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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import deepspeed
from deepspeed import utils
from .utils import *
from .backend import *
from .comm import *
from ..runtime import compiler
from deepspeed.utils.torch import required_torch_version
import os
DS_COMM_ALL_GATHER_OFF = False
DS_COMM_REDUCE_SCATTER_OFF = False
DS_COMM_BROADCAST_OFF = False
DS_COMM_ALL_REDUCE_OFF = False
DS_COMM_REDUCE_OFF = False
def build_shm_op():
builder = get_accelerator().create_op_builder("ShareMemCommBuilder")
if builder is None or not deepspeed.ops.__compatible_ops__[builder.NAME]:
return None
shm_cpp_module = builder.load()
print(f'DeepSpeed {builder.absolute_name()} built successfully')
return shm_cpp_module
def has_coalescing_manager():
has_c10d = hasattr(torch.distributed, 'distributed_c10d')
return has_c10d and hasattr(torch.distributed.distributed_c10d, '_coalescing_manager')
def has_all_reduce_coalesced():
return hasattr(torch.distributed, "all_reduce_coalesced") and required_torch_version(min_version=1.13)
def get_coalescing_manager(group, device, reqs, async_op):
if required_torch_version(min_version=2.0, max_version=2.0):
return torch.distributed.distributed_c10d._coalescing_manager(group, device=device, reqs=reqs)
elif required_torch_version(min_version=2.1):
return torch.distributed.distributed_c10d._coalescing_manager(group, device=device, async_ops=async_op)
else:
return torch.distributed.distributed_c10d._coalescing_manager(group, reqs)
##Utilities to turn comm off
##TODO: move to base comm (wrapper)
def all_gather_comm_off(flag=False):
global DS_COMM_ALL_GATHER_OFF
DS_COMM_ALL_GATHER_OFF = flag
def reduce_scatter_comm_off(flag=False):
global DS_COMM_REDUCE_SCATTER_OFF
DS_COMM_REDUCE_SCATTER_OFF = flag
def broadcast_comm_off(flag=False):
global DS_COMM_BROADCAST_OFF
DS_COMM_BROADCAST_OFF = flag
def all_reduce_comm_off(flag=False):
global DS_COMM_ALL_REDUCE_OFF
DS_COMM_ALL_REDUCE_OFF = flag
def reduce_comm_off(flag=False):
global DS_COMM_REDUCE_OFF
DS_COMM_REDUCE_OFF = flag
#assumption: all_gather and reduce scatter
## are what we care about
def backward_comm_off(flag=False):
all_gather_comm_off(flag)
reduce_scatter_comm_off(flag)
class Noop:
def wait(self):
return None
class TorchBackend(Backend):
"""
A light-weight wrapper class for torch.distributed API.
Only a subset of functions are wrapped. Once the init_process_group
is initialized, standard torch.distributed.* can be used directly
so no need to wrap all the functions. We can keep adding wrappers as
needed.
"""
def __init__(self, backend, timeout, init_method, rank=-1, world_size=-1, name='torch'):
super(TorchBackend, self).__init__()
self.shm_comm_op = build_shm_op()
self.has_all_reduce_coalesced = has_all_reduce_coalesced()
self.has_coalescing_manager = has_coalescing_manager()
self.all_gather_function = self.get_all_gather_function()
self.reduce_scatter_function = self.get_reduce_scatter_function()
self.initialized = True
self.name = name
# Future functionality to support ds.initialize() on a single GPU
# The idea is to fake that dist backend is initialized even when
# it is not so we can run on a single GPU without doing any init_process_group
self.single_gpu_mode = True
self.init_process_group(backend, timeout, init_method, rank, world_size)
if self.shm_comm_op != None:
self.shm_comm_op.initialize(self.get_world_size(), self.get_rank())
@classmethod
@compiler.disable
def get_all_gather_function(self):
if hasattr(torch.distributed, "all_gather_into_tensor"):
return torch.distributed.all_gather_into_tensor
elif hasattr(torch.distributed, "_all_gather_base"):
return torch.distributed._all_gather_base
return None
@classmethod
@compiler.disable
def get_reduce_scatter_function(self):
if hasattr(torch.distributed, "reduce_scatter_tensor"):
return torch.distributed.reduce_scatter_tensor
elif hasattr(torch.distributed, "_reduce_scatter_base"):
return torch.distributed._reduce_scatter_base
return None
def has_all_gather_into_tensor(self):
return self.all_gather_function is not None
def has_reduce_scatter_tensor(self):
return self.reduce_scatter_function is not None
def init_process_group(self, backend, timeout, init_method, rank, world_size):
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend,
timeout=timeout,
init_method=init_method,
rank=rank,
world_size=world_size)
self.using_mpi = torch.distributed.get_backend() == 'mpi'
@compiler.disable
