0x00 摘要

NVIDIA Megatron 是一個基于 PyTorch 的分布式訓(xùn)練框架，用來訓(xùn)練超大Transformer語言模型，其通過綜合應(yīng)用了數(shù)據(jù)并行，Tensor并行和Pipeline并行來復(fù)現(xiàn) GPT3，值得我們深入分析其背后機理。

本系列大概有6～7篇文章，通過論文和源碼和大家一起學(xué)習(xí)研究。本文將對 Megatron 的基本架構(gòu)做一下梳理。

0x01 啟動

1.1 分布式啟動

啟動腳本在 examples/pretrain_bert_distributed.sh，其利用了 torch.distributed.launch 來啟動多個進程。具體業(yè)務(wù)代碼是 pretrain_bert.py。

因為 GPUS_PER_NODE 是8，所以 nproc_per_node 是8，這樣，在本機上就啟動了8個進程，每個進程之中含有模型的一部分。進程的 rank 是被 torch.distributed.launch 調(diào)用 elastic 自動分配的。

#!/bin/bash

GPUS_PER_NODE=8
#Changeformultinodeconfig
MASTER_ADDR=localhost
MASTER_PORT=6000
NNODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))

DATA_PATH=_text_sentence
CHECKPOINT_PATH=

DISTRIBUTED_ARGS="--nproc_per_node$GPUS_PER_NODE--nnodes$NNODES--node_rank$NODE_RANK--master_addr$MASTER_ADDR--master_port$MASTER_PORT"

python-mtorch.distributed.launch$DISTRIBUTED_ARGS
pretrain_bert.py
--num-layers24
--hidden-size1024
--num-attention-heads16
--micro-batch-size4
--global-batch-size32
--seq-length512
--max-position-embeddings512
--train-iters1000000
--save$CHECKPOINT_PATH
--load$CHECKPOINT_PATH
--data-path$DATA_PATH
--vocab-filebert-vocab.txt
--data-implmmap
--split949,50,1
--distributed-backendnccl
--lr0.0001
--lr-decay-stylelinear
--min-lr1.0e-5
--lr-decay-iters990000
--weight-decay1e-2
--clip-grad1.0
--lr-warmup-fraction.01
--log-interval100
--save-interval10000
--eval-interval1000
--eval-iters10
--fp16

1.2 構(gòu)造基礎(chǔ)

pretrain_bert.py 會調(diào)用 pretrain 進行預(yù)訓(xùn)練。

if__name__=="__main__":

pretrain(train_valid_test_datasets_provider,model_provider,
ModelType.encoder_or_decoder,
forward_step,args_defaults={'tokenizer_type':'BertWordPieceLowerCase'})

1.2.1 獲取模型

model_provider返回模型普通版本（vanilla version）。所謂vanilla，我們指的是一個簡單的cpu模型，沒有 fp16或 ddp，但是已經(jīng)被 Megatron 改造為并行的版本。

defmodel_provider(pre_process=True,post_process=True):
"""Buildthemodel."""

print_rank_0('buildingBERTmodel...')

args=get_args()
num_tokentypes=2ifargs.bert_binary_headelse0
model=BertModel(
num_tokentypes=num_tokentypes,
add_binary_head=args.bert_binary_head,
parallel_output=True,
pre_process=pre_process,
post_process=post_process)

returnmodel

1.2.2 獲取數(shù)據(jù)集

train_valid_test_datasets_provider 會接受train/valid/test數(shù)據(jù)集的大小，并返回 “train，valid，test” 數(shù)據(jù)集。

deftrain_valid_test_datasets_provider(train_val_test_num_samples):
"""Buildtrain,valid,andtestdatasets."""
args=get_args()

print_rank_0('>buildingtrain,validation,andtestdatasets'
'forBERT...')
train_ds,valid_ds,test_ds=build_train_valid_test_datasets(
data_prefix=args.data_path,
data_impl=args.data_impl,
splits_string=args.split,
train_valid_test_num_samples=train_val_test_num_samples,
max_seq_length=args.seq_length,
masked_lm_prob=args.mask_prob,
short_seq_prob=args.short_seq_prob,
seed=args.seed,
skip_warmup=(notargs.mmap_warmup),
binary_head=args.bert_binary_head)
print_rank_0(">finishedcreatingBERTdatasets...")

