464 lines
16 KiB
Python
464 lines
16 KiB
Python
import torch
|
|
import tqdm
|
|
import numpy as np
|
|
from pytorch_lightning.root_module.memory import get_gpu_memory_map
|
|
import traceback
|
|
from pytorch_lightning.root_module.model_saving import TrainerIO
|
|
from torch.optim.lr_scheduler import MultiStepLR
|
|
from torch.nn import DataParallel
|
|
import pdb
|
|
|
|
try:
|
|
from apex import amp
|
|
APEX_AVAILABLE = True
|
|
except ModuleNotFoundError:
|
|
APEX_AVAILABLE = False
|
|
|
|
class Trainer(TrainerIO):
|
|
|
|
def __init__(self,
|
|
experiment,
|
|
checkpoint_callback, early_stop_callback,
|
|
cluster=None,
|
|
process_position=0,
|
|
current_gpu_name=0,
|
|
gpus=None,
|
|
enable_tqdm=True,
|
|
overfit_pct=0.0,
|
|
track_grad_norm=-1,
|
|
check_val_every_n_epoch=1,
|
|
fast_dev_run=False,
|
|
accumulate_grad_batches=1,
|
|
enable_early_stop=True, max_nb_epochs=5, min_nb_epochs=1,
|
|
train_percent_check=1.0, val_percent_check=1.0, test_percent_check=1.0, val_check_interval=0.95,
|
|
log_save_interval=1, add_log_row_interval=1,
|
|
lr_scheduler_milestones=None,
|
|
use_amp=False,
|
|
check_grad_nans=False,
|
|
amp_level='O2',
|
|
nb_sanity_val_steps=5):
|
|
|
|
# Transfer params
|
|
self.check_val_every_n_epoch = check_val_every_n_epoch
|
|
self.enable_early_stop = enable_early_stop
|
|
self.track_grad_norm = track_grad_norm
|
|
self.fast_dev_run = fast_dev_run
|
|
self.on_gpu = gpus is not None and torch.cuda.is_available()
|
|
self.enable_tqdm = enable_tqdm
|
|
self.experiment = experiment
|
|
self.exp_save_path = experiment.get_data_path(experiment.name, experiment.version)
|
|
self.cluster = cluster
|
|
self.process_position = process_position
|
|
self.current_gpu_name = current_gpu_name
|
|
self.checkpoint_callback = checkpoint_callback
|
|
self.checkpoint_callback.save_function = self.save_checkpoint
|
|
self.early_stop = early_stop_callback
|
|
self.model = None
|
|
self.max_nb_epochs = max_nb_epochs
|
|
self.accumulate_grad_batches = accumulate_grad_batches
|
|
self.early_stop_callback = early_stop_callback
|
|
self.min_nb_epochs = min_nb_epochs
|
|
self.nb_sanity_val_steps = nb_sanity_val_steps
|
|
self.lr_scheduler_milestones = [] if lr_scheduler_milestones is None else [int(x.strip()) for x in lr_scheduler_milestones.split(',')]
|
|
self.lr_schedulers = []
|
|
self.amp_level = amp_level
|
|
self.check_grad_nans = check_grad_nans
|
|
self.data_parallel_device_ids = gpus
|
|
self.data_parallel = gpus is not None and len(gpus) > 0
|
|
|
|
pdb.set_trace()
|
|
|
|
# training state
|
|
self.optimizers = None
|
|
self.prog_bar = None
|
|
self.global_step = 0
|
|
self.current_epoch = 0
|
|
self.total_batches = 0
|
|
|
|
# logging
|
|
self.log_save_interval = log_save_interval
|
|
self.val_check_interval = val_check_interval
|
|
self.add_log_row_interval = add_log_row_interval
|
|
|
|
# dataloaders
|
|
self.tng_dataloader = None
|
|
self.test_dataloader = None
|
|
self.val_dataloader = None
|
|
|
|
# how much of the data to use
|
|
self.__determine_data_use_amount(train_percent_check, val_percent_check, test_percent_check, overfit_pct)
|
|
print('gpu available: {}, used: {}'.format(torch.cuda.is_available(), self.on_gpu))
|
|
|
|
# apex test
|
|
self.use_amp = use_amp and APEX_AVAILABLE
|
|
if self.use_amp:
|
|
print('using 16bit precision')
|
|
|
|
def __determine_data_use_amount(self, train_percent_check, val_percent_check, test_percent_check, overfit_pct):
|
|
"""
|
|
Use less data for debugging purposes
|
|
"""
|
|
self.train_percent_check = train_percent_check
|
|
