def all_gather_stats_list(stat_list, max_size=4096):
"""
Gather a `Statistics` list accross all processes/nodes
Args:
stat_list(list([`Statistics`])): list of statistics objects to
gather accross all processes/nodes
max_size(int): max buffer size to use
Returns:
our_stats(list([`Statistics`])): list of updated stats
"""
from torch.distributed import get_rank
from onmt.utils.distributed import all_gather_list
# Get a list of world_size lists with len(stat_list) Statistics objects
all_stats = all_gather_list(stat_list, max_size=max_size)
our_rank = get_rank()
our_stats = all_stats[our_rank]
for other_rank, stats in enumerate(all_stats):
if other_rank == our_rank:
continue
for i, stat in enumerate(stats):
our_stats[i].update(stat, update_n_src_words=True)
return our_stats
def _init_group_test(self):
group = [1, 2]
group_id = dist.new_group(group)
rank = dist.get_rank()
if rank not in group:
return ([], None, rank)
return (group, group_id, rank)
def __init__(self, num_replicas=None, rank=None):
if num_replicas is None:
num_replicas = get_world_size()
if rank is None:
rank = get_rank()
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
self.extra = 0
def test_mpi():
dist.init_process_group('mpi')
world_size = dist.get_world_size()
rank = dist.get_rank()
vector = [0] * world_size
vector[rank] = 1
vector = torch.DoubleTensor(vector)
dist.all_reduce(vector, op=dist.reduce_op.SUM)
print("Host {} : Rank {} : {}".format(get_hostname(), rank, vector))
def __init__(self, dataset, num_replicas=None, rank=None):
if num_replicas is None:
num_replicas = get_world_size()
if rank is None:
rank = get_rank()
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
self.num_samples = int(math.ceil(len(self.dataset) / self.num_replicas))
self.total_size = self.num_samples * self.num_replicas
def test_get_rank(self):
test_dir = os.path.join(TEMP_DIR, 'test_dir')
pid = str(os.getpid())
num_processes = dist.get_world_size()
with open(os.path.join(test_dir, pid), 'w') as f:
f.write(str(dist.get_rank()))
self._barrier()
all_ranks = set()
for f_name in os.listdir(test_dir):
with open(os.path.join(test_dir, f_name), 'r') as f:
all_ranks.add(int(f.read()))
self.assertEqual(len(all_ranks), num_processes)
self._barrier()
if dist.get_rank() == 0:
for f_name in os.listdir(test_dir):
os.unlink(os.path.join(test_dir, f_name))
self._barrier()
def __init__(self, data_source, batch_size=1, num_replicas=None, rank=None):
"""
Samples batches assuming they are in order of size to batch similarly sized samples together.
"""
super(DistributedBucketingSampler, self).__init__(data_source)
if num_replicas is None:
num_replicas = get_world_size()
if rank is None:
rank = get_rank()
self.data_source = data_source
self.ids = list(range(0, len(data_source)))
self.batch_size = batch_size
self.bins = [self.ids[i:i + batch_size] for i in range(0, len(self.ids), batch_size)]
self.num_replicas = num_replicas
self.rank = rank
self.num_samples = int(math.ceil(len(self.bins) * 1.0 / self.num_replicas))
self.total_size = self.num_samples * self.num_replicas
def test_send_recv(self):
rank = dist.get_rank()
tensor = _build_tensor(rank + 1)
for dest in range(0, dist.get_world_size()):
if dest == rank:
continue
dist.send(tensor, dest)
for src in range(0, dist.get_world_size()):
if src == rank:
continue
tensor = _build_tensor(src + 1, value=-1)
expected_tensor = _build_tensor(src + 1)
dist.recv(tensor, src)
self.assertEqual(tensor, expected_tensor)
self._barrier()
def test_isend(self):
rank = dist.get_rank()
world_size = dist.get_world_size()
if rank == 0:
requests = [
dist.isend(_build_tensor(dest, 10), dest) for dest in range(1, world_size)
]
for request in requests:
request.wait()
self.assertTrue(request.is_completed())
else:
tensor = _build_tensor(rank, -1)
dist.recv(tensor, 0)
self.assertEqual(tensor, _build_tensor(rank, 10))
self._barrier()
def config_pytorch(options):
"""Config pytorch packages.
Fix random number for packages and initialize distributed environment for pytorch.
Setup cuda environment for pytorch.
:param options: A global object containing specified options.
:type options: argparse.Namespace
"""
# Setting `cudnn.deterministic = True` will turn on
# CUDNN deterministic setting which can slow down training considerably.
# Unexpected behavior may also be observed from checkpoint.
# See: https: // github.com/pytorch/examples/blob/master/imagenet/main.py
if options.cudnn_deterministic:
cudnn.deterministic = True
log.warning('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.', 0)
if options.seed is not None:
random.seed(options.seed)
torch.manual_seed(options.seed)
# define the graph for the computation.
if options.use_cuda:
assert torch.cuda.is_available()
options.rank = dist.get_rank()
options.world_size = dist.get_world_size()
options.graph = FCGraph(options)
# enable cudnn accelerator if we are using cuda.
if options.use_cuda:
options.graph.assigned_gpu_id()
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
if torch.backends.cudnn.version() is None:
log.warning("CUDNN not found on device.")
log.info("World size={}, Rank={}, hostname={}, cuda_available={}, cuda_device={}".format(
options.world_size, options.rank, socket.gethostname(), torch.cuda.is_available(),
torch.cuda.current_device()))
def test_send_recv_any_source(self):
rank = dist.get_rank()
tensor = _build_tensor(10, rank)
for dest in range(0, dist.get_world_size()):
if dest == rank:
continue
dist.send(tensor, dest)
recv_ranks = set()
for src in range(0, dist.get_world_size()):
if src == rank:
continue
tensor = _build_tensor(10, value=-1)
dist.recv(tensor)
recv_ranks.add(tensor.resize_(1)[0])
self.assertEqual(len(recv_ranks), dist.get_world_size() - 1)
self._barrier()
def test_irecv(self):
rank = dist.get_rank()
world_size = dist.get_world_size()
if rank == 0:
expected_tensors = [_build_tensor(src, -1) for src in range(1, world_size)]
requests = [
dist.irecv(expected_tensors[src - 1], src) for src in range(1, world_size)
]
for src in range(1, world_size):
requests[src - 1].wait()
self.assertTrue(requests[src - 1].is_completed())
self.assertEqual(expected_tensors[src - 1], _build_tensor(src, 10))
else:
tensor = _build_tensor(rank, 10)
dist.send(tensor, 0)
self._barrier()
def filter(self, record):
record.rank = dist.get_rank()
return True
def warning(content, who='all'):
if who == 'all' or who == dist.get_rank():
logger.warning("{}".format(content))
def info(content, who='all'):
if who == 'all' or who == dist.get_rank():
logger.info(content)
def train(args):
world_size = len(args.hosts)
is_distributed = world_size > 1
logger.debug('Number of hosts {}. Distributed training - {}'.format(world_size, is_distributed))
use_cuda = args.num_gpus > 0
logger.debug('Number of gpus available - {}'.format(args.num_gpus))
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
device = torch.device('cuda' if use_cuda else 'cpu')
if is_distributed:
# Initialize the distributed environment.
backend = 'gloo'
os.environ['WORLD_SIZE'] = str(world_size)
host_rank = args.hosts.index(args.current_host)
dist.init_process_group(backend=backend, rank=host_rank, world_size=world_size)
logger.info('Initialized the distributed environment: \'{}\' backend on {} nodes. '.format(
backend, dist.get_world_size()) + 'Current host rank is {}. Is cuda available: {}. Number of gpus: {}'.format(
dist.get_rank(), torch.cuda.is_available(), args.num_gpus))
# set the seed for generating random numbers
seed = 1
torch.manual_seed(seed)
if use_cuda:
torch.cuda.manual_seed(seed)
train_sampler, train_loader = _get_train_data_loader(args.data_dir, is_distributed, args.batch_size, **kwargs)
test_loader = _get_test_data_loader(args.data_dir, **kwargs)
logger.debug('Processes {}/{} ({:.0f}%) of train data'.format(
len(train_loader.sampler), len(train_loader.dataset),
100. * len(train_loader.sampler) / len(train_loader.dataset)
))
logger.debug('Processes {}/{} ({:.0f}%) of test data'.format(
len(test_loader.sampler), len(test_loader.dataset),
100. * len(test_loader.sampler) / len(test_loader.dataset)
))
model = Net().to(device)
if is_distributed and use_cuda:
# multi-machine multi-gpu case
logger.debug('Multi-machine multi-gpu: using DistributedDataParallel.')
model = torch.nn.parallel.DistributedDataParallel(model)
elif use_cuda:
# single-machine multi-gpu case
logger.debug('Single-machine multi-gpu: using DataParallel().cuda().')
model = torch.nn.DataParallel(model)
else:
# single-machine or multi-machine cpu case
logger.debug('Single-machine/multi-machine cpu: using DataParallel.')
