WuDao-Model/CogView/model/distributed.py

# coding=utf-8
# 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
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
import torch.distributed as dist
from torch.nn.modules import Module
from torch.autograd import Variable
from torch.nn.parallel.distributed import DistributedDataParallel as DDP

import mpu


class PyTorchDistributedDataParallel(DDP):
    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):
        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.")
                        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)
        #    handle = param.register_hook(allreduce_hook)
            #self.hooks.append(allreduce_hook)
            #self.hook_handles.append(handle)
        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):
        #[h.remove() for h in self.hook_handles]
        sd = self.module.state_dict(destination, prefix, keep_vars)
       # for handle, hook in zip(self.hook_handles, self.hooks):
       #     d = handle.hooks_dict_ref()
       #     d[handle.id] = hook

        return sd

    def load_state_dict(self, state_dict, strict=True):
        self.module.load_state_dict(state_dict, strict=strict)

    '''
    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)
    '''