returntrain_ds,valid_ds,test_ds

1.2.3 步進函數(shù)

forward_step函數(shù)接受一個“數(shù)據(jù)迭代器”和“模型”，并返回一個“l(fā)oss”標量，該標量帶有一個字典，其中key:value是希望在訓(xùn)練期間監(jiān)視的信息，例如“l(fā)m loss:value”。還要求此函數(shù)將“batch generator”添加到timers類中。

defforward_step(data_iterator,model):
"""Forwardstep."""
args=get_args()

#Getthebatch.
tokens,types,sentence_order,loss_mask,lm_labels,padding_mask=get_batch(
data_iterator)

ifnotargs.bert_binary_head:
types=None

#Forwardpassthroughthemodel.
output_tensor=model(tokens,padding_mask,tokentype_ids=types,
lm_labels=lm_labels)

returnoutput_tensor,partial(loss_func,loss_mask,sentence_order)

1.2.3.1 廣播數(shù)據(jù)

forward_step 會調(diào)用 get_batch 獲取batch 數(shù)據(jù)，其內(nèi)部會從迭代器獲取數(shù)據(jù)，然后使用broadcast_data函數(shù)把輸入數(shù)據(jù)從 rank 0 廣播到所有tensor-model-parallel 其他 ranks之上。

注意，數(shù)據(jù)并行是把不同數(shù)據(jù)加載到不同的rank之上，而 Tensor模型并行組之中每個rank都加載同樣數(shù)據(jù)。

defget_batch(data_iterator):
"""Buildthebatch."""

#Itemsandtheirtype.
keys=['text','types','labels','is_random','loss_mask','padding_mask']
datatype=torch.int64

#Broadcastdata.
ifdata_iteratorisnotNone:
data=next(data_iterator)#獲取數(shù)據(jù)
else:
data=None
data_b=mpu.broadcast_data(keys,data,datatype)#把數(shù)據(jù)廣播到各個GPU

#Unpack.
tokens=data_b['text'].long()
types=data_b['types'].long()
sentence_order=data_b['is_random'].long()
loss_mask=data_b['loss_mask'].float()
lm_labels=data_b['labels'].long()
padding_mask=data_b['padding_mask'].long()

returntokens,types,sentence_order,loss_mask,lm_labels,padding_mask

broadcast_data 在每個model parallel group之上，把數(shù)據(jù)從rank 0發(fā)送到同組其他成員。

defbroadcast_data(keys,data,datatype):
"""Broadcastdatafromrankzeroofeachmodelparallelgrouptothe
membersofthesamemodelparallelgroup.

Arguments:
keys:listofkeysinthedatadisctionarytobebroadcasted
data:datadictionaryofstringkeysandcputensorvalues.
datatype:torchdatatypeofalltensorsindataassociated
withkeys.
"""
#Build(key,size)and(key,numberofelements)dictionariesalong
#withthetotalnumberofelementsonallranks.
key_size,key_numel,total_numel=_build_key_size_numel_dictionaries(keys,
data)

#Packonrankzero.
ifget_tensor_model_parallel_rank()==0:#rank0才壓縮
#Checkthatallkeyshavethesamedatatype.
_check_data_types(keys,data,datatype)
#Flattenthedataassociatedwiththekeys
flatten_data=torch.cat(
[data[key].contiguous().view(-1)forkeyinkeys],dim=0).cuda()
else:
flatten_data=torch.empty(total_numel,
device=torch.cuda.current_device(),
dtype=datatype)

#Broadcast
torch.distributed.broadcast(flatten_data,get_tensor_model_parallel_src_rank(),
group=get_tensor_model_parallel_group())

