# the JSON file to store the S3 test stats
TEST_TIMES_FILE = ".pytorch-test-times.json"
# if a test file takes longer than 5 min, we add it to TARGET_DET_LIST
SLOW_TEST_THRESHOLD = 300
DISTRIBUTED_TESTS_CONFIG = {}
if dist.is_available():
DISTRIBUTED_TESTS_CONFIG["test"] = {"WORLD_SIZE": "1"}
if not TEST_WITH_ROCM and dist.is_mpi_available():
DISTRIBUTED_TESTS_CONFIG["mpi"] = {
"WORLD_SIZE": "3",
"TEST_REPORT_SOURCE_OVERRIDE": "dist-mpi",
}
if dist.is_nccl_available():
DISTRIBUTED_TESTS_CONFIG["nccl"] = {
"WORLD_SIZE": "2" if torch.cuda.device_count() == 2 else "3",
"TEST_REPORT_SOURCE_OVERRIDE": "dist-nccl",
}
if dist.is_gloo_available():
DISTRIBUTED_TESTS_CONFIG["gloo"] = {
"WORLD_SIZE": "2" if torch.cuda.device_count() == 2 else "3",
"TEST_REPORT_SOURCE_OVERRIDE": "dist-gloo",
}
# https://stackoverflow.com/questions/2549939/get-signal-names-from-numbers-in-python
SIGNALS_TO_NAMES_DICT = {
getattr(signal, n): n
for n in dir(signal) if n.startswith("SIG") and "_" not in n
}
def main(opt):
set_logging(RANK)
if RANK in [-1, 0]:
print(colorstr('train: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
check_git_status()
check_requirements(exclude=['thop'])
# Resume
wandb_run = check_wandb_resume(opt)
if opt.resume and not wandb_run: # resume an interrupted run
ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path
assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
with open(Path(ckpt).parent.parent / 'opt.yaml') as f:
opt = argparse.Namespace(**yaml.safe_load(f)) # replace
opt.cfg, opt.weights, opt.resume = '', ckpt, True # reinstate
logger.info('Resuming training from %s' % ckpt)
else:
# opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files
assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test)
opt.name = 'evolve' if opt.evolve else opt.name
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve))
# DDP mode
device = select_device(opt.device, batch_size=opt.batch_size)
if LOCAL_RANK != -1:
from datetime import timedelta
assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
torch.cuda.set_device(LOCAL_RANK)
device = torch.device('cuda', LOCAL_RANK)
dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo", timeout=timedelta(seconds=60))
assert opt.batch_size % WORLD_SIZE == 0, '--batch-size must be multiple of CUDA device count'
assert not opt.image_weights, '--image-weights argument is not compatible with DDP training'
# Train
if not opt.evolve:
train(opt.hyp, opt, device)
if WORLD_SIZE > 1 and RANK == 0:
_ = [print('Destroying process group... ', end=''), dist.destroy_process_group(), print('Done.')]
# Evolve hyperparameters (optional)
else:
# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
'box': (1, 0.02, 0.2), # box loss gain
'cls': (1, 0.2, 4.0), # cls loss gain
'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
'iou_t': (0, 0.1, 0.7), # IoU training threshold
'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
'scale': (1, 0.0, 0.9), # image scale (+/- gain)
'shear': (1, 0.0, 10.0), # image shear (+/- deg)
'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
'mosaic': (1, 0.0, 1.0), # image mixup (probability)
'mixup': (1, 0.0, 1.0)} # image mixup (probability)
with open(opt.hyp) as f:
hyp = yaml.safe_load(f) # load hyps dict
assert LOCAL_RANK == -1, 'DDP mode not implemented for --evolve'
opt.notest, opt.nosave = True, True # only test/save final epoch
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here
if opt.bucket:
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
for _ in range(300): # generations to evolve
if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate
# Select parent(s)
parent = 'single' # parent selection method: 'single' or 'weighted'
x = np.loadtxt('evolve.txt', ndmin=2)
n = min(5, len(x)) # number of previous results to consider
x = x[np.argsort(-fitness(x))][:n] # top n mutations
w = fitness(x) - fitness(x).min() # weights
if parent == 'single' or len(x) == 1:
# x = x[random.randint(0, n - 1)] # random selection
x = x[random.choices(range(n), weights=w)[0]] # weighted selection
elif parent == 'weighted':
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
# Mutate
mp, s = 0.8, 0.2 # mutation probability, sigma
npr = np.random
npr.seed(int(time.time()))
g = np.array([x[0] for x in meta.values()]) # gains 0-1
ng = len(meta)
v = np.ones(ng)
while all(v == 1): # mutate until a change occurs (prevent duplicates)
v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
hyp[k] = float(x[i + 7] * v[i]) # mutate
# Constrain to limits
for k, v in meta.items():