model = torch.nn.DataParallel(model)
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.5)
log_interval = 100
for epoch in range(1, args.epochs + 1):
if is_distributed:
train_sampler.set_epoch(epoch)
model.train()
for batch_idx, (data, target) in enumerate(train_loader, 1):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
if is_distributed and not use_cuda:
# average gradients manually for multi-machine cpu case only
_average_gradients(model)
optimizer.step()
if batch_idx % log_interval == 0:
logger.debug('Train Epoch: {} [{}/{} ({:.0f}%)] Loss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.sampler),
100. * batch_idx / len(train_loader), loss.item()))
accuracy = test(model, test_loader, device)
save_model(model, args.model_dir)
logger.debug('Overall test accuracy: {}'.format(accuracy))
def train(train_loader, r, optimizer, epoch):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
n = r.num_iterations(loader_size=len(train_loader))
if args.num_minibatches is not None:
n = min(n, args.num_minibatches)
r.train(n)
if not is_first_stage():
train_loader = None
r.set_loader(train_loader)
end = time.time()
epoch_start_time = time.time()
if args.no_input_pipelining:
num_warmup_minibatches = 0
else:
num_warmup_minibatches = r.num_warmup_minibatches
if args.verbose_frequency > 0:
logging.info("Letting in %d warm-up minibatches" %
num_warmup_minibatches)
logging.info("Running training for %d minibatches" % n)
# start num_warmup_minibatches forward passes
for i in range(num_warmup_minibatches):
r.run_forward()
for i in range(n - num_warmup_minibatches):
# perform forward pass
r.run_forward()
# Adjust learning rate
adjust_learning_rate(optimizer, epoch, args.epochs, r, args.lr_policy,
i, n)
if is_last_stage():
# measure accuracy and record loss
output, target, loss = r.output, r.target, r.loss
prec1, prec5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), output.size(0))
top1.update(prec1[0], output.size(0))
top5.update(prec5[0], output.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
epoch_time = (end - epoch_start_time) / 3600.0
full_epoch_time = (epoch_time / float(i + 1)) * float(n)
if i % args.print_freq == 0:
logging.info(
'Epoch: [{0}][{1}/{2}]\t'
'Time: {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Epoch time [hr]: {epoch_time:.3f} ({full_epoch_time:.3f})\t'
'Memory: {memory:.3f}G ({cached_memory:.3f}G)\t'
'Loss: {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1: {top1.val:.2f}% ({top1.avg:.2f}%)\t'
'Prec@5: {top5.val:.2f}% ({top5.avg:.2f}%)'.format(
epoch,
i,
n,
batch_time=batch_time,
epoch_time=epoch_time,
full_epoch_time=full_epoch_time,
loss=losses,
top1=top1,
top5=top5,
memory=(float(torch.cuda.memory_allocated()) / 10**9),
cached_memory=(float(torch.cuda.memory_cached()) /
10**9)))
import sys
sys.stdout.flush()
#print(losses.avg, i)
#print(top1.avg, i)
else:
if i % args.print_freq == 0:
logging.info(
'Epoch: [{0}][{1}/{2}]\tMemory: {memory:.3f}G ({cached_memory:.3f}G)'
.format(epoch,
i,
n,
memory=(float(torch.cuda.memory_allocated()) /
10**9),
cached_memory=(float(torch.cuda.memory_cached()) /
10**9)))
import sys
sys.stdout.flush()
# perform backward pass
if args.fp16:
r.zero_grad()
else:
optimizer.zero_grad()
# consistent
# optimizer.load_old_params()
r.run_backward()
# optimizer.load_new_params()
# s = optimizer.get_s()
if args.square:
s = (dist.get_world_size() - dist.get_rank())**2
else:
s = None
#logging.warning(f'outside: {dist.get_rank()}: {args.find_median}, s = {s}')
if args.spectrain:
optimizer.step(s=s, find_median=args.find_median)
else:
optimizer.step()
# inconsistent
# optimizer.load_old_params()
# r.run_backward()
# optimizer.load_new_params()
# optimizer.step()
global writer
if dist.get_rank() == dist.get_world_size() - 1:
writer.add_scalar('Train/Loss', losses.avg, epoch)
writer.add_scalar('Train/Accuracy', top1.avg, epoch)
# finish remaining backward passes
for i in range(num_warmup_minibatches):
optimizer.zero_grad()
# optimizer.load_old_params()
r.run_backward()
# optimizer.load_new_params()
if args.spectrain:
optimizer.step(s=s, find_median=args.find_median)
else:
optimizer.step()
# wait for all helper threads to complete
r.wait()
logging.info("Epoch %d: %.3f seconds" %
(epoch, time.time() - epoch_start_time))
logging.info("Epoch start time: %.3f, epoch end time: %.3f" %
(epoch_start_time, time.time()))
hidden_dim = 128
input_steps = segment_size
output_steps = segment_size
input_size = 1
output_size = 1
train_idx = list(range(training_size))
valid_idx = list(range(training_size, train_valid_size))
test_idx = list(range(train_valid_size, train_valid_size + test_size))
encoder = Encoder(input_size, hidden_dim, num_layers, dropout_rate)
decoder = Decoder(output_size, hidden_dim, num_layers, dropout_rate)
# to enable multi GPU training
rank = dist.get_rank()
world_size = dist.get_world_size()
print('rank {} and world size {}'.format(rank, world_size))
dist.init_process_group("gloo", rank=rank, world_size=world_size)
print(rank, world_size)
model = Seq2Seq(encoder, decoder, rank).to(rank)
model = DDP(model, device_ids=[rank])
model, loss, preds, min_valid_loss, test_rmse = train_model(
model,
X,
Y,
learning_rate,
output_steps=output_steps,
batch_size=64,
def train(train_loader, model, criterion, optimizer, epoch, log_writer):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
global iter_ptr
train_record.set()
# switch to train mode
model.train()
end = time.time()
torch.cuda.synchronize()
i = -1
#while input is not None:
for input, target in train_loader:
assert input.size(0) == target.size(0)
i += 1
iter_ptr += 1
if args.prof and (i > 200): break
# measure data loading time
data_time.update(time.time() - end)
input = input.cuda(async=True)
target = target.cuda(async=True)
input_var = Variable(input)
target_var = Variable(target)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
if args.distributed:
reduced_loss = reduce_tensor(loss.data)
#reduced_loss = loss.data
prec1 = reduce_tensor(prec1)
prec5 = reduce_tensor(prec5)
else:
reduced_loss = loss.data
losses.update(to_python_float(reduced_loss), input.size(0))
top1.update(to_python_float(prec1), input.size(0))
top5.update(to_python_float(prec5), input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
#input, target = prefetcher.next()
if dist.get_rank() == 0 and i % args.print_freq == 0 and i > 1:
train_record.record()
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})\t'
'Total Training Time {train_time:.3f}'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1, top5=top5, train_time=train_record.get_time()))
train_record.set()
if log_writer:
log_writer.add_scalar('train_iter/top1', top1.get_avg(), iter_ptr)
log_writer.add_scalar('train_iter/top5', top5.get_avg(), iter_ptr)
log_writer.add_scalar('train_iter/loss', losses.get_avg(), iter_ptr)
log_writer.add_scalar('train_iter/batch_time', batch_time.get_avg(), iter_ptr)
log_writer.add_scalar('train_iter/data_time', data_time.get_avg(), iter_ptr)
log_writer.add_scalar('train_iter/learning_rate_schedule', args.lr_present, iter_ptr)
log_writer.add_scalar('train_epoch/top1', top1.get_avg(), epoch)
log_writer.add_scalar('train_epoch/top5', top5.get_avg(), epoch)
log_writer.add_scalar('train_epoch/loss', losses.get_avg(), epoch)
log_writer.add_scalar('train_epoch/learning_rate_schedule', args.lr_present, epoch)
log_writer.add_scalar('train_time/top1', top1.get_avg(), train_record.get_time())
log_writer.add_scalar('train_time/top5', top5.get_avg(), train_record.get_time())
log_writer.add_scalar('train_time/loss', losses.get_avg(), train_record.get_time())
if args.larc_enable:
#add larc_adaptive_lr saving
laryer_saving_name = ['layer0.conv1.weight', 'layer0.bn1.weight', 'layer1.1.conv1.weight', \
'layer2.1.conv1.weight', 'layer3.1.conv1.weight', 'layer4.1.conv1.weight'] #correspond to list laryer_saving in Signum_SGD.py
for index, layer_lr in enumerate(optimizer.layer_adaptive_lr):
log_writer.add_scalar('larc_layer_adaptive_lr/' + laryer_saving_name[index], layer_lr, epoch)
def forward(self,
input,
input_mask=None,
attention_mask=None,
head_mask=None,
layer_past=None,
get_key_value=False,
get_present=False,
encoder_output=None,
enc_dec_attn_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
use_cache=False,
output_attentions=False):
get_present = (get_present or get_key_value or use_cache)
input_mask = input_mask if attention_mask is None else attention_mask
input_type = input.dtype
if (self.config.fp16 or self.config.q_int8) \
and input.dtype == torch.float:
input = input.half()
with torch.no_grad():
attention_output = self.attention(input, input_mask, head_mask,
layer_past, get_present,
encoder_hidden_states,
encoder_attention_mask,
output_attentions, self.norm_w,
self.norm_b)
if get_present:
attention_output, p_key, p_value = attention_output[0:3]
presents = (p_key, p_value)
elif output_attentions:
attention_output, _, _, context_output = attention_output[0:4]
else:
attention_output = attention_output[0]
residual_add = attention_output + self.attention.attn_ob
attention_output = self.ds_layernorm(residual_add, self.attn_nw,
self.attn_nb,
self.config.epsilon)
if self.config.mlp_type == 'residual':
res_mlp_out = self.res_mlp(attention_output, async_op=True)
res_coef_out = self.res_coef_func(attention_output,
async_op=True)
if self.expert_mp_group is not None:
tensor_list = [
torch.empty_like(attention_output) for _ in range(
dist.get_world_size(group=self.expert_mp_group))
]
tensor_list[dist.get_rank(
group=self.expert_mp_group)] = attention_output
dist.all_gather(tensor_list,
attention_output,
group=self.expert_mp_group)
attention_output = torch.cat(tensor_list).contiguous()
############## MoE Gating + Experts ###############
dispatched_attention, combined_weights = self.moe_gate_einsum(
attention_output)
dispatched_input = self._alltoall(dispatched_attention)
expert_outputs = self.expert_exec(dispatched_input)
expert_output = self._alltoall(expert_outputs)
output = self.scale_expert_output(attention_output, expert_output,
combined_weights)
################################################
if self.expert_mp_group is not None:
output = output.split(
output.shape[0] //
dist.get_world_size(group=self.expert_mp_group),
dim=0)[dist.get_rank(group=self.expert_mp_group)]
if self.config.mlp_type == 'residual':
inference_cuda_module.moe_res_matmul(res_mlp_out, res_coef_out,
output)
output = self.bias_residual_func(output, residual_add,
torch.empty(1))
if not self.config.pre_layer_norm:
output = self.ds_layernorm(output, self.norm_w, self.norm_b,
self.config.epsilon)
if input_type != output.dtype:
output = output.to(input_type)
if get_present:
output = (output, presents)
if self.config.return_tuple:
return output if type(output) is tuple else (output, )
else:
return output
def main(run_id, validation_only=False):
r"""Main logic."""