self.val_percent_check = val_percent_check
|
|
self.test_percent_check = test_percent_check
|
|
if overfit_pct > 0:
|
|
self.train_percent_check = overfit_pct
|
|
self.val_percent_check = overfit_pct
|
|
self.test_percent_check = overfit_pct
|
|
|
|
def __is_function_implemented(self, f_name):
|
|
f_op = getattr(self.model, f_name, None)
|
|
return callable(f_op)
|
|
|
|
@property
|
|
def __tng_tqdm_dic(self):
|
|
tqdm_dic = {
|
|
'tng_loss': '{0:.3f}'.format(self.avg_loss),
|
|
'gpu': '{}'.format(self.current_gpu_name),
|
|
'v_nb': '{}'.format(self.experiment.version),
|
|
'epoch': '{}'.format(self.current_epoch),
|
|
'batch_nb':'{}'.format(self.batch_nb),
|
|
}
|
|
tqdm_dic.update(self.tqdm_metrics)
|
|
return tqdm_dic
|
|
|
|
def __layout_bookeeping(self):
|
|
# training bookeeping
|
|
self.total_batch_nb = 0
|
|
self.running_loss = []
|
|
self.avg_loss = 0
|
|
self.batch_nb = 0
|
|
self.tqdm_metrics = {}
|
|
|
|
# determine number of training batches
|
|
self.nb_tng_batches = self.model.nb_batches(self.tng_dataloader)
|
|
self.nb_tng_batches = int(self.nb_tng_batches * self.train_percent_check)
|
|
|
|
# determine number of validation batches
|
|
self.nb_val_batches = self.model.nb_batches(self.val_dataloader)
|
|
self.nb_val_batches = int(self.nb_val_batches * self.val_percent_check)
|
|
self.nb_val_batches = max(1, self.nb_val_batches)
|
|
self.nb_val_batches = self.nb_val_batches
|
|
|
|
# determine number of test batches
|
|
self.nb_test_batches = self.model.nb_batches(self.test_dataloader)
|
|
self.nb_test_batches = int(self.nb_test_batches * self.test_percent_check)
|
|
|
|
# determine when to check validation
|
|
self.val_check_batch = int(self.nb_tng_batches * self.val_check_interval)
|
|
|
|
def __add_tqdm_metrics(self, metrics):
|
|
for k, v in metrics.items():
|
|
self.tqdm_metrics[k] = v
|
|
|
|
def validate(self, model, dataloader, max_batches):
|
|
"""
|
|
Run validation code
|
|
:param model: PT model
|
|
:param dataloader: PT dataloader
|
|
:param max_batches: Scalar
|
|
:return:
|
|
"""
|
|
print('validating...')
|
|
|
|
# enable eval mode
|
|
model.zero_grad()
|
|
model.eval()
|
|
|
|
# disable gradients to save memory
|
|
torch.set_grad_enabled(False)
|
|
|
|
# bookkeeping
|
|
outputs = []
|
|
|
|
# run training
|
|
for batch_i, data_batch in enumerate(dataloader):
|
|
|
|
if data_batch is None:
|
|
continue
|
|
|
|
# stop short when on fast dev run
|
|
if max_batches is not None and batch_i >= max_batches:
|
|
break
|
|
|
|
# -----------------
|
|
# RUN VALIDATION STEP
|
|
# -----------------
|
|
output = model.validation_step(data_batch, batch_i)
|
|
outputs.append(output)
|
|
|
|
# batch done
|
|
if self.enable_tqdm and self.prog_bar is not None:
|
|
self.prog_bar.update(1)
|
|
|
|
# give model a chance to do something with the outputs
|
|
val_results = model.validation_end(outputs)
|
|
|
|
# enable train mode again
|
|
model.train()
|
|
|
|
# enable gradients to save memory
|
|
torch.set_grad_enabled(True)
|
|
return val_results
|
|
|
|
def __get_dataloaders(self, model):
|
|
"""
|
|
Dataloaders are provided by the model
|
|
:param model:
|
|
:return:
|
|
"""
|
|
self.tng_dataloader = model.tng_dataloader
|
|
self.test_dataloader = model.test_dataloader
|
|
self.val_dataloader = model.val_dataloader
|
|
|
|
# -----------------------------
|
|
# MODEL TRAINING
|
|
# -----------------------------
|
|
def fit(self, model):
|
|
self.model = model
|
|
model.trainer = self
|
|
|
|
# transfer data loaders from model
|
|
self.__get_dataloaders(model)
|
|
|
|
# init training constants
|