hyp[k] = max(hyp[k], v[1]) # lower limit
hyp[k] = min(hyp[k], v[2]) # upper limit
hyp[k] = round(hyp[k], 5) # significant digits
# Train mutation
results = train(hyp.copy(), opt, device)
# Write mutation results
print_mutation(hyp.copy(), results, yaml_file, opt.bucket)
# Plot results
plot_evolution(yaml_file)
print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'
f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')
def main(opt, callbacks=Callbacks()):
# Checks
if RANK in [-1, 0]:
print_args(FILE.stem, opt)
check_git_status()
check_requirements(exclude=['thop'])
# Resume
if opt.resume and not check_wandb_resume(opt) and not opt.evolve: # resume an interrupted run
ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path
assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
with open(Path(ckpt).parent.parent / 'opt.yaml', errors='ignore') as f:
opt = argparse.Namespace(**yaml.safe_load(f)) # replace
opt.cfg, opt.weights, opt.resume = '', ckpt, True # reinstate
LOGGER.info(f'Resuming training from {ckpt}')
else:
opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \
check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks
assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
if opt.evolve:
opt.project = str(ROOT / 'runs/evolve')
opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
# DDP mode
device = select_device(opt.device, batch_size=opt.batch_size)
if LOCAL_RANK != -1:
assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
assert opt.batch_size % WORLD_SIZE == 0, '--batch-size must be multiple of CUDA device count'
assert not opt.image_weights, '--image-weights argument is not compatible with DDP training'
assert not opt.evolve, '--evolve argument is not compatible with DDP training'
torch.cuda.set_device(LOCAL_RANK)
device = torch.device('cuda', LOCAL_RANK)
dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
# Train
if not opt.evolve:
train(opt.hyp, opt, device, callbacks)
if WORLD_SIZE > 1 and RANK == 0:
LOGGER.info('Destroying process group... ')
dist.destroy_process_group()
# Evolve hyperparameters (optional)
else:
# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
'box': (1, 0.02, 0.2), # box loss gain
'cls': (1, 0.2, 4.0), # cls loss gain
'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
'iou_t': (0, 0.1, 0.7), # IoU training threshold
'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
'scale': (1, 0.0, 0.9), # image scale (+/- gain)
'shear': (1, 0.0, 10.0), # image shear (+/- deg)
'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
'mosaic': (1, 0.0, 1.0), # image mixup (probability)
'mixup': (1, 0.0, 1.0), # image mixup (probability)
'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability)
with open(opt.hyp, errors='ignore') as f:
hyp = yaml.safe_load(f) # load hyps dict
if 'anchors' not in hyp: # anchors commented in hyp.yaml
hyp['anchors'] = 3
opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv'
if opt.bucket:
os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {save_dir}') # download evolve.csv if exists
for _ in range(opt.evolve): # generations to evolve
if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
# Select parent(s)
parent = 'single' # parent selection method: 'single' or 'weighted'
x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1)
n = min(5, len(x)) # number of previous results to consider
x = x[np.argsort(-fitness(x))][:n] # top n mutations
w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0)
if parent == 'single' or len(x) == 1:
# x = x[random.randint(0, n - 1)] # random selection
x = x[random.choices(range(n), weights=w)[0]] # weighted selection
elif parent == 'weighted':
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
# Mutate
mp, s = 0.8, 0.2 # mutation probability, sigma
npr = np.random
npr.seed(int(time.time()))
g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1
ng = len(meta)
v = np.ones(ng)
while all(v == 1): # mutate until a change occurs (prevent duplicates)
v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
hyp[k] = float(x[i + 7] * v[i]) # mutate
# Constrain to limits
for k, v in meta.items():
hyp[k] = max(hyp[k], v[1]) # lower limit
hyp[k] = min(hyp[k], v[2]) # upper limit
hyp[k] = round(hyp[k], 5) # significant digits
# Train mutation
results = train(hyp.copy(), opt, device, callbacks)
# Write mutation results
print_mutation(results, hyp.copy(), save_dir, opt.bucket)
# Plot results
plot_evolve(evolve_csv)
LOGGER.info(f'Hyperparameter evolution finished\n'
f"Results saved to {colorstr('bold', save_dir)}\n"
