# flake8: noqa
# @TODO: code formatting issue for 20.07 release
from typing import Union
from collections import OrderedDict
import os
import random
import socket
import subprocess
import deprecation
from packaging.version import parse, Version
import torch
from torch import nn
import torch.distributed
from catalyst import __version__
from catalyst.utils.misc import get_fn_default_params
from catalyst.utils.torch import get_available_gpus
[docs]def check_ddp_wrapped(model: nn.Module) -> bool:
"""
Checks whether model is wrapped with DataParallel/DistributedDataParallel.
"""
parallel_wrappers = nn.DataParallel, nn.parallel.DistributedDataParallel
# Check whether Apex is installed and if it is,
# add Apex's DistributedDataParallel to list of checked types
try:
from apex.parallel import DistributedDataParallel as apex_DDP
parallel_wrappers = parallel_wrappers + (apex_DDP,)
except ImportError:
pass
return isinstance(model, parallel_wrappers)
[docs]def check_apex_available() -> bool:
"""Checks if apex is available."""
env_apex = os.getenv("USE_APEX", "1") == "1"
try:
import apex # noqa: F401
from apex import amp # noqa: F401
return True and env_apex
except ImportError:
return False and env_apex
[docs]def check_amp_available() -> bool:
"""Checks if torch.amp is available."""
return parse(torch.__version__) >= Version("1.6.0")
[docs]def check_torch_distributed_initialized() -> bool:
"""Checks if torch.distributed is available and initialized."""
return (
torch.distributed.is_available() and torch.distributed.is_initialized()
)
[docs]def check_slurm_available():
"""Checks if slurm is available."""
return "SLURM_JOB_NUM_NODES" in os.environ and "SLURM_NODEID" in os.environ
[docs]def assert_fp16_available() -> None:
"""Asserts for installed and available Apex FP16."""
assert (
torch.backends.cudnn.enabled
), "fp16 mode requires cudnn backend to be enabled."
assert check_apex_available(), (
"NVidia Apex package must be installed. "
"See https://github.com/NVIDIA/apex."
)
[docs]def initialize_apex(model, optimizer=None, **distributed_params):
"""@TODO: Docs. Contribution is welcome."""
import apex
amp_params = get_fn_default_params(
apex.amp.initialize, ["models", "optimizers"]
)
amp_params["opt_level"] = "O0"
for dp in distributed_params:
if dp in amp_params:
amp_params[dp] = distributed_params[dp]
# NVIDIA apex support only:
# model: nn.Module or list of modules
# optimizer: None, torch.Optimizer or list of optimizers
# while key-value is preferred in the `catalyst`.
# So if model/optimizer is a dict, convert it to lists of keys
# and values first, and then cast it back after apex initialization
model_keys, optimizer_keys = None, None
if isinstance(model, dict):
model_keys, model = list(model.keys()), list(model.values())
if isinstance(optimizer, dict):
optimizer_keys = list(optimizer.keys())
optimizer = list(optimizer.values())
amp_result = apex.amp.initialize(model, optimizer, **amp_params)
if optimizer is not None:
model, optimizer = amp_result
else:
model = amp_result
# convert model/optimizer back to dict if it needed
if model_keys is not None:
model = OrderedDict([(k, v) for k, v in zip(model_keys, model)])
if optimizer_keys is not None:
optimizers = [(k, v) for k, v in zip(optimizer_keys, optimizer)]
optimizer = OrderedDict(optimizers)
return model, optimizer
[docs]def get_nn_from_ddp_module(model: nn.Module) -> nn.Module:
"""
Return a real model from a torch.nn.DataParallel,
torch.nn.parallel.DistributedDataParallel, or
apex.parallel.DistributedDataParallel.
Args:
model: A model, or DataParallel wrapper.
Returns:
A model
"""
if check_ddp_wrapped(model):
model = model.module
return model
[docs]def get_rank() -> int:
"""
Returns the rank of the current worker.
