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https://gitcode.com/gh_mirrors/eas/EasyFace.git
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113 lines
4.3 KiB
Python
Executable File
113 lines
4.3 KiB
Python
Executable File
# Copyright 2021-2022 The Alibaba DAMO NLP Team Authors.
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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import torch
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import torch.distributed as dist
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from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
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from torch.autograd import Variable
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from torch.nn.modules import Module
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from megatron_util import mpu
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def normal_init_method(mean, std):
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def init_(tensor):
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return torch.nn.init.normal_(tensor, mean=mean, std=std)
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return init_
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def scaled_init_method(mean, std, num_layers):
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"""Init method based on N(0, sigma/sqrt(2*num_layers)."""
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std = std / math.sqrt(2.0 * num_layers)
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def init_(tensor):
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return torch.nn.init.normal_(tensor, mean=mean, std=std)
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return init_
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class DistributedDataParallel(Module):
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def __init__(self, module):
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super(DistributedDataParallel, self).__init__()
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self.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
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self.module = module
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self.data_parallel_group = mpu.get_data_parallel_group()
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src_rank = mpu.get_tensor_model_parallel_rank()
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for p in self.module.parameters():
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if torch.is_tensor(p):
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dist.broadcast(p, src_rank, group=self.data_parallel_group)
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def allreduce_params(reduce_after=True,
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no_scale=False,
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fp32_allreduce=False):
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if (self.needs_reduction):
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self.needs_reduction = False
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buckets = {}
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for name, param in self.module.named_parameters():
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if param.requires_grad and param.grad is not None:
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tp = (param.data.type())
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if tp not in buckets:
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buckets[tp] = []
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buckets[tp].append(param)
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if self.warn_on_half:
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if torch.cuda.HalfTensor in buckets:
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print(
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'WARNING: gloo dist backend for half parameters may be extremely slow.',
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'It is recommended to use the NCCL backend in this case.'
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)
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self.warn_on_half = False
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for tp in buckets:
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bucket = buckets[tp]
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grads = [param.grad.data for param in bucket]
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coalesced = _flatten_dense_tensors(grads)
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if fp32_allreduce:
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coalesced = coalesced.float()
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if not no_scale and not reduce_after:
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coalesced /= dist.get_world_size(
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group=self.data_parallel_group)
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dist.all_reduce(coalesced, group=self.data_parallel_group)
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torch.cuda.synchronize()
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if not no_scale and reduce_after:
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coalesced /= dist.get_world_size(
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group=self.data_parallel_group)
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for buf, synced in zip(
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grads, _unflatten_dense_tensors(coalesced, grads)):
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buf.copy_(synced)
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self.hook_handles = []
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self.hooks = []
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for param in list(self.module.parameters()):
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def allreduce_hook(*unused):
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Variable._execution_engine.queue_callback(allreduce_params)
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self.allreduce_params = allreduce_params
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def forward(self, *inputs, **kwargs):
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self.needs_reduction = True
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return self.module(*inputs, **kwargs)
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def state_dict(self, destination=None, prefix='', keep_vars=False):
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sd = self.module.state_dict(destination, prefix, keep_vars)
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return sd
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def load_state_dict(self, state_dict, strict=True):
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self.module.load_state_dict(state_dict, strict=strict)
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