f'Use best hyperparameters example: $ python train.py --hyp {evolve_yaml}')
from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes
import re
HIP_VERSION = 0.0 if torch.version.hip is None else float(
re.search(r"^\d+\.\d+", torch.version.hip)[0])
# load_tests from common_utils is used to automatically filter tests for
# sharding on sandcastle. This line silences flake warnings
load_tests = load_tests
nGPUs = torch.cuda.device_count()
if not TEST_CUDA:
print('CUDA not available, skipping tests', file=sys.stderr)
TestCase = object # noqa: F811
datatypes = [torch.float]
if (TEST_CUDA and CUDA11OrLater and c10d.is_nccl_available()
and nccl.version() >= (2, 10)) or TEST_WITH_ROCM:
datatypes.append(torch.bfloat16)
class TestNCCL(TestCase):
@sandcastle_skip_if(IS_WINDOWS, "NCCL doesn't support Windows")
def test_unique_id(self, device):
uid = nccl.unique_id()
self.assertIsInstance(uid, bytes)
self.assertGreater(len(uid), 1)
@sandcastle_skip_if(TEST_WITH_ROCM and HIP_VERSION < 3.5,
'Skip NCCL tests for ROCm')
@sandcastle_skip_if(IS_WINDOWS, "NCCL doesn't support Windows")
@sandcastle_skip_if(not TEST_MULTIGPU, "only one GPU detected")
def requires_nccl():
return unittest.skipUnless(
c10d.is_nccl_available(),
"c10d was not compiled with the NCCL backend",
)
def requires_nccl():
return sandcastle_skip_if(
not c10d.is_nccl_available(),
"c10d was not compiled with the NCCL backend",
)
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
if TEST_WITH_DEV_DBG_ASAN:
print(
"Skip dev-asan as torch + multiprocessing spawn have known issues",
file=sys.stderr,
)
sys.exit(0)
# Various mixed precision configs to test under.
default_mp = MixedPrecision()
nccl_supports_bf16 = (CUDA11OrLater and dist.is_nccl_available()
and nccl.version() >= (2, 10))
mp_configs = [default_mp]
if nccl_supports_bf16:
mp_diff_reduce = MixedPrecision(reduce_dtype=torch.bfloat16)
mp_diff_buffer = MixedPrecision(buffer_dtype=torch.bfloat16)
mp_diff_buffer_and_reduce = MixedPrecision(buffer_dtype=torch.bfloat16,
reduce_dtype=torch.float32)
mp_configs.extend([
mp_diff_reduce,
mp_diff_buffer,
mp_diff_buffer_and_reduce,
])
# print("\tIn Model: input size", input.size(),
# "output size", output.size())
return output
if __name__ == "__main__":
# Parameters
input_size = 5
output_size = 2
batch_size = 30
data_size = 100
# check the nccl backend
if not dist.is_nccl_available():
print("Error: nccl backend not available.")
sys.exit(1)
# init group
dist.init_process_group(backend="nccl", init_method="env://")
# get the process rank and the world size
rank = dist.get_rank()
world_size = dist.get_world_size()
# prepare the dataset
dataset = RandomDataset(input_size, data_size)
train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
rand_loader = DataLoader(dataset,
else:
assert "nccl" not in available_backends
if dist.is_gloo_available():
assert "gloo" in available_backends
else:
assert "gloo" not in available_backends
if dist.is_mpi_available():
assert "mpi" in available_backends
else:
assert "mpi" not in available_backends
@pytest.mark.distributed
@pytest.mark.skipif(not dist.is_nccl_available(),
reason="Skip if nccl not available")
@pytest.mark.skipif("WORLD_SIZE" in os.environ,
reason="Skip if launched as multiproc")
def test__native_nccl_but_no_gpu(mock_gpu_is_not_available):
with pytest.raises(
RuntimeError,
match=r"Nccl backend is required but no cuda capable devices"):
_NativeDistModel(backend="nccl")
@pytest.mark.distributed
@pytest.mark.skipif("WORLD_SIZE" in os.environ,
reason="Skip if launched as multiproc")
def test__native_dist_model_create_from_backend_bad_config():
def get_world_size():
if not dist.is_nccl_available():
return 1
if not dist.is_initialized():
return 1
return dist.get_world_size()
def get_rank():
if not dist.is_nccl_available():
return 0
if not dist.is_initialized():
return 0
return dist.get_rank()
def init_distributed(rank=-1,
local_rank=-1,
size=-1,
use_gpu=False,
backend=""):
global myreq
global my_rank
global my_size
global my_local_rank
global my_local_size
global a2a_impl
global alltoall_supported
# guess MPI ranks from env (works for IMPI, OMPI and MVAPICH2)
num_mpi_ranks = env2int([
"PMI_SIZE", "OMPI_COMM_WORLD_SIZE", "MV2_COMM_WORLD_SIZE", "WORLD_SIZE"
])
if backend == "" and num_mpi_ranks > 1:
if torch_ccl and env2int(["CCL_WORKER_COUNT"]) > 0:
backend = "ccl"
elif use_gpu and dist.is_nccl_available():
backend = "nccl"
elif dist.is_mpi_available():
backend = "mpi"
else:
print(
"WARNING: MPI multi-process launch detected but PyTorch MPI backend not available."