Returns:
int: ``rank`` if torch.distributed is initialized, otherwise ``-1``
"""
if check_torch_distributed_initialized():
return torch.distributed.get_rank()
else:
return -1
[docs]def get_distributed_mean(value: Union[float, torch.Tensor]):
"""Computes distributed mean among all nodes."""
if check_torch_distributed_initialized():
# Fix for runtime warning:
# To copy construct from a tensor, it is recommended to use
# sourceTensor.clone().detach() or
# sourceTensor.clone().detach().requires_grad_(True),
# rather than torch.tensor(sourceTensor).
if torch.is_tensor(value):
value = (
value.clone()
.detach()
.to(device=f"cuda:{torch.cuda.current_device()}")
)
else:
value = torch.tensor(
value,
dtype=torch.float,
device=f"cuda:{torch.cuda.current_device()}",
requires_grad=False,
)
torch.distributed.all_reduce(value)
value = float(value.item() / torch.distributed.get_world_size())
return value
[docs]def get_slurm_params():
"""@TODO: Docs. Contribution is welcome."""
cmd = "scontrol show hostnames '%s'" % os.environ["SLURM_JOB_NODELIST"]
nodes = subprocess.getoutput(cmd).split()
num_nodes = int(os.environ["SLURM_JOB_NUM_NODES"])
current_node = os.environ["SLURMD_NODENAME"]
master_node = socket.gethostbyname(nodes[0])
cur_node_idx = nodes.index(current_node)
job_id = os.environ["SLURM_JOB_ID"]
master_port = str(5 * 10 ** 4 + int(job_id) % 10 ** 4)
return cur_node_idx, num_nodes, master_node, master_port
[docs]def get_distributed_params():
"""@TODO: Docs. Contribution is welcome."""
master_port = str(random.randint(5 * 10 ** 4, 6 * 10 ** 4))
master_addr = "127.0.0.1"
cur_node, num_nodes = 0, 1
if check_slurm_available():
cur_node, num_nodes, master_addr, master_port = get_slurm_params()
os.environ["MASTER_ADDR"] = os.getenv("MASTER_ADDR", master_addr)
os.environ["MASTER_PORT"] = os.getenv("MASTER_PORT", master_port)
workers_per_node = torch.cuda.device_count()
start_rank = cur_node * workers_per_node
world_size = num_nodes * workers_per_node
local_rank = os.getenv("LOCAL_RANK", None)
rank = os.getenv("RANK", None)
local_rank, rank = [v and int(v) for v in [local_rank, rank]]
world_size = int(os.getenv("WORLD_SIZE", world_size))
output = OrderedDict(
local_rank=local_rank,
start_rank=start_rank,
rank=rank,
world_size=world_size,
master_addr=os.environ["MASTER_ADDR"],
master_port=os.environ["MASTER_PORT"],
)
return output
[docs]def get_distributed_env(
local_rank, rank, world_size, use_cuda_visible_devices=True
):
"""@TODO: Docs. Contribution is welcome."""
env = os.environ.copy()
env["RANK"] = str(rank)
env["WORLD_SIZE"] = str(world_size)
env["LOCAL_RANK"] = str(local_rank)
if use_cuda_visible_devices:
available_gpus = get_available_gpus()
env["LOCAL_RANK"] = "0"
env["CUDA_VISIBLE_DEVICES"] = str(available_gpus[local_rank])
return env
[docs]@deprecation.deprecated(
deprecated_in="20.05",
removed_in="20.06",
current_version=__version__,
details="Use check_ddp_wrapped instead.",
)
def is_wrapped_with_ddp(model: nn.Module) -> bool:
"""
Checks whether model is wrapped with DataParallel/DistributedDataParallel.
"""
return check_ddp_wrapped(model)
[docs]@deprecation.deprecated(
deprecated_in="20.05",
removed_in="20.06",
current_version=__version__,
details="Use check_torch_distributed_initialized instead.",
)
def is_torch_distributed_initialized() -> bool:
"""Checks if torch.distributed is available and initialized."""
return check_torch_distributed_initialized()
[docs]@deprecation.deprecated(
deprecated_in="20.05",
removed_in="20.06",
current_version=__version__,
details="Use check_slurm_available instead.",
)
def is_slurm_available() -> bool:
"""Checks if slurm is available."""
return check_slurm_available()
[docs]@deprecation.deprecated(
deprecated_in="20.05",
removed_in="20.06",
current_version=__version__,
details="Use check_apex_available instead.",
)
def is_apex_available() -> bool:
"""Checks if apex is available."""
return check_apex_available()
__all__ = [
"check_ddp_wrapped",
"check_apex_available",
"check_amp_available",
"check_torch_distributed_initialized",
"check_slurm_available",
"assert_fp16_available",
"initialize_apex",
"get_nn_from_ddp_module",
"get_rank",
"get_distributed_mean",
"get_distributed_env",
"get_distributed_params",
"get_slurm_params",
"is_wrapped_with_ddp",
"is_torch_distributed_initialized",
"is_slurm_available",
"is_apex_available",
]