#Unpack
output={}
offset=0
forkeyinkeys:
size=key_size[key]
numel=key_numel[key]
output[key]=flatten_data.narrow(0,offset,numel).view(size)
offset+=numel

returnoutput

get_tensor_model_parallel_src_rank 計算與張量模型并行組中第一個local rank對應(yīng)的全局rank。

defget_tensor_model_parallel_src_rank():
"""Calculatetheglobalrankcorrespondingtothefirstlocalrank
inthetensormodelparallelgroup."""
global_rank=torch.distributed.get_rank()
local_world_size=get_tensor_model_parallel_world_size()
return(global_rank//local_world_size)*local_world_size

邏輯圖具體如下，三個不同的函數(shù)分別為預(yù)訓(xùn)練提供不同的功能輸入，做到了解耦。

0x02 Pretrain

BERT訓(xùn)練主要分為兩步：

Pre-train：pre-train是遷移學(xué)習(xí)的基礎(chǔ)，是訓(xùn)練token-level的語義理解。

Fine-tuning：在已經(jīng)訓(xùn)練好的語言模型基礎(chǔ)之上，加入特定領(lǐng)域（比如金融醫(yī)療）的參數(shù)來重新訓(xùn)練，比如對于分類問題就可以在pre-train模型基礎(chǔ)之上加上一個softmax，再使用語料 fine-tune。

Pre-train 主要如下：

初始化Megatron。

使用model_provider設(shè)置模型、優(yōu)化器和lr計劃。

調(diào)用train_val_test_data_provider以獲取train/val/test數(shù)據(jù)集。

使用forward_step_func訓(xùn)練模型。

具體代碼如下：

defpretrain(train_valid_test_dataset_provider,
model_provider,
model_type,
forward_step_func,
extra_args_provider=None,
args_defaults={}):
"""Maintrainingprogram.

Thisfunctionwillrunthefollowingsintheorderprovided:
1)initializeMegatron.
2)setupmodel,optimizerandlrscheduleusingthemodel_provider.
3)calltrain_val_test_data_providertogettrain/val/testdatasets.
4)trainthemodleusingtheforward_step_func.
"""

#Initalizeandgetarguments,timers,andTensorboardwriter.
initialize_megatron(extra_args_provider=extra_args_provider,
args_defaults=args_defaults)

#Adjustthestartuptimesoitreflectsthelargestvalue.
#Thiswillbeclosertowhatschedulerwillsee(outsideof
#image...launches.
global_TRAIN_START_TIME
start_time_tensor=torch.cuda.DoubleTensor([_TRAIN_START_TIME])
torch.distributed.all_reduce(start_time_tensor,
op=torch.distributed.ReduceOp.MIN)
_TRAIN_START_TIME=start_time_tensor.item()

args=get_args()
timers=get_timers()

#Model,optimizer,andlearningrate.使用model_provider設(shè)置模型、優(yōu)化器和lr計劃
model,optimizer,lr_scheduler=setup_model_and_optimizer(model_provider,
model_type)

#Datastuff.調(diào)用train_val_test_data_provider以獲取train/val/測試數(shù)據(jù)集
ifargs.virtual_pipeline_model_parallel_sizeisnotNone:
all_data_iterators=[
build_train_valid_test_data_iterators(train_valid_test_dataset_provider)
for_inrange(len(model))
]
train_data_iterator=[data_iterators[0]fordata_iteratorsinall_data_iterators]
valid_data_iterator=[data_iterators[1]fordata_iteratorsinall_data_iterators]
test_data_iterator=[data_iterators[2]fordata_iteratorsinall_data_iterators]
else:
train_data_iterator,valid_data_iterator,test_data_iterator
=build_train_valid_test_data_iterators(
train_valid_test_dataset_provider)

iteration=0
ifargs.do_trainandargs.train_iters>0:
iteration=train(forward_step_func,#訓(xùn)練模型
model,optimizer,lr_scheduler,
train_data_iterator,valid_data_iterator)

ifargs.do_valid:
prefix='theendoftrainingforvaldata'
evaluate_and_print_results(prefix,forward_step_func,
valid_data_iterator,model,
iteration,False)

ifargs.saveanditeration!=0:
save_checkpoint(iteration,model,optimizer,lr_scheduler)

ifargs.do_test:
#Runontestdata.
prefix='theendoftrainingfortestdata'
evaluate_and_print_results(prefix,forward_step_func,
test_data_iterator,model,
0,True)