)
backend = "gloo"
if backend != "":
# guess Rank and size
if rank == -1:
rank = env2int([
"PMI_RANK", "OMPI_COMM_WORLD_RANK", "MV2_COMM_WORLD_RANK",
"RANK"
], 0)
if size == -1:
size = env2int(
[
"PMI_SIZE",
"OMPI_COMM_WORLD_SIZE",
"MV2_COMM_WORLD_SIZE",
"WORLD_SIZE",
],
1,
)
if not os.environ.get("RANK", None) and rank != -1:
os.environ["RANK"] = str(rank)
if not os.environ.get("WORLD_SIZE", None) and size != -1:
os.environ["WORLD_SIZE"] = str(size)
if not os.environ.get("MASTER_PORT", None):
os.environ["MASTER_PORT"] = "29500"
if not os.environ.get("MASTER_ADDR", None):
local_size = env2int(
[
"MPI_LOCALNRANKS",
"OMPI_COMM_WORLD_LOCAL_SIZE",
"MV2_COMM_WORLD_LOCAL_SIZE",
],
1,
)
if local_size != size and backend != "mpi":
print(
"Warning: Looks like distributed multinode run but MASTER_ADDR env not set, using '127.0.0.1' as default"
)
print(
"If this run hangs, try exporting rank 0's hostname as MASTER_ADDR"
)
os.environ["MASTER_ADDR"] = "127.0.0.1"
if size > 1:
if local_rank == -1:
my_local_rank = env2int(
[
"MPI_LOCALRANKID",
"OMPI_COMM_WORLD_LOCAL_RANK",
"MV2_COMM_WORLD_LOCAL_RANK",
"LOCAL_RANK",
],
0,
)
else:
my_local_rank = local_rank
my_local_size = env2int(
[
"MPI_LOCALNRANKS",
"OMPI_COMM_WORLD_LOCAL_SIZE",
"MV2_COMM_WORLD_LOCAL_SIZE",
],
1,
)
if use_gpu:
if my_local_size > torch.cuda.device_count():
print(
"Not sufficient GPUs available... local_size = %d, ngpus = %d"
% (my_local_size, torch.cuda.device_count()))
sys.exit(1)
torch.cuda.set_device(my_local_rank)
dist.init_process_group(backend, rank=rank, world_size=size)
my_rank = dist.get_rank()
my_size = dist.get_world_size()
if my_rank == 0:
print("Running on %d ranks using %s backend" % (my_size, backend))
if hasattr(dist, "all_to_all_single"):
try:
t = torch.zeros([4])
if use_gpu:
t = t.cuda()
dist.all_to_all_single(t, t)
alltoall_supported = True
except RuntimeError as err:
print("fail to enable all_to_all_single primitive: %s" % err)
if a2a_impl == "alltoall" and alltoall_supported == False:
print(
"Requested DLRM_ALLTOALL_IMPL=%s but backend %s does not support it, use scatter/gather based alltoall"
% (a2a_impl, backend))
a2a_impl = "scatter"
if a2a_impl != "":
print("Using DLRM_ALLTOALL_IMPL=%s" % a2a_impl)
else:
my_rank = 0
my_size = 1
my_local_rank = 0
my_local_size = 1
print_all("world size: %d, current rank: %d, local rank: %d" %
(my_size, my_rank, my_local_rank))
myreq = Request()