def all_reduce(self, tensor, op=torch.distributed.ReduceOp.SUM, group=None, async_op=False):
op = self._reduce_op(op)
return torch.distributed.all_reduce(tensor=tensor, op=op, group=group, async_op=async_op)
@compiler.disable
def inference_all_reduce(self, tensor, op, group=None):
if self.shm_comm_op == None or self.shm_comm_op.inference_all_reduce(tensor, op) == -1:
op = self._reduce_op(op)
return torch.distributed.all_reduce(tensor=tensor, op=op, group=group, async_op=False)
@compiler.disable
def all_reduce_coalesced(self, tensors, op=torch.distributed.ReduceOp.SUM, group=None, async_op=False):
""" proxy func to torch.distributed.all_reduce_coalesced,
which is included in PyTorch 1.13 and above
"""
if not self.has_all_reduce_coalesced:
raise RuntimeError(f"Current torch version does not have all_reduce_coalesced "
f"api (torch.__version__: {torch.__version__})")
op = self._reduce_op(op)
return torch.distributed.all_reduce_coalesced(tensors=tensors, op=op, group=group, async_op=async_op)
@compiler.disable
def reduce(self, tensor, dst, op=ReduceOp.SUM, group=None, async_op=False):
if DS_COMM_REDUCE_OFF:
if int(os.getenv('RANK', '0')) == 0:
utils.logger.warning("REDUCE is OFF")
return Noop()
return torch.distributed.reduce(tensor=tensor, dst=dst, op=self._reduce_op(op), group=group, async_op=async_op)
@compiler.disable
def reduce_scatter(self, output, input_list, op=ReduceOp.SUM, group=None, async_op=False):
if DS_COMM_REDUCE_SCATTER_OFF:
if int(os.getenv('RANK', '0')) == 0:
utils.logger.warning("REDUCE SCATTER is OFF")
return Noop()
else:
return torch.distributed.reduce_scatter(output=output,
input_list=input_list,
op=self._reduce_op(op),
group=group,
async_op=async_op)
@compiler.disable
def broadcast(self, tensor, src, group=None, async_op=False):
if DS_COMM_BROADCAST_OFF:
if int(os.getenv('RANK', '0')) == 0:
utils.logger.warning("BROADCAST is OFF")
return Noop()
else:
return torch.distributed.broadcast(tensor=tensor, src=src, group=group, async_op=async_op)
@compiler.disable
def all_gather(self, tensor_list, tensor, group=None, async_op=False):
if DS_COMM_ALL_GATHER_OFF:
if int(os.getenv('RANK', '0')) == 0:
utils.logger.warning("All Gather is OFF")
return Noop()
else:
return torch.distributed.all_gather(tensor_list=tensor_list, tensor=tensor, group=group, async_op=async_op)
@compiler.disable
def all_gather_into_tensor(self, output_tensor, input_tensor, group=None, async_op=False):
if self.has_all_gather_into_tensor():
return self.all_gather_function(output_tensor=output_tensor,
input_tensor=input_tensor,
group=group,
async_op=async_op)
@compiler.disable
def all_gather_base(self, output_tensor, input_tensor, group=None, async_op=False):
if DS_COMM_ALL_GATHER_OFF:
if int(os.getenv('RANK', '0')) == 0:
utils.logger.warning("All Gather is OFF")
return Noop()
else:
if self.has_allgather_base:
return torch.distributed.distributed_c10d._all_gather_base(output_tensor=output_tensor,
input_tensor=input_tensor,
group=group,
async_op=async_op)
else:
utils.logger.warning("unable to find torch.distributed._all_gather_base. will fall back to "
"torch.distributed.reduce_scatter which will result in suboptimal performance. "
"please consider upgrading your pytorch installation.")
pass
@compiler.disable
def all_gather_coalesced(self, output_tensors, input_tensors, group=None, async_op=False):
""""""
assert len(output_tensors) == len(input_tensors), ""
if hasattr(torch.distributed.distributed_c10d, '_all_gather_base_coalesced'):
# customized PyTorch
return torch.distributed.distributed_c10d._all_gather_base_coalesced(output_tensors,
input_tensors,
group=group,
async_op=async_op)
elif has_coalescing_manager():
reqs = []
with get_coalescing_manager(group, input_tensors[0].device, reqs, async_op):
for output, input in zip(output_tensors, input_tensors):
handle = torch.distributed.distributed_c10d.all_gather_into_tensor(output,
input,
group=group,
async_op=True)
reqs.append(handle)
if async_op:
return reqs[-1]
else:
reqs[-1].wait()
@compiler.disable
def reduce_scatter_tensor(self, output_tensor, input_tensor, op=ReduceOp.SUM, group=None, async_op=False):
if self.has_reduce_scatter_tensor():
return self.reduce_scatter_function(output_tensor,
input_tensor,
op=self._reduce_op(op),
group=group,
async_op=async_op)
else:
utils.logger.warning("unable to find torch.distributed.reduce_scatter_tensor. will fall back to "
"torch.distributed.reduce_scatter which will result in suboptimal performance. "
"please consider upgrading your pytorch installation.")