對于我們分析來說，initialize_megatron 是重點，這里初始化了 megatron。

0x03 初始化

3.1 initialize_megatron

initialize_megatron 方法會設(shè)置全局變量，初始化分布式環(huán)境等等。

definitialize_megatron(extra_args_provider=None,args_defaults={},
ignore_unknown_args=False,allow_no_cuda=False):
"""Setglobalvariables,initializedistributed,and
setautoresumeandrandomseeds.
`allow_no_cuda`shouldnotbesetunlessusingmegatronforcpuonly
dataprocessing.Ingeneralthisargshouldnotbesetunlessyouknow
whatyouaredoing.
Returnsafunctiontofinalizedistributedenvinitialization
(optionally,onlywhenargs.lazy_mpu_init==True)
"""
ifnotallow_no_cuda:
#Makesurecudaisavailable.
asserttorch.cuda.is_available(),'MegatronrequiresCUDA.'

#Parseargs,buildtokenizer,andsetadlr-autoresume,
#tensorboard-writer,andtimers.
set_global_variables(extra_args_provider=extra_args_provider,#設(shè)置全局變量
args_defaults=args_defaults,
ignore_unknown_args=ignore_unknown_args)

#torch.distributedinitialization
deffinish_mpu_init():
args=get_args()
#Pytorchdistributed.
_initialize_distributed()#設(shè)置分布式

#Randomseedsforreproducibility.
ifargs.rank==0:
print('>settingrandomseedsto{}...'.format(args.seed))
_set_random_seed(args.seed)

#SetpytorchJITlayerfusionoptions.
_set_jit_fusion_options()

args=get_args()
ifargs.lazy_mpu_init:
args.use_cpu_initialization=True
#delayedinitializationofDDP-relatedstuff
#WeonlysetbasicDDPglobals
set_tensor_model_parallel_world_size(args.tensor_model_parallel_size)
#andreturnfunctionforexternalDDPmanager
#tocallwhenithasDDPinitialized
set_tensor_model_parallel_rank(args.rank)
returnfinish_mpu_init
else:
#Megatron'sMPUisthemaster.Completeinitializationrightaway.
finish_mpu_init()

#Autoresume.
_init_autoresume()

#Compiledependencies.
_compile_dependencies()

#Nocontinuationfunction
returnNone

3.2 初始化分布式環(huán)境

_initialize_distributed 代碼位于 megatron/initialize.py，此方法會：

調(diào)用 torch.distributed.init_process_group 初始化分布式環(huán)境。

調(diào)用 mpu.initialize_model_parallel 來設(shè)置模型并行，數(shù)據(jù)并行等各種進程組，我們下文會重點討論。

創(chuàng)建完worker進程之后，程序需要知道哪些進程在訓(xùn)練同一個模型，torch.distributed.init_process_group 就實現(xiàn)了這個功能。torch.distributed.init_process_group 會生成一個進程組，同組內(nèi)進程訓(xùn)練同一個模型，也能確定用什么方式進行通信。進程組會給組內(nèi)每個進程一個序號，就是gloabl rank，如果是多機并行，每個機器創(chuàng)建的進程之間也有一個序號，就是 local rank。如果是單機多卡并行，local rank 和 global rank是一致的。

def_initialize_distributed():
"""Initializetorch.distributedandmpu."""
args=get_args()

device_count=torch.cuda.device_count()
iftorch.distributed.is_initialized():
args.rank=torch.distributed.get_rank()
args.world_size=torch.distributed.get_world_size()
else:
#Manuallysetthedeviceids.
ifdevice_count>0:
device=args.rank%device_count
ifargs.local_rankisnotNone:
assertargs.local_rank==device,
'expectedlocal-ranktobethesameasrank%device-count.'
else:
args.local_rank=device
torch.cuda.set_device(device)
#Calltheinitprocess
torch.distributed.init_process_group(#初始化PyTorch分布式環(huán)境
backend=args.distributed_backend,
world_size=args.world_size,rank=args.rank,
timeout=timedelta(minutes=10))