|
self.__layout_bookeeping()
|
|
|
|
# CHOOSE OPTIMIZER
|
|
# filter out the weights that were done on gpu so we can load on good old cpus
|
|
self.optimizers = model.configure_optimizers()
|
|
|
|
if self.use_amp:
|
|
# An example
|
|
self.model, optimizer = amp.initialize(
|
|
self.model, self.optimizers[0], opt_level=self.amp_level,
|
|
)
|
|
self.optimizers[0] = optimizer
|
|
model.trainer = self
|
|
|
|
# add lr schedulers
|
|
if self.lr_scheduler_milestones is not None:
|
|
for optimizer in self.optimizers:
|
|
scheduler = MultiStepLR(optimizer, self.lr_scheduler_milestones)
|
|
self.lr_schedulers.append(scheduler)
|
|
|
|
# print model summary
|
|
model.summarize()
|
|
|
|
# put on gpu if needed
|
|
if self.on_gpu:
|
|
if self.data_parallel:
|
|
model = DataParallel(model, device_ids=self.data_parallel_device_ids)
|
|
else:
|
|
model = model.cuda()
|
|
|
|
# run tiny validation to make sure program won't crash during val
|
|
_ = self.validate(model, self.val_dataloader, max_batches=self.nb_sanity_val_steps)
|
|
|
|
# save exp to get started
|
|
self.experiment.save()
|
|
|
|
# enable cluster checkpointing
|
|
if self.cluster is not None:
|
|
self.enable_auto_hpc_walltime_manager()
|
|
|
|
# ---------------------------
|
|
# CORE TRAINING LOOP
|
|
# ---------------------------
|
|
self.__train()
|
|
|
|
def __train(self):
|
|
# run all epochs
|
|
for epoch_nb in range(self.current_epoch, self.max_nb_epochs):
|
|
# update the lr scheduler
|
|
for lr_scheduler in self.lr_schedulers:
|
|
lr_scheduler.step()
|
|
|
|
self.model.current_epoch = epoch_nb
|
|
|
|
# hook
|
|
if self.__is_function_implemented('on_epoch_start'):
|
|
self.model.on_epoch_start()
|
|
|
|
self.current_epoch = epoch_nb
|
|
self.total_batches = self.nb_tng_batches + self.nb_val_batches
|
|
self.batch_loss_value = 0 # accumulated grads
|
|
|
|
# init progbar when requested
|
|
if self.enable_tqdm:
|
|
self.prog_bar = tqdm.tqdm(range(self.total_batches), position=self.process_position)
|
|
|
|
for batch_nb, data_batch in enumerate(self.tng_dataloader):
|
|
self.batch_nb = batch_nb
|
|
self.global_step += 1
|
|
self.model.global_step = self.global_step
|
|
|
|
# stop when the flag is changed or we've gone past the amount requested in the batches
|
|
self.total_batch_nb += 1
|
|
met_batch_limit = batch_nb > self.nb_tng_batches
|
|
if met_batch_limit:
|
|
break
|
|
|
|
# ---------------
|
|
# RUN TRAIN STEP
|
|
# ---------------
|
|
batch_result = self.__run_tng_batch(data_batch, batch_nb)
|
|
early_stop_epoch = batch_result == -1
|
|
|
|
# ---------------
|
|
# RUN VAL STEP
|
|
# ---------------
|
|
is_val_check_batch = (batch_nb + 1) % self.val_check_batch == 0
|
|
if self.fast_dev_run or is_val_check_batch or early_stop_epoch:
|
|
self.__run_validation()
|
|
|
|
# when batch should be saved
|
|
if (batch_nb + 1) % self.log_save_interval == 0 or early_stop_epoch:
|
|
self.experiment.save()
|
|
|
|
# when metrics should be logged
|
|
if batch_nb % self.add_log_row_interval == 0 or early_stop_epoch:
|
|
# count items in memory
|
|
# nb_params, nb_tensors = count_mem_items()
|
|
|
|
metrics = self.model.update_tng_log_metrics(self.__tng_tqdm_dic)
|
|
|
|
# add gpu memory
|
|
if self.on_gpu:
|
|
mem_map = get_gpu_memory_map()
|
|
metrics.update(mem_map)
|
|
|
|
# add norms
|
|
if self.track_grad_norm > 0:
|
|
grad_norm_dic = self.model.grad_norm(self.track_grad_norm)
|
|
metrics.update(grad_norm_dic)
|
|
|
|
# log metrics
|
|
self.experiment.log(metrics)
|
|
self.experiment.save()
|
|
|
|
# hook
|
|