num_parallel_workers = 2
dataset_root = '/datasets/torch/cifar10'
ckpt_run_dir = '/checkpoints/decentralized/cifar_resnet20'
use_cuda = True
train_epochs = 164
initialize_backends(
comm_backend='mpi',
logging_level='INFO',
logging_file='/mlbench.log',
use_cuda=use_cuda,
seed=42,
cudnn_deterministic=False,
ckpt_run_dir=ckpt_run_dir,
delete_existing_ckpts=not validation_only)
rank = dist.get_rank()
world_size = dist.get_world_size()
batch_size = 256 // world_size
model = ResNetCIFAR(
resnet_size=20,
bottleneck=False,
num_classes=10,
version=1)
optimizer = optim.SGD(
model.parameters(),
lr=0.1,
momentum=0.9,
weight_decay=1e-4,
nesterov=True)
# Create a learning rate scheduler for an optimizer
scheduler = MultiStepLR(
optimizer,
milestones=[82, 109],
gamma=0.1)
# A loss_function for computing the loss
loss_function = CrossEntropyLoss()
if use_cuda:
model = model.cuda()
loss_function = loss_function.cuda()
# Metrics like Top 1/5 Accuracy
metrics = [
TopKAccuracy(topk=1),
TopKAccuracy(topk=5)
]
train_set = CIFAR10V1(dataset_root, train=True, download=True)
val_set = CIFAR10V1(dataset_root, train=False, download=True)
train_set = partition_dataset_by_rank(train_set, rank, world_size)
val_set = partition_dataset_by_rank(val_set, rank, world_size)
train_loader = DataLoader(
train_set,
batch_size=batch_size,
shuffle=True,
num_workers=num_parallel_workers,
pin_memory=use_cuda,
drop_last=False)
val_loader = DataLoader(
val_set,
batch_size=batch_size,
shuffle=False,
num_workers=num_parallel_workers,
pin_memory=use_cuda,
drop_last=False)
checkpointer = Checkpointer(
ckpt_run_dir=ckpt_run_dir,
rank=rank,
checkpoint_all=True)
if not validation_only:
# Aggregation
ring_neighbors = [(rank + 1) % world_size, (rank - 1) % world_size]
agg_fn = DecentralizedAggregation(
rank=rank, neighbors=ring_neighbors).agg_model
controlflow = TrainValidation(
model=model,
optimizer=optimizer,
loss_function=loss_function,
metrics=metrics,
scheduler=scheduler,
batch_size=batch_size,
train_epochs=train_epochs,
rank=rank,
world_size=world_size,
run_id=run_id,
dtype='fp32',
validate=True,
schedule_per='epoch',
checkpoint=checkpointer,
transform_target_type=None,
average_models=True,
use_cuda=use_cuda,
max_batch_per_epoch=None,
agg_fn=agg_fn)
controlflow.run(
dataloader_train=train_loader,
dataloader_val=val_loader,
dataloader_train_fn=None,
dataloader_val_fn=None,
resume=False,
repartition_per_epoch=False)
else:
cecf = CheckpointsEvaluationControlFlow(
ckpt_dir=ckpt_run_dir,
rank=rank,
world_size=world_size,
checkpointer=checkpointer,
model=model,
epochs=train_epochs,
loss_function=loss_function,
metrics=metrics,
use_cuda=use_cuda,
dtype='fp32',
max_batch_per_epoch=None)
train_stats = cecf.evaluate_by_epochs(train_loader)
with open(os.path.join(ckpt_run_dir, "train_stats.json"), 'w') as f:
json.dump(train_stats, f)
val_stats = cecf.evaluate_by_epochs(val_loader)
with open(os.path.join(ckpt_run_dir, "val_stats.json"), 'w') as f:
json.dump(val_stats, f)
def train(verbose=True, **kwargs):
args = kwargs['args']
torch.cuda.set_device(args.local_rank)
dist.init_process_group(backend='nccl',
init_method='tcp://127.0.0.1:{}'.format(cfg.port),
world_size=torch.cuda.device_count(),
rank=args.local_rank)
setup_logger(cfg.respth)
logger = logging.getLogger()
## dataset
ds = CityScapes(cfg, mode='train_val')
sampler = torch.utils.data.distributed.DistributedSampler(ds)
dl = DataLoader(ds,
batch_size=cfg.ims_per_gpu,
shuffle=False,
sampler=sampler,
num_workers=cfg.n_workers,
pin_memory=True,
drop_last=True)
## model
net = EaNet(cfg)
net.cuda()
it_start = 0
n_epoch = 0
## optimizer
optim = Optimizer(
net,
cfg.lr_start,
cfg.momentum,
cfg.weight_decay,
cfg.warmup_steps,
cfg.warmup_start_lr,
cfg.max_iter,
cfg.lr_power,
# start_iter = it_start
)
## resume
if cfg.resume:
print("=> loading checkpoint '{}'".format(cfg.resume))
checkpoint = torch.load(cfg.resume)
if '.tar' in cfg.resume:
net.load_state_dict(checkpoint['model'])
optim.optim.load_state_dict(checkpoint['optimizer'])
# it_start = checkpoint['it']
n_epoch = checkpoint['epoch']
bestMIOU = checkpoint['mIOU']
# optim.it = it_start
print('Pth.Tar Load model from {}'.format(cfg.resume))
else:
net.load_state_dict(checkpoint)
print('Pth Load model from {}'.format(cfg.resume))
print('pretrained model loaded')
net.eval()
evaluator = MscEval(cfg)
mIOU = evaluator(net)
print('mIOU start from %f' % mIOU)
del checkpoint
net.train()
net = nn.parallel.DistributedDataParallel(net,
device_ids=[
args.local_rank,
],
output_device=args.local_rank)
n_min = cfg.ims_per_gpu * cfg.crop_size[0] * cfg.crop_size[1] // 16
#criteria = OhemCELoss(thresh=cfg.ohem_thresh, n_min=n_min).cuda()
criteria = ECELoss(thresh=cfg.ohem_thresh,
n_min=n_min,
n_classes=cfg.n_classes,
alpha=cfg.alpha,
radius=cfg.radius,
beta=cfg.beta,
ignore_lb=cfg.ignore_label,
mode=cfg.mode).cuda()
## train loop
loss_avg = []
st = glob_st = time.time()
diter = iter(dl)
# n_epoch = 0
counter = 0
#count for the epoch finished
#已经跑结束的epoch
epochF = 0
bestMIOU = 0
for it in range(it_start, cfg.max_iter):
try:
im, lb = next(diter)
if not im.size()[0] == cfg.ims_per_gpu: continue
except StopIteration:
n_epoch += 1
sampler.set_epoch(n_epoch)
diter = iter(dl)
im, lb = next(diter)
im = im.cuda()
lb = lb.cuda()
H, W = im.size()[2:]
lb = torch.squeeze(lb, 1)
try:
optim.zero_grad()
logits = net(im)
loss = criteria(logits, lb)
loss.backward()
optim.step()
except RuntimeError as e:
if 'out of memory' in e:
print('| WARNING: run out of memory')
if hasattr(troch.cuda, 'empty_cach'):
torch.cuda.empty_cache()
else:
raise e
'''
logits = net(im)
loss = criteria(logits, lb)
loss = loss / (cfg.ims_per_gpu)
counter += 1
loss.backward()
if counter == cfg.ims_per_gpu:
optim.step()
optim.zero_grad()
counter = 0
'''
loss_avg.append(loss.item())
## print training log message
if it % cfg.msg_iter == 0 and not it == 0:
loss_avg = sum(loss_avg) / len(loss_avg)
lr = optim.lr
ed = time.time()
t_intv, glob_t_intv = ed - st, ed - glob_st
eta = int((cfg.max_iter - it) * (glob_t_intv / it))
eta = str(datetime.timedelta(seconds=eta))
msg = ', '.join([
'iter: {it}/{max_it}',
'lr: {lr:4f}',
'loss: {loss:.4f}',
'eta: {eta}',
'time: {time:.4f}',
]).format(it=it,
max_it=cfg.max_iter,
lr=lr,
loss=loss_avg,
time=t_intv,
eta=eta)
logger.info(msg)
loss_avg = []
st = ed
#每隔一段时间评估一次
if n_epoch > epochF and n_epoch > 20:
#置为相等的了
epochF = n_epoch
#if (n_epoch > 35) and it%(5*cfg.msg_iter) == 0 and not it==0:
# net.cpu()
# save_pth = osp.join(cfg.respth, 'model_final_best.pth')
# state = net.module.state_dict() if hasattr(net, 'module') else net.state_dict()
# if dist.get_rank()==0: torch.save(state, save_pth)
# logger.info('training done, model saved to: {}'.format(save_pth))
# logger.info('evaluating the final model')
# net.cuda()
net.eval()
evaluator = MscEval(cfg)
mIOU = evaluator(net)
logger.info('mIOU is: {}'.format(mIOU))
# 保存check point
save_pth = osp.join(cfg.respth, 'checkpoint.pth.tar')
state = net.module.state_dict() if hasattr(
net, 'module') else net.state_dict()
if dist.get_rank() == 0:
stateF = {
'model': state,
'lr': optim.lr,
'mIOU': mIOU,
'it': it,
'epoch': n_epoch,
'optimizer': optim.optim.state_dict(),
}
torch.save(stateF, save_pth)
if mIOU > bestMIOU:
logger.info('Get a new best mIMOU:{} at epoch:{}'.format(
bestMIOU, n_epoch))
#print('Get a new best mIMOU:{}'.format(bestMIOU))
bestMIOU = mIOU
#net.cpu()
save_pth = osp.join(cfg.respth,
'model_final_{}.pth'.format(n_epoch))
state = net.module.state_dict() if hasattr(
net, 'module') else net.state_dict()
if dist.get_rank() == 0: torch.save(state, save_pth)
#重新加载到cuda
#net.cuda()
net.train()
if verbose:
net.cpu()
save_pth = osp.join(cfg.respth, 'model_final.pth.rar')
state = net.module.state_dict() if hasattr(
net, 'module') else net.state_dict()
stateF = {
'model': state,
'lr': optim.lr,
'mIOU': mIOU,
'it': it,
'epoch': n_epoch,
'optimizer': optim.optim.state_dict(),
}
torch.save(stateF, save_pth)
#if dist.get_rank()==0: torch.save(state, save_pth)
logger.info('training done, model saved to: {}'.format(save_pth))
logger.info('evaluating the final model')
net.cuda()
net.eval()
evaluator = MscEval(cfg)
mIOU = evaluator(net)
logger.info('mIOU is: {}'.format(mIOU))
def get_rank():
try:
return dist.get_rank()
except:
return None
def train_epoch(self, data_loader):
self.model.train()
num_ckpt = int(np.ceil(len(data_loader) / 10))
meter_loss = tnt.meter.MovingAverageValueMeter(
len(data_loader) // 100 + 1)
#meter_accuracy = tnt.meter.ClassErrorMeter(accuracy=True)
#meter_confusion = tnt.meter.ConfusionMeter(p.NUM_CTC_LABELS, normalized=True)
if self.lr_scheduler is not None:
self.lr_scheduler.step()
logger.debug(f"current lr = {self.lr_scheduler.get_lr()}")
if is_distributed() and data_loader.sampler is not None:
data_loader.sampler.set_epoch(self.epoch)
# count the number of supervised batches seen in this epoch
t = tqdm(enumerate(data_loader),
total=len(data_loader),
desc="training")
for i, (data) in t:
loss_value = self.unit_train(data)
meter_loss.add(loss_value)
t.set_description(f"training (loss: {meter_loss.value()[0]:.3f})")
t.refresh()
#self.meter_accuracy.add(ys_int, ys)
#self.meter_confusion.add(ys_int, ys)
if 0 < i < len(data_loader) and i % num_ckpt == 0:
if not is_distributed() or (is_distributed()
and dist.get_rank() == 0):
title = "train"
x = self.epoch + i / len(data_loader)
if logger.visdom is not None:
logger.visdom.add_point(title=title,
x=x,
y=meter_loss.value()[0])
if logger.tensorboard is not None:
logger.tensorboard.add_graph(self.model, xs)
xs_img = tvu.make_grid(xs[0, 0],
normalize=True,
scale_each=True)
logger.tensorboard.add_image('xs', x, xs_img)
ys_hat_img = tvu.make_grid(ys_hat[0].transpose(0, 1),
normalize=True,
scale_each=True)
logger.tensorboard.add_image('ys_hat', x, ys_hat_img)
logger.tensorboard.add_scalars(
title, x, {
'loss': meter_loss.value()[0],
})
if self.checkpoint:
logger.info(
f"training loss at epoch_{self.epoch:03d}_ckpt_{i:07d}: "
f"{meter_loss.value()[0]:5.3f}")
if not is_distributed() or (is_distributed()
and dist.get_rank() == 0):
self.save(
self.__get_model_name(
f"epoch_{self.epoch:03d}_ckpt_{i:07d}"))
#input("press key to continue")
self.epoch += 1
logger.info(f"epoch {self.epoch:03d}: "
f"training loss {meter_loss.value()[0]:5.3f} ")
#f"training accuracy {meter_accuracy.value()[0]:6.3f}")
if not is_distributed() or (is_distributed() and dist.get_rank() == 0):
self.save(self.__get_model_name(f"epoch_{self.epoch:03d}"))
self.__remove_ckpt_files(self.epoch - 1)
def main():
args = parse_args()
# Devices
if args.local_rank == -1:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
torch.distributed.init_process_group(backend="nccl")
default_gpu = False
if dist.is_available() and args.local_rank != -1:
rank = dist.get_rank()
if rank == 0:
default_gpu = True
else:
default_gpu = True
logger.info(
f"device: {device} n_gpu: {n_gpu}, distributed training: {bool(args.local_rank != -1)}"
)
# Load config
config = BertConfig.from_json_file(args.config_file)
# Load task config
with open(args.tasks_config_file, "r") as f:
task_cfg = edict(yaml.safe_load(f))
task_id = args.task.strip()
task = "TASK" + task_id
task_name = task_cfg[task]["name"]
if task_cfg[task].get("fusion_method", None):
# VL-BERT pooling for VQA
config.fusion_method = task_cfg[task]["fusion_method"]
# Output dirs
timeStamp = args.from_pretrained.split("/")[-1] + "-" + args.save_name
savePath = os.path.join(args.output_dir, timeStamp)
if default_gpu and not os.path.exists(savePath):
os.makedirs(savePath)
# Seed
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# Dataset
batch_size, task2num_iters, dset_val, dl_val = LoadDatasetEval(
args, config, task_cfg, args.task)
# Logging
tb_logger = tbLogger(timeStamp,
savePath, [task_name], [task],
task2num_iters,
1,
save_logger=False,
txt_name="eval.txt")
# Model
model = BertForVLTasks.from_pretrained(args.from_pretrained,
config=config,
task_cfg=task_cfg,
task_ids=[task])
# Optimization details
criterion = LoadLoss(task_cfg, args.task)
# Move to GPU(s)
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
model = DDP(model, delay_allreduce=True)
elif n_gpu > 1:
model = nn.DataParallel(model)
# Print summary
if default_gpu:
print("***** Running evaluation *****")
print(" Num Iters: ", task2num_iters[task])
print(" Batch size: ", batch_size)
# Evaluate
model.eval()
results = []
others = []
for i, batch in tqdm(enumerate(dl_val), total=task2num_iters[task]):
loss, score, batch_size, results, others = EvaluatingModel(
config, task_cfg, device, task, batch, model, dl_val, criterion,
results, others)
tb_logger.step_val(0, float(loss), float(score), task, batch_size,
"val")
sys.stdout.write("%d/%d\r" % (i, len(dl_val)))
sys.stdout.flush()