#Setthetensormodel-parallel,pipelinemodel-parallel,and
#data-parallelcommunicators.
ifdevice_count>0:
ifmpu.model_parallel_is_initialized():
print('modelparallelisalreadyinitialized')
else:
#初始化模型并行，比如設(shè)置各種進程組
mpu.initialize_model_parallel(args.tensor_model_parallel_size,
args.pipeline_model_parallel_size,
args.virtual_pipeline_model_parallel_size,
args.pipeline_model_parallel_split_rank)

3.3 初始化進程組全局變量

因為調(diào)用了 mpu.initialize_model_parallel 來設(shè)置模型并行，數(shù)據(jù)并行等各種進程組，所以我們假定目前進程組都已經(jīng)設(shè)置成功，所以每個 rank 對應(yīng)的進程都有自己的全局變量。假定目前有16個GPU，屬于兩個node，rank 0 ～7 屬于第一個節(jié)點，rank 8 ～ 15 屬于第二個節(jié)點。下面的 gi 指的是第 i 個 GPU。

_TENSOR_MODEL_PARALLEL_GROUP ：當(dāng)前 rank 所屬于的Intra-layer model parallel group，就是tensor 并行進程組。

假如每一層分為兩個tensor，則 _TENSOR_MODEL_PARALLEL_GROUP 例子為：[g0, g1], [g2, g3], [g4, g5], [g6, g7], [g8, g9], [g10, g11], [g12, g13], [g14, g15]。

_PIPELINE_MODEL_PARALLEL_GROUP ：當(dāng)前 rank 所屬于的Intra-layer model parallel group，就是流水線進程組。

假如流水線深度為4，則例子為 [g0, g4, g8, g12], [g1, g5, g9, g13], [g2, g6, g10, g14], [g3, g7, g11, g15]。

_MODEL_PARALLEL_GROUP ：當(dāng)前 rank 所屬于的模型并行進程組，包括了以上兩組。

針對我們例子，就是完整模型被復(fù)制了兩份，兩份分別對應(yīng)的 GPU 具體是[0, 1, 4, 5, 8, 9, 12, 13]，[2, 3, 6, 7, 10, 11, 14, 15]

_EMBEDDING_GROUP ：嵌入對應(yīng)的進程組。

_DATA_PARALLEL_GROUP ：當(dāng)前 rank 所屬于的Data parallel group。

假如數(shù)據(jù)并行度數(shù)為2，則例子為[g0, g2], [g1, g3], [g4, g6], [g5, g7], [g8, g10], [g9, g11], [g12, g14], [g13, g15]。

#Intra-layermodelparallelgroupthatthecurrentrankbelongsto.
_TENSOR_MODEL_PARALLEL_GROUP=None
#Inter-layermodelparallelgroupthatthecurrentrankbelongsto.
_PIPELINE_MODEL_PARALLEL_GROUP=None
#Modelparallelgroup(bothintra-andpipeline)thatthecurrentrankbelongsto.
_MODEL_PARALLEL_GROUP=None
#Embeddinggroup.
_EMBEDDING_GROUP=None
#Dataparallelgroupthatthecurrentrankbelongsto.
_DATA_PARALLEL_GROUP=None

0x04 設(shè)置模型

在 Pretrain 之中，會調(diào)用如下來設(shè)置模型，優(yōu)化器等等。

#Model,optimizer,andlearningrate.使用model_provider設(shè)置模型、優(yōu)化器和lr計劃
model,optimizer,lr_scheduler=setup_model_and_optimizer(model_provider,
model_type)