if self.__is_function_implemented('on_batch_end'):
|
|
self.model.on_batch_end()
|
|
|
|
# end epoch early
|
|
if early_stop_epoch:
|
|
break
|
|
|
|
# hook
|
|
if self.__is_function_implemented('on_epoch_end'):
|
|
self.model.on_epoch_end()
|
|
|
|
# early stopping
|
|
if self.enable_early_stop:
|
|
should_stop = self.early_stop_callback.on_epoch_end(epoch=epoch_nb, logs=self.__tng_tqdm_dic)
|
|
met_min_epochs = epoch_nb > self.min_nb_epochs
|
|
|
|
# stop training
|
|
stop = should_stop and met_min_epochs
|
|
if stop:
|
|
return
|
|
|
|
|
|
def __run_tng_batch(self, data_batch, batch_nb):
|
|
if data_batch is None:
|
|
return 0
|
|
|
|
# hook
|
|
if self.__is_function_implemented('on_batch_start'):
|
|
response = self.model.on_batch_start(data_batch)
|
|
if response == -1:
|
|
return -1
|
|
|
|
if self.enable_tqdm:
|
|
self.prog_bar.update(1)
|
|
|
|
# forward pass
|
|
# return a scalar value and a dic with tqdm metrics
|
|
loss, model_specific_tqdm_metrics_dic = self.model.training_step(data_batch, batch_nb)
|
|
self.__add_tqdm_metrics(model_specific_tqdm_metrics_dic)
|
|
|
|
# backward pass
|
|
if self.use_amp:
|
|
for optimizer in self.optimizers:
|
|
with amp.scale_loss(loss, optimizer) as scaled_loss:
|
|
scaled_loss.backward()
|
|
else:
|
|
loss.backward()
|
|
|
|
if self.check_grad_nans:
|
|
for param in self.model.parameters():
|
|
print(param.grad.float().sum())
|
|
|
|
self.batch_loss_value += loss.item()
|
|
|
|
# gradient update with accumulated gradients
|
|
if (self.batch_nb + 1) % self.accumulate_grad_batches == 0:
|
|
|
|
# update gradients across all optimizers
|
|
for optimizer in self.optimizers:
|
|
optimizer.step()
|
|
|
|
# clear gradients
|
|
optimizer.zero_grad()
|
|
|
|
# queuing loss across batches blows it up proportionally... divide out the number accumulated
|
|
self.batch_loss_value = self.batch_loss_value / self.accumulate_grad_batches
|
|
|
|
# track loss
|
|
self.running_loss.append(self.batch_loss_value)
|
|
self.batch_loss_value = 0
|
|
self.avg_loss = np.mean(self.running_loss[-100:])
|
|
|
|
# update progbar
|
|
if self.enable_tqdm:
|
|
# add model specific metrics
|
|
tqdm_metrics = self.__tng_tqdm_dic
|
|
self.prog_bar.set_postfix(**tqdm_metrics)
|
|
|
|
# activate batch end hook
|
|
if self.__is_function_implemented('on_batch_end'):
|
|
self.model.on_batch_end()
|
|
|
|
return 0
|
|
|
|
def __run_validation(self):
|
|
# decide if can check epochs
|
|
can_check_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0
|
|
if self.fast_dev_run:
|
|
print('skipping to check performance bc of --fast_dev_run')
|
|
elif not can_check_epoch:
|
|
return
|
|
|
|
try:
|
|
# hook
|
|
if self.__is_function_implemented('on_pre_performance_check'):
|
|
self.model.on_pre_performance_check()
|
|
|
|
# use full val set on end of epoch
|
|
# use a small portion otherwise
|
|
max_batches = None if not self.fast_dev_run else 1
|
|
model_specific_tqdm_metrics_dic = self.validate(
|
|
self.model,
|
|
self.val_dataloader,
|
|
max_batches
|
|
)
|
|
self.__add_tqdm_metrics(model_specific_tqdm_metrics_dic)
|
|
|
|
# hook
|
|
if self.__is_function_implemented('on_post_performance_check'):
|
|
self.model.on_post_performance_check()
|
|
|
|
except Exception as e:
|
|
print(e)
|
|
print(traceback.print_exc())
|
|
|
|
if self.enable_tqdm:
|
|
# add model specific metrics
|
|
tqdm_metrics = self.__tng_tqdm_dic
|
|
self.prog_bar.set_postfix(**tqdm_metrics)
|
|
|
|
# model checkpointing
|
|
print('save callback...')
|
|
self.checkpoint_callback.on_epoch_end(epoch=self.current_epoch, logs=self.__tng_tqdm_dic)
|