# save the result or evaluate the result.
ave_score = tb_logger.showLossVal(task)
if args.split:
json_path = os.path.join(savePath, args.split)
else:
json_path = os.path.join(savePath, task_cfg[task]["val_split"])
json.dump(results, open(json_path + "_result.json", "w"))
json.dump(others, open(json_path + "_others.json", "w"))
def load_model(args):
# Prepare GLUE task
args.task_name = args.task_name.lower()
args.output_mode = "classification"
label_list = ["0", "1"]
num_labels = len(label_list)
# store args
if args.local_rank != -1:
args.world_size = torch.distributed.get_world_size()
args.rank = dist.get_rank()
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
# Make sure only the first process in distributed training will
# download model & vocab
torch.distributed.barrier()
args.train_model_type = args.train_model_type.lower()
configObj = MSMarcoConfigDict[args.train_model_type]
if 'fairseq' not in args.train_model_type:
config = configObj.config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
finetuning_task=args.task_name,
cache_dir=args.cache_dir if args.cache_dir else None,
)
tokenizer = configObj.tokenizer_class.from_pretrained(
args.tokenizer_name
if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None,
)
model = configObj.model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None,
)
elif 'fast' in args.train_model_type:
config = configObj.config_class.from_pretrained(
'roberta-base',
num_labels=num_labels,
finetuning_task=args.task_name,
cache_dir=args.cache_dir if args.cache_dir else None,
)
#print('???',args.model_name_or_path)
model = configObj.model_class(config)
#print('???',model.state_dict()['encoder.layers.1.fc2.weight'])
#print('???',model.state_dict().keys())
if os.path.isdir(args.model_name_or_path):
model.from_pretrained(
os.path.join(args.model_name_or_path, args.model_file))
else:
model.from_pretrained(os.path.join(args.model_name_or_path))
#print('???',model.state_dict()['encoder.layers.1.fc2.weight'])
tokenizer = BertWordPieceTokenizer(args.bpe_vocab_file,
clean_text=False,
strip_accents=False,
lowercase=False)
else:
config = configObj.config_class.from_pretrained(
'roberta-base',
num_labels=args.num_labels,
finetuning_task=args.task_name,
cache_dir=args.cache_dir if args.cache_dir else None,
)
model = configObj.model_class(config)
model.from_pretrained(
os.path.join(args.model_name_or_path, args.model_file))
tokenizer = torch.hub.load('pytorch/fairseq', 'roberta.base')
if args.local_rank == 0:
# Make sure only the first process in distributed training will
# download model & vocab
torch.distributed.barrier()
model.to(args.device)
return tokenizer, model
def _init_global_test(self):
group = [i for i in range(0, dist.get_world_size())]
group_id = dist.group.WORLD
rank = dist.get_rank()
return (group, group_id, rank)
def check_write_log():
return dist.get_rank(
) == 0 or not use_multigpu_with_single_device_per_process
def _run_train(device: torch.device, is_distributed: bool):
serialize_dir = os.path.join(ROOT_PATH, "data/easytext/tests/trainer/save_and_load")
if is_distributed:
if TorchDist.get_rank() == 0:
if os.path.isdir(serialize_dir):
shutil.rmtree(serialize_dir)
os.makedirs(serialize_dir)
TorchDist.barrier()
else:
if os.path.isdir(serialize_dir):
shutil.rmtree(serialize_dir)
os.makedirs(serialize_dir)
model = ModelDemo()
optimizer_factory = _DemoOptimizerFactory()
loss = _DemoLoss()
metric = _DemoMetric()
tensorboard_log_dir = "data/tensorboard"
tensorboard_log_dir = os.path.join(ROOT_PATH, tensorboard_log_dir)
# shutil.rmtree(tensorboard_log_dir)
trainer = Trainer(num_epoch=100,
model=model,
loss=loss,
metrics=metric,
optimizer_factory=optimizer_factory,
serialize_dir=serialize_dir,
patient=20,
num_check_point_keep=25,
device=device,
trainer_callback=None,
is_distributed=is_distributed
)
logging.info(f"test is_distributed: {is_distributed}")
# trainer_callback = BasicTrainerCallbackComposite(tensorboard_log_dir=tensorboard_log_dir)
train_dataset = _DemoDataset()
if is_distributed:
sampler = DistributedSampler(dataset=train_dataset)
else:
sampler = None
train_data_loader = DataLoader(dataset=train_dataset,
collate_fn=_DemoCollate(),
batch_size=200,
num_workers=0,
sampler=sampler)
if is_distributed:
sampler = DistributedSampler(dataset=train_dataset)
else:
sampler = None
validation_data_loader = DataLoader(dataset=train_dataset,
collate_fn=_DemoCollate(),
batch_size=200,
num_workers=0,
sampler=sampler)
trainer.train(train_data_loader=train_data_loader,
validation_data_loader=validation_data_loader)
expect_model_state_dict = json.loads(json2str(trainer.model.state_dict()))
expect_optimizer_state_dict = json.loads(json2str(trainer.optimizer.state_dict()))
expect_current_epoch = trainer.current_epoch
expect_num_epoch = trainer.num_epoch
expect_metric = trainer.metrics.metric[0]
expect_metric_tracker = json.loads(json2str(trainer.metric_tracker))
trainer.load_checkpoint(serialize_dir=serialize_dir)
loaded_model_state_dict = json.loads(json2str(trainer.model.state_dict()))
loaded_optimizer_state_dict = json.loads(json2str(trainer.optimizer.state_dict()))
current_epoch = trainer.current_epoch
num_epoch = trainer.num_epoch
metric = trainer.metrics.metric[0]
metric_tracker = json.loads(json2str(trainer.metric_tracker))
ASSERT.assertDictEqual(expect_model_state_dict, loaded_model_state_dict)
ASSERT.assertDictEqual(expect_optimizer_state_dict, loaded_optimizer_state_dict)
ASSERT.assertEqual(expect_current_epoch, current_epoch)
ASSERT.assertEqual(expect_num_epoch, num_epoch)
ASSERT.assertDictEqual(expect_metric, metric)
ASSERT.assertDictEqual(expect_metric_tracker, metric_tracker)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--bert_model",
default="bert-base-uncased",
type=str,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.",
)
parser.add_argument(
"--from_pretrained",
default="bert-base-uncased",
type=str,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.",
)
parser.add_argument(
"--output_dir",
default="results",
type=str,
help=
"The output directory where the model checkpoints will be written.",
)
parser.add_argument(
"--config_file",
default="config/bert_base_6layer_interbert.json",
type=str,
help="The config file which specified the model details.",
)
parser.add_argument("--no_cuda",
action="store_true",
help="Whether not to use CUDA when available")
parser.add_argument(
"--do_lower_case",
default=True,
type=bool,
help=
"Whether to lower case the input text. True for uncased models, False for cased models.",
)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument("--seed",
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
"--fp16",
action="store_true",
help="Whether to use 16-bit float precision instead of 32-bit",
)
parser.add_argument(
"--loss_scale",
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n",
)
parser.add_argument("--num_workers",
type=int,
default=16,
help="Number of workers in the dataloader.")
parser.add_argument(
"--save_name",
default='',
type=str,
help="save name for training.",
)
parser.add_argument("--tasks",
default='',
type=str,
help="1-2-3... training task separate by -")
parser.add_argument("--in_memory",
default=False,
type=bool,
help="whether use chunck for parallel training.")
parser.add_argument("--zero_shot",
action="store_true",
help="whether use single stream baseline.")
parser.add_argument("--split",
default="",
type=str,
help="which split to use.")
parser.add_argument("--batch_size",
default=1,
type=int,
help="which split to use.")
args = parser.parse_args()
with open('interbert_tasks.yml', 'r') as f:
task_cfg = edict(yaml.safe_load(f))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
from bertmodel.modules import BertConfig
task_names = []
for i, task_id in enumerate(args.tasks.split('-')):
task = 'TASK' + task_id
name = task_cfg[task]['name']
task_names.append(name)
# timeStamp = '-'.join(task_names) + '_' + args.config_file.split('/')[1].split('.')[0]
if '/' in args.from_pretrained:
timeStamp = args.from_pretrained.split('/')[1]
else:
timeStamp = args.from_pretrained
savePath = os.path.join(args.output_dir, timeStamp)
config = BertConfig.from_json_file(args.config_file)
bert_weight_name = json.load(
open("config/" + "bert-base-uncased_weight_name.json", "r"))
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend="nccl")
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
default_gpu = False
if dist.is_available() and args.local_rank != -1:
rank = dist.get_rank()
if rank == 0:
default_gpu = True
else:
default_gpu = True
if default_gpu and not os.path.exists(savePath):
os.makedirs(savePath)
task_batch_size, task_num_iters, task_ids, task_datasets_val, task_dataloader_val \
= LoadDatasetEval(args, task_cfg, args.tasks.split('-'))
num_labels = max(
[dataset.num_labels for dataset in task_datasets_val.values()])
config.fast_mode = True
if args.zero_shot:
model = InterBertForMultiModalPreTraining.from_pretrained(
args.from_pretrained, config)
else:
model = InterBertForVLTasks.from_pretrained(args.from_pretrained,
config,
num_labels=num_labels,
default_gpu=default_gpu)
task_losses = LoadLosses(args, task_cfg, args.tasks.split('-'))
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
model = DDP(model, deay_allreduce=True)
elif n_gpu > 1:
model = nn.DataParallel(model)
no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
print(" Num Iters: ", task_num_iters)
print(" Batch size: ", task_batch_size)
model.eval()