4.1 setup_model_and_optimizer

setup_model_and_optimizer 方法會設(shè)置模型和優(yōu)化器，其中重點是get_model。

defsetup_model_and_optimizer(model_provider_func,model_type):
"""Setupmodelandoptimizer."""
args=get_args()
model=get_model(model_provider_func,model_type)
unwrapped_model=unwrap_model(model,
(torchDDP,LocalDDP,Float16Module))
optimizer=get_megatron_optimizer(unwrapped_model)
lr_scheduler=get_learning_rate_scheduler(optimizer)

ifargs.loadisnotNone:
timers=get_timers()
#Extrabarrierisaddedtomakesureallranksreportthe
#maxtime.
torch.distributed.barrier()
args.iteration=load_checkpoint(model,optimizer,lr_scheduler)
torch.distributed.barrier()
else:
args.iteration=0

#WeonlysupportlocalDDPwithmultiplemicro-batches.
iflen(model)>1ormpu.get_pipeline_model_parallel_world_size()>1:
assertargs.DDP_impl=='local'

#getmodelwithoutFP16and/orTorchDDPwrappers
ifargs.iteration==0andlen(unwrapped_model)==1
andhasattr(unwrapped_model[0],'init_state_dict_from_bert'):
unwrapped_model[0].init_state_dict_from_bert()
ifargs.fp16:
optimizer.reload_model_params()

returnmodel,optimizer,lr_scheduler

4.2 模型

4.2.1 BertModel

我們首先看看 BertModel 的初始化函數(shù)，略過其他功能函數(shù)。其主要調(diào)用了 get_language_model。

classBertModel(MegatronModule):
"""BertLanguagemodel."""

def__init__(self,
num_tokentypes=2,
add_binary_head=True,
parallel_output=True,
pre_process=True,
post_process=True):
super(BertModel,self).__init__()
args=get_args()

self.fp16_lm_cross_entropy=args.fp16_lm_cross_entropy
self.add_binary_head=add_binary_head
self.parallel_output=parallel_output
self.pre_process=pre_process
self.post_process=post_process

init_method=init_method_normal(args.init_method_std)
scaled_init_method=scaled_init_method_normal(args.init_method_std,
args.num_layers)

#獲取語言模型
self.language_model,self._language_model_key=get_language_model(
num_tokentypes=num_tokentypes,
add_pooler=self.add_binary_head,
encoder_attn_mask_type=AttnMaskType.padding,
init_method=init_method,
scaled_init_method=scaled_init_method,
pre_process=self.pre_process,
post_process=self.post_process)

self.initialize_word_embeddings(init_method_normal)
ifself.post_process:#如果是最后一層，會特殊處理
self.lm_head=BertLMHead(
self.word_embeddings_weight().size(0),
args.hidden_size,init_method,args.layernorm_epsilon,parallel_output)
self._lm_head_key='lm_head'
self.binary_head=None
ifself.add_binary_head:
self.binary_head=get_linear_layer(args.hidden_size,2,
init_method)
self._binary_head_key='binary_head'

4.2.2 語言模型

get_language_model 會獲取一個 TransformerLanguageModel。

defget_language_model(num_tokentypes,add_pooler,
encoder_attn_mask_type,init_method=None,
scaled_init_method=None,add_encoder=True,
add_decoder=False,
decoder_attn_mask_type=AttnMaskType.causal,
pre_process=True,post_process=True):
"""Buildlanguagemodelandreturnalongwiththekeytosave."""
args=get_args()

ifinit_methodisNone:
init_method=init_method_normal(args.init_method_std)

ifscaled_init_methodisNone:
scaled_init_method=scaled_init_method_normal(args.init_method_std,
args.num_layers)

