128 lines
5.4 KiB
Python
128 lines
5.4 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
|
|
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
from megatron_util import mpu
|
|
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
|
|
from torch.autograd import Variable
|
|
from torch.nn.modules import Module
|
|
from torch.nn.parallel.distributed import DistributedDataParallel as DDP
|
|
|
|
|
|
class PyTorchDistributedDataParallel(DDP):
|
|
|
|
def named_parameters(self, prefix: str = '', recurse: bool = True):
|
|
return self.module.named_parameters(prefix=prefix, recurse=recurse)
|
|
|
|
def state_dict(self, destination=None, prefix='', keep_vars=False):
|
|
sd = self.module.state_dict(destination, prefix, keep_vars)
|
|
return sd
|
|
|
|
def load_state_dict(self, state_dict, strict=True):
|
|
return self.module.load_state_dict(state_dict, strict=strict)
|
|
|
|
|
|
class DistributedDataParallel(Module):
|
|
|
|
def __init__(self, module):
|
|
super(DistributedDataParallel, self).__init__()
|
|
self.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
|
|
|
|
self.module = module
|
|
self.data_parallel_group = mpu.get_data_parallel_group()
|
|
src_rank = mpu.get_model_parallel_rank()
|
|
for p in self.module.parameters():
|
|
if torch.is_tensor(p):
|
|
dist.broadcast(p, src_rank, group=self.data_parallel_group)
|
|
|
|
def allreduce_params(reduce_after=True,
|
|
no_scale=False,
|
|
fp32_allreduce=False):
|
|
if (self.needs_reduction):
|
|
self.needs_reduction = False
|
|
buckets = {}
|
|
for name, param in self.module.named_parameters():
|
|
if param.requires_grad and param.grad is not None:
|
|
tp = (param.data.type())
|
|
if tp not in buckets:
|
|
buckets[tp] = []
|
|
buckets[tp].append(param)
|
|
if self.warn_on_half:
|
|
if torch.cuda.HalfTensor in buckets:
|
|
print(
|
|
'WARNING: gloo dist backend for half parameters may be extremely slow. It is recommended to use the NCCL backend in this case.' # noqa
|
|
)
|
|
self.warn_on_half = False
|
|
for tp in buckets:
|
|
bucket = buckets[tp]
|
|
grads = [param.grad.data for param in bucket]
|
|
coalesced = _flatten_dense_tensors(grads)
|
|
if fp32_allreduce:
|
|
coalesced = coalesced.float()
|
|
if not no_scale and not reduce_after:
|
|
coalesced /= dist.get_world_size(
|
|
group=self.data_parallel_group)
|
|
dist.all_reduce(coalesced, group=self.data_parallel_group)
|
|
torch.cuda.synchronize()
|
|
if not no_scale and reduce_after:
|
|
coalesced /= dist.get_world_size(
|
|
group=self.data_parallel_group)
|
|
for buf, synced in zip(
|
|
grads, _unflatten_dense_tensors(coalesced, grads)):
|
|
buf.copy_(synced)
|
|
|
|
self.hook_handles = []
|
|
self.hooks = []
|
|
for param in list(self.module.parameters()):
|
|
|
|
def allreduce_hook(*unused):
|
|
Variable._execution_engine.queue_callback(allreduce_params)
|
|
|
|
self.allreduce_params = allreduce_params
|
|
|
|
def forward(self, *inputs, **kwargs):
|
|
self.needs_reduction = True
|
|
return self.module(*inputs, **kwargs)
|
|
|
|
def state_dict(self, destination=None, prefix='', keep_vars=False):
|
|
sd = self.module.state_dict(destination, prefix, keep_vars)
|
|
return sd
|
|
|
|
def load_state_dict(self, state_dict, strict=True):
|
|
return self.module.load_state_dict(state_dict, strict=strict)
|
|
|
|
def named_parameters(self, prefix: str = '', recurse: bool = True):
|
|
return self.module.named_parameters(prefix=prefix, recurse=recurse)
|
|
|
|
'''
|
|
def _sync_buffers(self):
|
|
buffers = list(self.module._all_buffers())
|
|
if len(buffers) > 0:
|
|
# cross-node buffer sync
|
|
flat_buffers = _flatten_dense_tensors(buffers)
|
|
dist.broadcast(flat_buffers, 0)
|
|
for buf, synced in zip(buffers, _unflatten_dense_tensors(flat_buffers, buffers)):
|
|
buf.copy_(synced)
|
|
def train(self, mode=True):
|
|
# Clear NCCL communicator and CUDA event cache of the default group ID,
|
|
# These cache will be recreated at the later call. This is currently a
|
|
# work-around for a potential NCCL deadlock.
|
|
if dist._backend == dist.dist_backend.NCCL:
|
|
dist._clear_group_cache()
|
|
super(DistributedDataParallel, self).train(mode)
|
|
self.module.train(mode)
|
|
'''
|