# when run evaluate, we run each task sequentially.
for task_id in task_ids:
results = []
others = []
score_matrix = np.zeros((5000, 1000))
target_matrix = np.zeros((5000, 1000))
rank_matrix = np.ones((5000)) * 1000
count = 0
for i, batch in enumerate(task_dataloader_val[task_id]):
batch = tuple(
t.cuda(device=device, non_blocking=True) for t in batch)
features, spatials, image_mask, question, target, input_mask, segment_ids, caption_idx, image_idx = batch
if task_id in ['TASK3']:
batch_size = features.size(0)
features = features.squeeze(0)
spatials = spatials.squeeze(0)
image_mask = image_mask.squeeze(0)
multimodal_mask = torch.cat(
(image_mask, input_mask.expand(image_mask.size(0), -1)),
dim=-1)
question = question.expand(features.size(0), -1)
with torch.no_grad():
if args.zero_shot:
_, _, vil_logit, _ = model(question,
features,
spatials,
segment_ids,
input_mask,
image_mask,
multimodal_mask=multimodal_mask)
else:
_, _, vil_logit, _, _, _, _ = model(
question,
features,
spatials,
segment_ids,
input_mask,
image_mask,
multimodal_mask=multimodal_mask)
score_matrix[caption_idx, image_idx * 500:(image_idx + 1) *
500] = torch.softmax(
vil_logit, dim=1)[:,
0].view(-1).cpu().numpy()
target_matrix[caption_idx, image_idx * 500:(image_idx + 1) *
500] = target.view(-1).float().cpu().numpy()
if image_idx.item() == 1:
rank = np.where(
(np.argsort(-score_matrix[caption_idx]) == np.where(
target_matrix[caption_idx] == 1)[0][0]) == 1)[0][0]
rank_matrix[caption_idx] = rank
rank_matrix_tmp = rank_matrix[:caption_idx + 1]
r1 = 100.0 * np.sum(
rank_matrix_tmp < 1) / len(rank_matrix_tmp)
r5 = 100.0 * np.sum(
rank_matrix_tmp < 5) / len(rank_matrix_tmp)
r10 = 100.0 * np.sum(
rank_matrix_tmp < 10) / len(rank_matrix_tmp)
medr = np.floor(np.median(rank_matrix_tmp) + 1)
meanr = np.mean(rank_matrix_tmp) + 1
print(
"%d Final r1:%.3f, r5:%.3f, r10:%.3f, mder:%.3f, meanr:%.3f"
% (count, r1, r5, r10, medr, meanr))
results.append(
np.argsort(-score_matrix[caption_idx]).tolist()[:20])
count += 1
r1 = 100.0 * np.sum(rank_matrix < 1) / len(rank_matrix)
r5 = 100.0 * np.sum(rank_matrix < 5) / len(rank_matrix)
r10 = 100.0 * np.sum(rank_matrix < 10) / len(rank_matrix)
medr = np.floor(np.median(rank_matrix) + 1)
meanr = np.mean(rank_matrix) + 1
print("************************************************")
print("Final r1:%.3f, r5:%.3f, r10:%.3f, mder:%.3f, meanr:%.3f" %
(r1, r5, r10, medr, meanr))
print("************************************************")
if args.split:
json_path = os.path.join(savePath, args.split)
else:
json_path = os.path.join(savePath, task_cfg[task_id]['val_split'])
json.dump(results, open(json_path + '_result.json', 'w'))
json.dump(others, open(json_path + '_others.json', 'w'))
def main():
global args, best_prec1
args = parser.parse_args()
if int(args.rank) == int(args.world_size) - 1:
log_level = logging.INFO
else:
log_level = logging.WARNING
logging.basicConfig(
level=log_level,
format="[%(asctime)s] %(name)s:%(levelname)s: %(message)s")
logging.info(f'Find median: {args.find_median}')
logging.warning(
f'master addr: {args.master_addr}, rank:{args.rank}, local_rank:{args.local_rank}'
)
torch.cuda.set_device(args.local_rank)
# define loss function (criterion)
criterion = nn.CrossEntropyLoss()
# create stages of the model
module = importlib.import_module(args.module)
args.arch = module.arch()
model = module.model(criterion)
# determine shapes of all tensors in passed-in model
if args.arch == 'inception_v3':
input_size = [args.batch_size, 3, 299, 299]
else:
#input_size = [args.batch_size, 3, 224, 224]
input_size = [args.batch_size, 3, 32, 32]
training_tensor_shapes = {
"input0": input_size,
"target": [args.batch_size]
}
dtypes = {"input0": torch.int64, "target": torch.int64}
inputs_module_destinations = {"input": 0}
target_tensor_names = {"target"}
for (stage, inputs, outputs) in model[:-1]: # Skip last layer (loss).
input_tensors = []
for input in inputs:
input_tensor = torch.zeros(tuple(training_tensor_shapes[input]),
dtype=torch.float32)
input_tensors.append(input_tensor)
with torch.no_grad():
output_tensors = stage(*tuple(input_tensors))
if not type(output_tensors) is tuple:
output_tensors = [output_tensors]
for output, output_tensor in zip(outputs, list(output_tensors)):
training_tensor_shapes[output] = list(output_tensor.size())
dtypes[output] = output_tensor.dtype
eval_tensor_shapes = {}
for key in training_tensor_shapes:
eval_tensor_shapes[key] = tuple([args.eval_batch_size] +
training_tensor_shapes[key][1:])
training_tensor_shapes[key] = tuple(training_tensor_shapes[key])
configuration_maps = {
'module_to_stage_map': None,
'stage_to_rank_map': None,
'stage_to_depth_map': None
}
if args.config_path is not None:
json_config_file = json.load(open(args.config_path, 'r'))
configuration_maps['module_to_stage_map'] = json_config_file.get(
"module_to_stage_map", None)
configuration_maps['stage_to_rank_map'] = json_config_file.get(
"stage_to_rank_map", None)
configuration_maps['stage_to_rank_map'] = {
int(k): v
for (k, v) in configuration_maps['stage_to_rank_map'].items()
}
configuration_maps['stage_to_depth_map'] = json_config_file.get(
"stage_to_depth_map", None)
r = runtime.StageRuntime(
model=model,
distributed_backend=args.distributed_backend,
fp16=args.fp16,
loss_scale=args.loss_scale,
training_tensor_shapes=training_tensor_shapes,
eval_tensor_shapes=eval_tensor_shapes,
training_tensor_dtypes=dtypes,
inputs_module_destinations=inputs_module_destinations,
target_tensor_names=target_tensor_names,
configuration_maps=configuration_maps,
master_addr=args.master_addr,
rank=args.rank,
local_rank=args.local_rank,
num_ranks_in_server=args.num_ranks_in_server,
verbose_freq=args.verbose_frequency,
model_type=runtime.IMAGE_CLASSIFICATION,
port=args.port,
enable_recompute=args.recompute)
# stage needed to determine if current stage is the first stage
# num_stages needed to determine if current stage is the last stage
# num_ranks needed to determine number of warmup_minibatches in case of pipelining
args.stage = r.stage
args.num_stages = r.num_stages
args.num_ranks = r.num_ranks
if not is_first_stage():
args.synthetic_data = True
# define optimizer
if args.no_input_pipelining:
num_versions = 1
else:
# number of versions is the total number of machines following the current
# stage, shared amongst all replicas in this stage
num_versions = r.num_warmup_minibatches + 1
# if specified, resume from checkpoint
if args.resume:
checkpoint_file_path = "%s.%d.pth.tar" % (args.resume, r.stage)
assert os.path.isfile(checkpoint_file_path)
logging.info("=> loading checkpoint '{}'".format(checkpoint_file_path))
checkpoint = torch.load(checkpoint_file_path)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
r.load_state_dict(checkpoint['state_dict'])
logging.info("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_file_path, checkpoint['epoch']))
#optimizer = sgd.SGDWithWeightStashing(r.modules(), r.master_parameters,
if args.spectrain:
if args.log_dir != None:
args.log_dir += '_spectrain'
logging.info('Using spectrain')
if args.square:
if args.log_dir != None:
args.log_dir += '_square'
logging.info('s = version difference ^ 2')
else:
logging.info('s = version difference')
optimizer = sgd.SGDWithSpectrain(r.modules(),
r.master_parameters,
r.model_parameters,
args.loss_scale,
num_versions=num_versions,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
verbose_freq=args.verbose_frequency,
macrobatch=args.macrobatch)
else:
logging.info('Not using spectrain')
optimizer = sgd.SGDWithWeightStashing(
r.modules(),
r.master_parameters,
r.model_parameters,
args.loss_scale,
num_versions=num_versions,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
verbose_freq=args.verbose_frequency,
macrobatch=args.macrobatch)
logging.info(f'log_dir: {args.log_dir}')
if args.resume:
optimizer.load_state_dict(checkpoint['optimizer'])
cudnn.benchmark = True
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
if args.arch == 'inception_v3':
if args.synthetic_data:
train_dataset = SyntheticDataset((3, 299, 299), 10000)
else:
traindir = os.path.join(args.data_dir, 'train')
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(299),
transforms.ToTensor(),
normalize,
]))
else:
if args.synthetic_data:
train_dataset = SyntheticDataset((3, 224, 224), 50000)
else:
traindir = os.path.join(args.data_dir, 'train')
transform_train = transforms.Compose([
#transforms.RandomResizedCrop(224),
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
#train_dataset = datasets.ImageFolder(
train_dataset = datasets.CIFAR10(
traindir,
True,
transform_train,
#transforms.Compose([
# transforms.RandomResizedCrop(224),
# transforms.RandomHorizontalFlip(),
# transforms.ToTensor(),
# normalize,
#]),
download=True)
if args.synthetic_data:
val_dataset = SyntheticDataset((3, 224, 224), 10000)
else:
valdir = os.path.join(args.data_dir, 'val')
transform_test = transforms.Compose([
#transforms.RandomResizedCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
#val_dataset = datasets.ImageFolder(valdir, transforms.Compose([
val_dataset = datasets.CIFAR10(
valdir,
False,
transform_test,
#transforms.Compose([
# transforms.Resize(256),
# transforms.CenterCrop(224),
# transforms.ToTensor(),
# normalize,
#]),
download=True,
)
global writer
if dist.get_rank() == dist.get_world_size() - 1:
writer = SummaryWriter(args.log_dir)
distributed_sampler = False
train_sampler = None
val_sampler = None
if configuration_maps['stage_to_rank_map'] is not None:
num_ranks_in_first_stage = len(
configuration_maps['stage_to_rank_map'][0])
if num_ranks_in_first_stage > 1:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=num_ranks_in_first_stage,
rank=args.rank)
val_sampler = torch.utils.data.distributed.DistributedSampler(
val_dataset,
num_replicas=num_ranks_in_first_stage,
rank=args.rank)
distributed_sampler = True
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler,
drop_last=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.eval_batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
sampler=val_sampler,
drop_last=True)
# if checkpoint is loaded, start by running validation
if args.resume:
assert args.start_epoch > 0
validate(val_loader, r, args.start_epoch - 1)
for epoch in range(args.start_epoch, args.epochs):
if distributed_sampler:
train_sampler.set_epoch(epoch)
# train or run forward pass only for one epoch
if args.forward_only:
validate(val_loader, r, epoch)
else:
train(train_loader, r, optimizer, epoch)
# evaluate on validation set
prec1 = validate(val_loader, r, epoch)
if r.stage != r.num_stages:
prec1 = 0
# remember best prec@1 and save checkpoint
best_prec1 = max(prec1, best_prec1)
should_save_checkpoint = args.checkpoint_dir_not_nfs or r.rank_in_stage == 0
if args.checkpoint_dir and should_save_checkpoint:
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': r.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
}, args.checkpoint_dir, r.stage)
def is_logging_process():
return not dist.is_initialized() or dist.get_rank() == 0
def validate(val_loader, r, epoch):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
n = r.num_iterations(loader_size=len(val_loader))
if args.num_minibatches is not None:
n = min(n, args.num_minibatches)
r.eval(n)
if not is_first_stage():
val_loader = None
r.set_loader(val_loader)
end = time.time()