pass
@compiler.disable
def all_to_all_single(self,
output,
input,
output_split_sizes=None,
input_split_sizes=None,
group=None,
async_op=False):
return torch.distributed.all_to_all_single(output=output,
input=input,
output_split_sizes=output_split_sizes,
input_split_sizes=input_split_sizes,
group=group,
async_op=async_op)
@compiler.disable
def all_to_all(self, output_tensor_list, input_tensor_list, group=None, async_op=False):
return torch.distributed.all_to_all(output_tensor_list, input_tensor_list, group=group, async_op=async_op)
@compiler.disable
def send(self, tensor, dst, group=None, tag=0):
return torch.distributed.send(tensor=tensor, dst=dst, group=group, tag=tag)
@compiler.disable
def recv(self, tensor, src=None, group=None, tag=0):
return torch.distributed.recv(tensor=tensor, src=src, group=group, tag=tag)
@compiler.disable
def isend(self, tensor, dst, group=None, tag=0):
return torch.distributed.isend(tensor=tensor, dst=dst, group=group, tag=tag)
@compiler.disable
def irecv(self, tensor, src=None, group=None, tag=0):
return torch.distributed.irecv(tensor=tensor, src=src, group=group, tag=tag)
@compiler.disable
def gather(self, tensor, gather_list=None, dst=0, group=None, async_op=False):
return torch.distributed.gather(tensor=tensor,
gather_list=gather_list,
dst=dst,
group=group,
async_op=async_op)
@compiler.disable
def scatter(self, tensor, scatter_list=None, src=0, group=None, async_op=False):
return torch.distributed.scatter(tensor=tensor,
scatter_list=scatter_list,
src=src,
group=group,
async_op=async_op)
@compiler.disable
def barrier(self, group=torch.distributed.GroupMember.WORLD, async_op=False, device_ids=None):
if group is None:
group = torch.distributed.GroupMember.WORLD
return torch.distributed.barrier(group=group, async_op=async_op, device_ids=device_ids)
@compiler.disable
def monitored_barrier(self, group=torch.distributed.GroupMember.WORLD, timeout=None, wait_all_ranks=False):
if group is None:
group = torch.distributed.GroupMember.WORLD
return torch.distributed.monitored_barrier(group=group, timeout=timeout, wait_all_ranks=wait_all_ranks)
def get_rank(self, group=None):
return torch.distributed.get_rank(group=group)
def get_world_size(self, group=None):
return torch.distributed.get_world_size(group=group)
def is_initialized(self):
return torch.distributed.is_initialized()
def get_backend(self, group=None):
return torch.distributed.get_backend(group=group)
def new_group(self, ranks):
return torch.distributed.new_group(ranks)
def get_global_rank(self, group, group_rank):
if hasattr(torch.distributed.distributed_c10d, "get_global_rank"):
from torch.distributed.distributed_c10d import get_global_rank as _get_global_rank
else:
from torch.distributed.distributed_c10d import _get_global_rank
return _get_global_rank(group, group_rank)
def get_world_group(self):
return torch.distributed.group.WORLD
def destroy_process_group(self, group=None):
return torch.distributed.destroy_process_group(group=group)
def _reduce_op(self, op):
'''
Helper function. If the op provided is not a torch.dist.ReduceOp, convert it and return
'''
if not isinstance(op, torch.distributed.ReduceOp):
if op == ReduceOp.SUM:
op = torch.distributed.ReduceOp.SUM
elif op == ReduceOp.PRODUCT:
op = torch.distributed.ReduceOp.PRODUCT
elif op == ReduceOp.AVG:
op = torch.distributed.ReduceOp.AVG
elif op == ReduceOp.MIN:
op = torch.distributed.ReduceOp.MIN
elif op == ReduceOp.MAX:
op = torch.distributed.ReduceOp.MAX
elif op == ReduceOp.BAND:
op = torch.distributed.ReduceOp.BAND
elif op == ReduceOp.BOR:
op = torch.distributed.ReduceOp.BOR
elif op == ReduceOp.BXOR:
op = torch.distributed.ReduceOp.BXOR
return op
# This will become a light-weight wrapper around torch.distributed functions
# TODO: create some example to show how this wrapper can help profile communication
# TODO: make sure there is no performance regression with this approach
# TODO: explore monkey-patching if this does not work
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