Adam since 0
# grads will also update a model even if the step doesn't produce
# gradients
self.optim.zero_grad(set_to_none=True)
with torch.cuda.amp.autocast(
enabled=self.optim_conf.amp.enabled,
dtype=get_amp_type(self.optim_conf.amp.amp_dtype),
):
loss_dict, batch_size, extra_losses = self._step(
batch,
self.model,
phase,
)
assert len(loss_dict) == 1
loss_key, loss = loss_dict.popitem()
if not math.isfinite(loss.item()):
error_msg = f"Loss is {loss.item()}, attempting to stop training"
logging.error(error_msg)
if raise_on_error:
raise FloatingPointError(error_msg)
else:
return
self.scaler.scale(loss).backward()
loss_mts[loss_key].update(loss.item(), batch_size)
for extra_loss_key, extra_loss in extra_losses.items():
if extra_loss_key not in extra_loss_mts:
extra_loss_mts[extra_loss_key] = AverageMeter(
extra_loss_key, self.device, ":.2e"
)
extra_loss_mts[extra_loss_key].update(extra_loss.item(), batch_size)
def _log_meters_and_save_best_ckpts(self, phases: List[str]):
logging.info("Synchronizing meters")
out_dict = {}
checkpoint_save_keys = []
for key, meter in self._get_meters(phases).items():
meter_output = meter.compute_synced()
is_better_check = getattr(meter, "is_better", None)
for meter_subkey, meter_value in meter_output.items():
out_dict[os.path.join("Meters_train", key, meter_subkey)] = meter_value
if is_better_check is None:
continue
tracked_meter_key = os.path.join(key, meter_subkey)
if tracked_meter_key not in self.best_meter_values or is_better_check(
meter_value,
self.best_meter_values[tracked_meter_key],
):
self.best_meter_values[tracked_meter_key] = meter_value
if (
self.checkpoint_conf.save_best_meters is not None
and key in self.checkpoint_conf.save_best_meters
):
checkpoint_save_keys.append(tracked_meter_key.replace("/", "_"))
if len(checkpoint_save_keys) > 0:
self.save_checkpoint(self.epoch + 1, checkpoint_save_keys)
return out_dict
def _log_timers(self, phase):
time_remaining = 0
epochs_remaining = self.max_epochs - self.epoch - 1
val_epochs_remaining = sum(
n % self.val_epoch_freq == 0 for n in range(self.epoch, self.max_epochs)
)
# Adding the guaranteed val run at the end if val_epoch_freq doesn't coincide with
# the end epoch.
if (self.max_epochs - 1) % self.val_epoch_freq != 0:
val_epochs_remaining += 1
# Remove the current val run from estimate
if phase == Phase.VAL:
val_epochs_remaining -= 1
time_remaining += (
epochs_remaining * self.est_epoch_time[Phase.TRAIN]
+ val_epochs_remaining * self.est_epoch_time[Phase.VAL]
)
self.logger.log(
os.path.join("Step_Stats", phase, self.time_elapsed_meter.name),
self.time_elapsed_meter.val,
self.steps[phase],
)
logging.info(f"Estimated time remaining: {human_readable_time(time_remaining)}")
def _reset_meters(self, phases: str) -> None:
for meter in self._get_meters(phases).values():
meter.reset()
def _check_val_key_match(self, val_keys, phase):
if val_keys is not None:
# Check if there are any duplicates
assert len(val_keys) == len(
set(val_keys)
), f"Duplicate keys in val datasets, keys: {val_keys}"
# Check that the keys match the meter keys
if self.meters_conf is not None and phase in self.meters_conf:
assert set(val_keys) == set(self.meters_conf[phase].keys()), (
f"Keys in val datasets do not match the keys in meters."
f"\nMissing in meters: {set(val_keys) - set(self.meters_conf[phase].keys())}"
f"\nMissing in val datasets: {set(self.meters_conf[phase].keys()) - set(val_keys)}"
)
if self.loss_conf is not None:
loss_keys = set(self.loss_conf.keys()) - set(["all"])
assert all([k in loss_keys for k in val_keys]), (
f"Keys in val datasets do not match the keys in losses."
f"\nMissing in losses: {set(val_keys) - loss_keys}"
f"\nMissing in val datasets: {loss_keys - set(val_keys)}"
)
def _setup_components(self):
# Get the keys for all the val datasets, if any
val_phase = Phase.VAL
val_keys = None
if self.data_conf.get(val_phase, None) is not None:
val_keys = collect_dict_keys(self.data_conf[val_phase])
# Additional checks on the sanity of the config for val datasets
self._check_val_key_match(val_keys, phase=val_phase)
logging.info("Setting up components: Model, loss, optim, meters etc.")
self.epoch = 0
self.steps = {Phase.TRAIN: 0, Phase.VAL: 0}
self.logger = Logger(self.logging_conf)
self.model = instantiate(self.model_conf, _convert_="all")
print_model_summary(self.model)
self.loss = None
if self.loss_conf:
self.loss = {