epoch_start_time = time.time()
if args.no_input_pipelining:
num_warmup_minibatches = 0
else:
num_warmup_minibatches = r.num_warmup_minibatches
if args.verbose_frequency > 0:
logging.info("Letting in %d warm-up minibatches" %
num_warmup_minibatches)
logging.info("Running validation for %d minibatches" % n)
with torch.no_grad():
for i in range(num_warmup_minibatches):
r.run_forward()
for i in range(n - num_warmup_minibatches):
# perform forward pass
r.run_forward()
r.run_ack()
if is_last_stage():
output, target, loss = r.output, r.target, r.loss
# measure accuracy and record loss
prec1, prec5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), output.size(0))
top1.update(prec1[0], output.size(0))
top5.update(prec5[0], output.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
logging.info(
'Test: [{0}][{1}/{2}]\t'
'Time: {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Memory: {memory:.3f}G ({cached_memory:.3f}G)\t'
'Loss: {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1: {top1.val:.2f}% ({top1.avg:.2f}%)\t'
'Prec@5: {top5.val:.2f}% ({top5.avg:.2f}%)'.format(
epoch,
i,
n,
batch_time=batch_time,
loss=losses,
top1=top1,
top5=top5,
memory=(float(torch.cuda.memory_allocated()) /
10**9),
cached_memory=(float(torch.cuda.memory_cached()) /
10**9)))
import sys
sys.stdout.flush()
if is_last_stage():
logging.info(
' * Prec@1 {top1.avg:.2f}% Prec@5 {top5.avg:.2f}%'.format(
top1=top1, top5=top5))
for i in range(num_warmup_minibatches):
r.run_ack()
# wait for all helper threads to complete
r.wait()
logging.info('Epoch %d: %.3f seconds' %
(epoch, time.time() - epoch_start_time))
logging.info("Epoch start time: %.3f, epoch end time: %.3f" %
(epoch_start_time, time.time()))
global writer
if dist.get_rank() == dist.get_world_size() - 1:
writer.add_scalar('Test/Loss', losses.avg, epoch)
writer.add_scalar('Test/Accuracy', top1.avg, epoch)
return top1.avg
def get_rank():
if not is_dist_avail_and_initialized():
return 0
return dist.get_rank()
parser.add_argument('--min-num-tensors', dest='min_num_tensors', action='store',
default=2, type=int,
help='set the inclusive lower limit for the number of ' +
'tensors to be sent during one test run; ' +
'default: 2 (10**2 = 100)')
args = parser.parse_args()
MIN_NUM_TENSORS = args.min_num_tensors
MIN_BYTES = args.min_bytes
MAX_NUM_TENSORS = args.max_num_tensors + 1
MAX_BYTES = args.max_bytes + 1
dist.init_process_group(backend=os.environ['BACKEND'])
rank = dist.get_rank()
dist.barrier()
if rank == 0:
print_header("broadcast")
for bytes in [2**n for n in range(MIN_BYTES, MAX_BYTES)]:
tensor = torch.ByteTensor(bytes).fill_(42)
for num_tensors in [10**n for n in range(MIN_NUM_TENSORS, MAX_NUM_TENSORS)]:
start = timer()
for i in range(0, num_tensors):
dist.broadcast(tensor, 0)
end = timer()
print_stats(bytes, num_tensors, end - start)
print()
else:
for bytes in [2**n for n in range(MIN_BYTES, MAX_BYTES)]:
def __init__(self,
train_data,
model,
optimizer=None,
loss=None,
callbacks_all=None,
callbacks_master=None,
batch_size_per_gpu=8,
n_epochs=1,
num_workers=1,
drop_last=False,
dev_data=None,
metrics=None,
metric_key=None,
update_every=1,
print_every=10,
validate_every=-1,
save_path=None,
device='auto',
fp16='',
use_tqdm=True):
r"""
:param train_data: 训练集， :class:`~fastNLP.DataSet` 类型。
:param nn.modules model: 待训练的模型
:param optimizer: `torch.optim.Optimizer` 优化器。如果为None，则Trainer使用默认的Adam(model.parameters(), lr=4e-3)这个优化器
:param loss: 使用的 :class:`~fastNLP.core.losses.LossBase` 对象。当为None时，默认使用 :class:`~fastNLP.LossInForward`
:param list callbacks_all: 用于在train过程中起调节作用的回调函数，作用于所有训练进程中。
可使用的callback参见 :mod:`callback模块 <fastNLP.core.callback>`
:param list callbacks_master: 用于在train过程中起调节作用的回调函数，只作用于其中一个进程（ Master 进程）。
可使用的callback参见 :mod:`callback模块 <fastNLP.core.callback>`
:param int batch_size_per_gpu: 训练时，每个进程的 batch 大小。
:param int n_epochs: 需要优化迭代多少次。
:param num_workers: int, 有多少个线程来进行数据pad处理。
:param drop_last: 如果最后一个batch没有正好为batch_size这么多数据，就扔掉最后一个batch
:param dev_data: 用于做验证的DataSet， :class:`~fastNLP.DataSet` 类型。
:param metrics: 验证的评估函数。可以只使用一个 :class:`Metric<fastNLP.core.metrics.MetricBase>` ，
也可以使用多个 :class:`Metric<fastNLP.core.metrics.MetricBase>` ，通过列表传入。
如验证时取得了更好的验证结果(如果有多个Metric，以列表中第一个Metric为准)，且save_path不为None，
则保存当前模型。Metric种类详见 :mod:`metrics模块 <fastNLP.core.metrics>` 。仅在传入dev_data时有效。
:param str,None metric_key: :class:`Metric<fastNLP.core.metrics.MetricBase>` 有时会有多个指标，
比如 :class:`~fastNLP.core.metrics.SpanFPreRecMetric` 中包含了'f', 'pre', 'rec'。此时需
要指定以哪个指标为准。另外有些指标是越小效果越好，比如语言模型的困惑度，这种情况下，在key前面增加一个'-'来表
明验证时，值越小越好(比如: "-ppl")。仅在传入dev_data时有效。
:param update_every: int, 多少步更新一次梯度。用于希望累计梯度的场景，比如需要128的batch_size, 但是直接设为128
会导致内存不足，通过设置batch_size=32, update_every=4达到目的。当optimizer为None时，该参数无效。
:param int print_every: 多少次反向传播更新tqdm显示的loss; 如果use_tqdm=False, 则多少次反向传播打印loss。
:param int validate_every: 多少个step在验证集上验证一次; 如果为-1，则每个epoch结束验证一次。仅在传入dev_data时有效。
:param str,None save_path: 将模型保存路径，如果路径不存在，将自动创建文件夹。如果为None，则不保存模型。如果dev_data为None，则保存
最后一次迭代的模型。保存的时候不仅保存了参数，还保存了模型结构。即便使用DataParallel，这里也只保存模型。
:param str device: 指定 device，可以是 gpu，cpu 或 auto
:param str fp16: 指定半精度训练的优化等级，可为 O1，O2 或 O3，若为空字符串则不使用半精度。
:param bool use_tqdm: 是否使用tqdm来显示训练进度; 如果为False，则将loss打印在终端中。
"""
assert device in [
'auto', 'cuda', 'cpu'
], "Please set correct device in [auto', 'cuda', 'cpu']"
if device == 'auto':
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# init distributed
if device == 'cuda':
torch.cuda.set_device(get_local_rank())
self.device = torch.device("cuda", get_local_rank())
else:
self.device = torch.device(device)
init_logger_dist()
self.world_size = dist.get_world_size()
self.rank = dist.get_rank() # unique id for each process
self.train_data = train_data
self.batch_size_per_gpu = int(batch_size_per_gpu)
self.n_epochs = int(n_epochs)
self.num_data_workers = int(num_workers)
self.drop_last = drop_last
self.update_every = int(update_every)
self.print_every = int(print_every)
self.validate_every = int(validate_every)
self.save_path = save_path
self.losser = _prepare_losser(loss)
self.fp16 = fp16
self.local_rank = get_local_rank()
self._forward_func = model.forward
self.callback_manager = DistCallbackManager(
env={"trainer": self},
callbacks_all=callbacks_all,
callbacks_master=callbacks_master)
self.test_manager = DistCallbackManager(env={'trainer': self})
self.metric_key = metric_key
self.use_tqdm = use_tqdm
model.to(self.device)
optimizer = self._get_optimizer(optimizer)
# init fp16, must before DataParallel init
if len(self.fp16):
assert isinstance(
self.fp16, str
), "Please set Apex AMP optimization level selected in ['O0', 'O1', 'O2', 'O3']"
_check_fp16()
assert device == 'cuda', "Amp requires cuda device"
model, optimizer = amp.initialize(model,
optimizer,
opt_level=self.fp16)
# init DataParallel
if parse_version(torch.__version__) >= parse_version('1.1'):
self.ddp_model = DDP(model,
device_ids=[self.local_rank],
output_device=self.local_rank,
find_unused_parameters=True)
else:
self.ddp_model = DDP(model,
device_ids=[self.local_rank],
output_device=self.local_rank)
self.model = self.ddp_model.module
self.optimizer = optimizer
self.sampler = DistributedSampler(self.train_data)
self.data_iterator = self._get_data_iter(self.train_data)
self.batch_size = self.world_size * self.batch_size_per_gpu
self.n_steps = self._get_n_steps()
# for evaluation, only run eval on master proc
if dev_data and metrics:
cb = _TesterCallback(dev_data,
model,
metrics,
batch_size=batch_size_per_gpu,
num_workers=num_workers)
self.test_manager.add_callback([cb], master=True)
# Setup logging
dist.barrier()
self.start_time = datetime.now().strftime('%m_%d_%Y-%H_%M')
if self.save_path:
self.cp_save_path = self.save_path
else:
self.cp_save_path = None
# use INFO in the master, WARN for others
self.logger = logger
self.logger.info("Setup Distributed Trainer")
self.logger.warning(
"Process pid: {}, rank: {}, local rank: {}, device: {}, fp16: {}".
format(os.getpid(), self.rank, self.local_rank, self.device,
self.fp16 if self.fp16 else False))
self.logger.info("Num of processes: {}".format(self.world_size))
self.logger.info("Use device: {}".format(device))
self.logger.info(
"Training with fp16: {}, optimization level: {}".format(
len(self.fp16) > 0, self.fp16 if self.fp16 else None))
def debug(content, who='all'):
if who == 'all' or who == dist.get_rank():
logger.debug(content)
def train(model_config, model, benchmark_config, model_specs, args):
lm_dataloader, _, _ = model_config["data"]
criterion = benchmark_config["criterion"]
vocab_size = model_specs["vocab_size"]
optimizer = model_config["optimizer"]
model.train()
log_number_of_parameters(model)
total_loss = 0.0
word_counter = 0
optimizer = optimizer(model.parameters())
pipe_group = model.group if hasattr(model, "group") else None
if args.ddp_zero:
model = DDP(
model,
device_ids=[torch.cuda.current_device()],
process_group=get_data_parallel_group(),
find_unused_parameters=False,
)
# TODO(anj-s): Avoid sending fake data to all replicas except the first and last one.
if pipe_group and pipe_group.rank() != 0 and pipe_group.rank() != (pipe_group.size() - 1):
lm_dataloader, _, _ = get_synthetic_dataloaders(args, benchmark_config, model_specs)
total_tokens = 0
total_tokens_per_log_interval = 0
bptt = 2
start_time = time.time()
epoch_start_time = 0.0
def get_batch(source):
seq_len = len(source) - 1
data = source[0:seq_len]
target = source[1 : 1 + seq_len]
return data, target
for i, batch in enumerate(lm_dataloader):
if i == 1:
epoch_start_time = time.time()
source, target = get_batch(batch)
if args.max_batch and i > args.max_batch:
break
if i > 0:
total_tokens += source.numel()
optimizer.zero_grad()
try:
if (pipe_group is None or pipe_group.rank() == 0) and not args.ddp_zero:
tmp = source.to(get_device(model, 0))
output = model(tmp)
else:
output = model(source)
except Exception as e:
raise RuntimeError(f"training failed on {torch.distributed.get_rank()}") from e
if pipe_group is None or pipe_group.rank() == pipe_group.size() - 1:
target = target.to(get_device(model, -1))
output = output.to(target.device)
loss = criterion(output.view(-1, vocab_size), target.view(-1))
if args.ddp_zero:
ddp_group = get_data_parallel_group()
torch.distributed.all_reduce(loss, op=torch.distributed.ReduceOp.SUM, group=ddp_group)
loss /= ddp_group.size()
loss.backward()
del target
else:
if args.ddp_zero:
model.module.back_helper(output)
else:
model.back_helper(output)
del output
torch.nn.utils.clip_grad_value_(model.parameters(), model_specs["clip_value"])
optimizer.step()
if pipe_group is None or pipe_group.rank() == pipe_group.size() - 1:
total_loss += loss.item()
log_interval = 1
total_tokens_per_log_interval += source.numel()
if i % log_interval == 0 and i > 0:
cur_loss = total_loss / log_interval
elapsed = time.time() - start_time
if not args.multiprocess or dist.get_rank() == dist.get_world_size() - 1:
print(
"| batch {:5d} | wps {:5.2f} | loss {:5.2f} | ppl {:8.2f}".format(
i, total_tokens_per_log_interval / elapsed, cur_loss, math.exp(cur_loss)
)
)
total_tokens_per_log_interval = 0
total_loss = 0
start_time = time.time()
if epoch_start_time != 0:
wps = total_tokens / (time.time() - epoch_start_time)
else:
raise RuntimeError(
"Unable to benchmark on a single batch. Increase the size " " of the dataset and rerun the benchmark."
)
if not args.multiprocess or dist.get_rank() == dist.get_world_size() - 1:
return wps, loss.item()
else:
return 0.0, 0.0
def run(rank, size):
torch.manual_seed(1234)
test_set, bsz = partition_dataset()
model = load_model(nn.parallel.DistributedDataParallel(Net()),
"sgd_150_0.1_state_Dict_150.pth").float()
num_batches = np.ceil(len(test_set.dataset) / float(bsz))
best_loss = float("inf")
preds, labels = get_all_preds(model, test_set)
#print("Preds Size")
#print(preds.size()) ([7551,15])
#print("Labels Size") ([7551])
#print(labels.size())
pred_lbl_fl = preds.argmax(1).float()
lbl_fl = labels.float()
prediction_list = [torch.zeros_like(pred_lbl_fl) for _ in range(size)]
labels_list = [torch.zeros_like(pred_lbl_fl) for _ in range(size)]
#print(labels)
if dist.get_rank() == 0:
gather(pred_lbl_fl, prediction_list)
gather(lbl_fl, labels_list)
else:
gather(pred_lbl_fl)
gather(lbl_fl)
if dist.get_rank() == 0:
new_preds = torch.tensor([], dtype=torch.float32)
new_labels = torch.tensor([], dtype=torch.float32)
for t1 in prediction_list:
new_preds = torch.cat((new_preds, t1), dim=0)
for t2 in labels_list:
new_labels = torch.cat((new_labels, t2), dim=0)
print("Preds:")
k = new_preds.tolist()
print(k[0:20])
print("Actual:")
j = new_labels.tolist()
print(j[0:20])
accuracry = calculate_accuracy(new_labels, new_preds)
print("Accuracy : ", accuracry)
print("Classification Report")
print(
classification_report(new_labels,
new_preds,
target_names=class_names))
#roc_auc = roc_auc_compute_fn(new_preds, new_labels)
#print("ROC-AUC score :", roc_auc)
cm = get_confusion_matrix(new_labels, new_preds)
print("Confusion Matrix :")
print(cm)
def train():
logger = logging.getLogger()
is_dist = dist.is_initialized()
## dataset
dl = get_data_loader(cfg, mode='train', distributed=is_dist)
## model
net, criteria_pre, criteria_aux = set_model()
## optimizer
optim = set_optimizer(net)
## mixed precision training
scaler = amp.GradScaler()
## ddp training
net = set_model_dist(net)
## meters
time_meter, loss_meter, loss_pre_meter, loss_aux_meters = set_meters()
## lr scheduler
lr_schdr = WarmupPolyLrScheduler(optim, power=0.9,
max_iter=cfg.max_iter, warmup_iter=cfg.warmup_iters,
warmup_ratio=0.1, warmup='exp', last_epoch=-1,)
## train loop
for it, (im, lb) in enumerate(dl):
im = im.cuda()
lb = lb.cuda()
lb = torch.squeeze(lb, 1)
optim.zero_grad()
with amp.autocast(enabled=cfg.use_fp16):
logits, *logits_aux = net(im)
loss_pre = criteria_pre(logits, lb)
loss_aux = [crit(lgt, lb) for crit, lgt in zip(criteria_aux, logits_aux)]
loss = loss_pre + sum(loss_aux)
scaler.scale(loss).backward()
scaler.step(optim)
scaler.update()
torch.cuda.synchronize()
time_meter.update()
loss_meter.update(loss.item())
loss_pre_meter.update(loss_pre.item())
_ = [mter.update(lss.item()) for mter, lss in zip(loss_aux_meters, loss_aux)]
## print training log message
if (it + 1) % 100 == 0:
lr = lr_schdr.get_lr()
lr = sum(lr) / len(lr)
print_log_msg(
it, cfg.max_iter, lr, time_meter, loss_meter,
loss_pre_meter, loss_aux_meters)
lr_schdr.step()
## dump the final model and evaluate the result
save_pth = osp.join(cfg.respth, 'model_final.pth')
logger.info('\nsave models to {}'.format(save_pth))
state = net.module.state_dict()
if dist.get_rank() == 0: torch.save(state, save_pth)
logger.info('\nevaluating the final model')
torch.cuda.empty_cache()
heads, mious = eval_model(cfg, net)
logger.info(tabulate([mious, ], headers=heads, tablefmt='orgtbl'))
return
def save_layers_on_all_rank_zero_workers(ctx, model):
gpus_per_model = ctx["gpus_per_model"]
rank = torch_distrib.get_rank()
if rank in range(gpus_per_model):
seq = list(model.children())[0]
torch.save(seq, f"seq_{rank}.pt")
def __init__(self,
in_channels,
bottleneck_channels,
out_channels,
stride=1,
groups=1,
dilation=1,
norm_func=None,
use_cudnn=False,
explicit_nhwc=False,
spatial_group_size=1,
communicator=None):
super(SpatialBottleneck, self).__init__()
if groups != 1:
raise RuntimeError('Only support groups == 1')
if dilation != 1:
raise RuntimeError('Only support dilation == 1')
if norm_func == None:
norm_func = FrozenBatchNorm2d
else:
raise RuntimeError('Only support frozen BN now.')
if stride != 1 or in_channels != out_channels:
self.downsample = nn.Sequential(
conv1x1(in_channels, out_channels, stride),
norm_func(out_channels),
)
else:
self.downsample = None
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(in_channels, bottleneck_channels, stride)
self.conv2 = conv3x3(bottleneck_channels, bottleneck_channels)
self.conv3 = conv1x1(bottleneck_channels, out_channels)
self.relu = nn.ReLU(inplace=True)
self.stride = stride
self.bn1 = norm_func(bottleneck_channels)
self.bn2 = norm_func(bottleneck_channels)
self.bn3 = norm_func(out_channels)
self.use_cudnn = use_cudnn
# setup conv weights
self.w_conv = [self.conv1.weight, self.conv2.weight, self.conv3.weight]
if self.downsample is not None:
self.w_conv.append(self.downsample[0].weight)
# init weight in nchw format before possible transpose
for w in self.w_conv:
kaiming_uniform_(w, a=1)
# TODO: prevent unsupported case usage
# support cases
# native cudnn
# normal yes no
# channel_last yes yes
# explicit_nhwc no yes
self.explicit_nhwc = explicit_nhwc
if self.explicit_nhwc:
for p in self.parameters():
with torch.no_grad():
p.data = p.data.permute(0, 2, 3, 1).contiguous()
# spatial communicator
self.spatial_group_size = spatial_group_size
if spatial_group_size > 1:
world_size = dist.get_world_size()
num_groups = world_size // spatial_group_size
assert (
num_groups * spatial_group_size == world_size
), "torch.distributed.get_world_size() must be multiple of group_size"
rank = dist.get_rank()
self.local_rank = rank % spatial_group_size
if communicator is None:
for group in range(num_groups):
ranks = list(
range(group * spatial_group_size,
(group + 1) * spatial_group_size))
comm = torch.distributed.new_group(ranks=ranks)
if rank in ranks:
self.communicator = comm
else:
self.communicator = communicator
self.stream1 = torch.cuda.Stream()
self.spatial_args = self.spatial_group_size, self.local_rank, self.communicator, self.stream1
else:
self.spatial_args = 1, 0, None, None
return
def get_rank():
return dist.get_rank()
def set_main_rpc_process(self):
self.main_rpc_process = torch_distrib.get_rank(
group=mpu.get_pipeline_parallel_group()) == 0
def train(args):
is_distributed = len(args.hosts) > 1 and args.backend is not None
logger.debug("Distributed training - {}".format(is_distributed))
use_cuda = args.num_gpus > 0
logger.debug("Number of gpus available - {}".format(args.num_gpus))
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
device = torch.device("cuda" if use_cuda else "cpu")
if is_distributed:
# Initialize the distributed environment.
world_size = len(args.hosts)
os.environ['WORLD_SIZE'] = str(world_size)
host_rank = args.hosts.index(args.current_host)
dist.init_process_group(backend=args.backend, rank=host_rank, world_size=world_size)
logger.info('Initialized the distributed environment: \'{}\' backend on {} nodes. '.format(
args.backend, dist.get_world_size()) + 'Current host rank is {}. Number of gpus: {}'.format(
dist.get_rank(), args.num_gpus))
# set the seed for generating random numbers
torch.manual_seed(args.seed)
if use_cuda:
torch.cuda.manual_seed(args.seed)
train_loader = _get_train_data_loader(args.batch_size, args.data_dir, is_distributed, **kwargs)
test_loader = _get_test_data_loader(args.test_batch_size, args.data_dir, **kwargs)
logger.debug("Processes {}/{} ({:.0f}%) of train data".format(
len(train_loader.sampler), len(train_loader.dataset),
100. * len(train_loader.sampler) / len(train_loader.dataset)
))
logger.debug("Processes {}/{} ({:.0f}%) of test data".format(
len(test_loader.sampler), len(test_loader.dataset),
100. * len(test_loader.sampler) / len(test_loader.dataset)
))
model = Net().to(device)
if is_distributed and use_cuda:
# multi-machine multi-gpu case
model = torch.nn.parallel.DistributedDataParallel(model)
else:
# single-machine multi-gpu case or single-machine or multi-machine cpu case
model = torch.nn.DataParallel(model)
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
for epoch in range(1, args.epochs + 1):
model.train()
for batch_idx, (data, target) in enumerate(train_loader, 1):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
if is_distributed and not use_cuda:
# average gradients manually for multi-machine cpu case only
_average_gradients(model)
optimizer.step()
if batch_idx % args.log_interval == 0:
logger.info('Train Epoch: {} [{}/{} ({:.0f}%)] Loss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.sampler),
100. * batch_idx / len(train_loader), loss.item()))
test(model, test_loader, device)
save_model(model, args.model_dir)
def get_rank():
if not dist.is_available():
return 0
if not dist.is_initialized():
return 0
return dist.get_rank()
def validate(val_loader, model, criterion, epoch, start_time, log_writer):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
global iter_ptr
model.eval()
end = time.time()
prefetcher = data_prefetcher(val_loader)
input, target = prefetcher.next()
i = -1
while input is not None:
i += 1
target = target.cuda(async=True)
input_var = Variable(input)
target_var = Variable(target)
# compute output
with torch.no_grad():
output = model(input_var)
loss = criterion(output, target_var)
reduced_loss = reduce_tensor(loss.data)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
reduced_prec1 = reduce_tensor(prec1)
reduced_prec5 = reduce_tensor(prec5)
losses.update(to_python_float(reduced_loss), input.size(0))
top1.update(to_python_float(prec1), input.size(0))
top5.update(to_python_float(prec5), input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if dist.get_rank() == 0 and i % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
top1=top1, top5=top5))
input, target = prefetcher.next()
time_diff = datetime.now()-start_time
if dist.get_rank() == 0:
print(f'~~{epoch}\t{float(time_diff.total_seconds() / 3600.0)}\t{top5.avg:.3f}\n')
print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'.format(top1=top1, top5=top5))
if log_writer:
log_writer.add_scalar('test_iter/top1', top1.get_avg(), iter_ptr)
log_writer.add_scalar('test_iter/top5', top5.get_avg(), iter_ptr)
log_writer.add_scalar('test_iter/loss', losses.get_avg(), iter_ptr)
log_writer.add_scalar('test_iter/batch_time', batch_time.get_avg(), iter_ptr)
log_writer.add_scalar('test_epoch/top1', top1.get_avg(), epoch)
log_writer.add_scalar('test_epoch/top5', top5.get_avg(), epoch)
log_writer.add_scalar('test_epoch/loss', losses.get_avg(), epoch)
log_writer.add_scalar('test_time/top1', top1.get_avg(), train_record.get_time())
log_writer.add_scalar('test_time/top5', top5.get_avg(), train_record.get_time())
log_writer.add_scalar('test_time/loss', losses.get_avg(), train_record.get_time())
return top1.avg
logger.info(f'E{epoch} V{v} * msIoU {IoUs}')
if overall_acc:
logger.info(f'E{epoch} V{v} * OA {overall_acc:.4%}')
return mIoU
if __name__ == "__main__":
args, config = parse_config()
torch.cuda.set_device(config.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
os.makedirs(args.log_dir, exist_ok=True)
os.environ["JOB_LOAD_DIR"] = os.path.dirname(config.load_path)
logger = setup_logger(output=config.log_dir, distributed_rank=dist.get_rank(), name="s3dis_eval")
if dist.get_rank() == 0:
path = os.path.join(config.log_dir, "config.json")
with open(path, 'w') as f:
json.dump(vars(args), f, indent=2)
json.dump(vars(config), f, indent=2)
os.system('cp %s %s' % (args.cfg, config.log_dir))
logger.info("Full config saved to {}".format(path))
# main function
main(config)
def _train(args):
is_distributed = len(args.hosts) > 1 and args.dist_backend is not None
logger.debug("Distributed training - {}".format(is_distributed))
if is_distributed:
# Initialize the distributed environment.
world_size = len(args.hosts)
os.environ['WORLD_SIZE'] = str(world_size)
host_rank = args.hosts.index(args.current_host)
dist.init_process_group(backend=args.dist_backend, rank=host_rank, world_size=world_size)
logger.info(
'Initialized the distributed environment: \'{}\' backend on {} nodes. '.format(
args.dist_backend,
dist.get_world_size()) + 'Current host rank is {}. Using cuda: {}. Number of gpus: {}'.format(
dist.get_rank(), torch.cuda.is_available(), args.num_gpus))
device = 'cuda' if torch.cuda.is_available() else 'cpu'
logger.info("Device Type: {}".format(device))
logger.info("Loading Cifar10 dataset")
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root=args.data_dir, train=True,
download=False, transform=transform)
train_loader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size,
shuffle=True, num_workers=args.workers)
testset = torchvision.datasets.CIFAR10(root=args.data_dir, train=False,
download=False, transform=transform)
test_loader = torch.utils.data.DataLoader(testset, batch_size=args.batch_size,
shuffle=False, num_workers=args.workers)
logger.info("Model loaded")
model = Net()
if torch.cuda.device_count() > 1:
logger.info("Gpu count: {}".format(torch.cuda.device_count()))
model = nn.DataParallel(model)
model = model.to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
for epoch in range(0, args.epochs):
running_loss = 0.0
for i, data in enumerate(train_loader):
# get the inputs
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
return _save_model(model, args.model_dir)
def test_synchronize_sgd():
torch.manual_seed(42)
dist.init_process_group('mpi')
rank = dist.get_rank()
world_size = dist.get_world_size()
device = torch.device('cpu')
# device = torch.device('cuda') # Uncomment this to run on GPU
# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10
# Create random Tensors to hold input and outputs
x = torch.randn(N, D_in, device=device)
y = torch.randn(N, D_out, device=device)
x = x[rank::world_size]
y = y[rank::world_size]
# Create random Tensors for weights; setting requires_grad=True means that we
# want to compute gradients for these Tensors during the backward pass.
w1 = torch.randn(D_in, H, device=device, requires_grad=True)
w2 = torch.randn(H, D_out, device=device, requires_grad=True)
learning_rate = 1e-6
for t in range(500):
# Forward pass: compute predicted y using operations on Tensors. Since w1 and
# w2 have requires_grad=True, operations involving these Tensors will cause
# PyTorch to build a computational graph, allowing automatic computation of
# gradients. Since we are no longer implementing the backward pass by hand we
# don't need to keep references to intermediate values.
y_pred = x.mm(w1).clamp(min=0).mm(w2)
# Compute and print loss. Loss is a Tensor of shape (), and loss.item()
# is a Python number giving its value.
loss = (y_pred - y).pow(2).sum()
if rank == 0:
print("Iter {} : {:10.3e}".format(t, loss.item()))
# Use autograd to compute the backward pass. This call will compute the
# gradient of loss with respect to all Tensors with requires_grad=True.
# After this call w1.grad and w2.grad will be Tensors holding the gradient
# of the loss with respect to w1 and w2 respectively.
loss.backward()
# Update weights using gradient descent. For this step we just want to mutate
# the values of w1 and w2 in-place; we don't want to build up a computational
# graph for the update steps, so we use the torch.no_grad() context manager
# to prevent PyTorch from building a computational graph for the updates
with torch.no_grad():
w1 -= learning_rate * w1.grad
w2 -= learning_rate * w2.grad
# Manually zero the gradients after running the backward pass
w1.grad.zero_()
w2.grad.zero_()
# Synchronize weights
dist.all_reduce(w1, op=dist.reduce_op.SUM)
dist.all_reduce(w2, op=dist.reduce_op.SUM)
w1 /= world_size
w2 /= world_size
def test_basic_math_ops(self):
ops = [
"torch.add", "torch.sub", "torch.mul", "torch.div", "+", "-", "*",
"/"
]
spec = ChunkShardingSpec(
dim=0,
placements=[
"rank:0/cuda:0",
"rank:1/cuda:1",
"rank:2/cuda:2",
"rank:3/cuda:3",
],
)
sharded_lhs = sharded_tensor.rand(spec, (12, 3))
sharded_rhs = sharded_tensor.rand(spec, (12, 3))
current_rank = dist.get_rank()
global_lhs = torch.empty(
(12, 3), device=current_rank) if current_rank == 0 else None
global_rhs = torch.empty(
(12, 3), device=current_rank) if current_rank == 0 else None
sharded_lhs.gather(dst=0, out=global_lhs)
sharded_rhs.gather(dst=0, out=global_rhs)
for op in ops:
binary_op = gen_binary_op_func(op)
binary_op_ = gen_binary_op_func(op, inplace=True)
# test basic math ops between ShardedTensors
sharded_output = binary_op(sharded_lhs, sharded_rhs)
output = torch.empty(
(12, 3), device=current_rank) if current_rank == 0 else None
sharded_output.gather(dst=0, out=output)
if current_rank == 0:
global_output = binary_op(global_lhs, global_rhs)
self.assertEqual(output, global_output)
# test basic math ops between ShardedTensor and scalar
scalars = [3, 1.8]
for scalar in scalars:
sharded_output_lhs = binary_op(sharded_lhs, scalar)
sharded_output_lhs_ = binary_op_(sharded_lhs, scalar)
self.assertTrue(
torch.allclose(sharded_output_lhs, sharded_output_lhs_))
output_lhs = torch.empty(
(12,
3), device=current_rank) if current_rank == 0 else None
sharded_output_lhs.gather(dst=0, out=output_lhs)
sharded_output_rhs = binary_op(scalar, sharded_lhs)
output_rhs = torch.empty(
(12,
3), device=current_rank) if current_rank == 0 else None
sharded_output_rhs.gather(dst=0, out=output_rhs)
if current_rank == 0:
global_output_lhs = binary_op(global_lhs, scalar)
global_output_rhs = binary_op(scalar, global_lhs)
self.assertEqual(output_lhs, global_output_lhs)
self.assertEqual(output_rhs, global_output_rhs)
def main():
args = create_argparser().parse_args()
dist_util.setup_dist()
logger.configure()
logger.log("creating model and diffusion...")
model, diffusion = create_model_and_diffusion(
**args_to_dict(args,
model_and_diffusion_defaults().keys()))
model.load_state_dict(
dist_util.load_state_dict(args.model_path, map_location="cpu"))
model.to(dist_util.dev())
if args.use_fp16:
model.convert_to_fp16()
model.eval()
logger.log("loading classifier...")
classifier = create_classifier(
**args_to_dict(args,
classifier_defaults().keys()))
classifier.load_state_dict(
dist_util.load_state_dict(args.classifier_path, map_location="cpu"))
classifier.to(dist_util.dev())
if args.classifier_use_fp16:
classifier.convert_to_fp16()
classifier.eval()
def cond_fn(x, t, y=None):
assert y is not None
with th.enable_grad():
x_in = x.detach().requires_grad_(True)
logits = classifier(x_in, t)
log_probs = F.log_softmax(logits, dim=-1)
selected = log_probs[range(len(logits)), y.view(-1)]
return th.autograd.grad(selected.sum(),
x_in)[0] * args.classifier_scale
def model_fn(x, t, y=None):
assert y is not None
return model(x, t, y if args.class_cond else None)
logger.log("sampling...")
all_images = []
all_labels = []
while len(all_images) * args.batch_size < args.num_samples:
model_kwargs = {}
classes = th.randint(low=0,
high=NUM_CLASSES,
size=(args.batch_size, ),
device=dist_util.dev())
model_kwargs["y"] = classes
sample_fn = (diffusion.p_sample_loop
if not args.use_ddim else diffusion.ddim_sample_loop)
sample = sample_fn(
model_fn,
(args.batch_size, 3, args.image_size, args.image_size),
clip_denoised=args.clip_denoised,
model_kwargs=model_kwargs,
cond_fn=cond_fn,
device=dist_util.dev(),
)
sample = ((sample + 1) * 127.5).clamp(0, 255).to(th.uint8)
sample = sample.permute(0, 2, 3, 1)
sample = sample.contiguous()
gathered_samples = [
th.zeros_like(sample) for _ in range(dist.get_world_size())
]
dist.all_gather(gathered_samples,
sample) # gather not supported with NCCL
all_images.extend(
[sample.cpu().numpy() for sample in gathered_samples])
gathered_labels = [
th.zeros_like(classes) for _ in range(dist.get_world_size())
]
dist.all_gather(gathered_labels, classes)
all_labels.extend([labels.cpu().numpy() for labels in gathered_labels])
logger.log(f"created {len(all_images) * args.batch_size} samples")
arr = np.concatenate(all_images, axis=0)
arr = arr[:args.num_samples]
label_arr = np.concatenate(all_labels, axis=0)
label_arr = label_arr[:args.num_samples]
if dist.get_rank() == 0:
shape_str = "x".join([str(x) for x in arr.shape])
out_path = os.path.join(logger.get_dir(), f"samples_{shape_str}.npz")
logger.log(f"saving to {out_path}")
np.savez(out_path, arr, label_arr)
dist.barrier()
logger.log("sampling complete")