#Languagemodel.
language_model=TransformerLanguageModel(
init_method,
scaled_init_method,
encoder_attn_mask_type,
num_tokentypes=num_tokentypes,
add_encoder=add_encoder,
add_decoder=add_decoder,
decoder_attn_mask_type=decoder_attn_mask_type,
add_pooler=add_pooler,
pre_process=pre_process,
post_process=post_process
)
#keyusedforcheckpoints.
language_model_key='language_model'

returnlanguage_model,language_model_key

TransformerLanguageModel 就是具體的語言模型，其中重要的是 ParallelTransformer。這里會依據(jù)傳入的配置來進行生成。

如果是第一層，即有 pre_process，則會加入 embedding layer。

如果是中間層，則會根據(jù) encoder 還是 decoder 來生成對應(yīng)的 ParallelTransformer。

如果是最后一層，即有 post_process，則會加入 Pooler，在外層 BertModel 也會有對應(yīng)處理。

classTransformerLanguageModel(MegatronModule):
"""Transformerlanguagemodel.

Arguments:
transformer_hparams:transformerhyperparameters
vocab_size:vocabularysize
max_sequence_length:maximumsizeofsequence.This
isusedforpositionalembedding
embedding_dropout_prob:dropoutprobabilityforembeddings
num_tokentypes:sizeofthetoken-typeembeddings.0value
willignorethisembedding
"""

def__init__(self,
init_method,
output_layer_init_method,
encoder_attn_mask_type,
num_tokentypes=0,
add_encoder=True,
add_decoder=False,
decoder_attn_mask_type=AttnMaskType.causal,
add_pooler=False,
pre_process=True,
post_process=True):
super(TransformerLanguageModel,self).__init__()
args=get_args()

self.pre_process=pre_process
self.post_process=post_process
self.hidden_size=args.hidden_size
self.num_tokentypes=num_tokentypes
self.init_method=init_method
self.add_encoder=add_encoder
self.encoder_attn_mask_type=encoder_attn_mask_type
self.add_decoder=add_decoder
self.decoder_attn_mask_type=decoder_attn_mask_type
self.add_pooler=add_pooler
self.encoder_hidden_state=None

#Embeddings.
ifself.pre_process:
self.embedding=Embedding(self.hidden_size,
args.padded_vocab_size,
args.max_position_embeddings,
args.hidden_dropout,
self.init_method,
self.num_tokentypes)
self._embedding_key='embedding'

#Transformer.
#Encoder(usuallysettoTrue,Falseifpartofanencoder-decoder
#architectureandinencoder-onlystage).
ifself.add_encoder:
self.encoder=ParallelTransformer(
self.init_method,
output_layer_init_method,
self_attn_mask_type=self.encoder_attn_mask_type,
pre_process=self.pre_process,
post_process=self.post_process
)
self._encoder_key='encoder'
else:
self.encoder=None

#Decoder(usuallysettoFalse,Trueifpartofanencoder-decoder
#architectureandindecoder-onlystage).
ifself.add_decoder:
#Temporaryassertionuntilweverifycorrectnessofpipelineparallelism
#implementationofT5.
self.decoder=ParallelTransformer(
self.init_method,
output_layer_init_method,
layer_type=LayerType.decoder,
self_attn_mask_type=self.decoder_attn_mask_type,
pre_process=self.pre_process,
post_process=self.post_process)
self._decoder_key='decoder'
else:
self.decoder=None

ifself.post_process:
#Pooler.
ifself.add_pooler:
self.pooler=Pooler(self.hidden_size,self.init_method)
self._pooler_key='pooler'

4.2.3 ParallelTransformer

這里會調(diào)用 ParallelTransformerLayer 生成具體的 Transformer層，我們會在后文中進行分析。

即，ParallelTransformer 包括多個 Transformer，其中每層 Transformer 是一個 ParallelTransformerLayer。

classParallelTransformer(MegatronModule):
"""Transformerclass."""

def__init__(self,init_method,output_layer_init_method,
layer_type=LayerType.encoder,
self_attn_mask_type=AttnMaskType.padding,
pre_process=True,post_process=True):
super(ParallelTransformer,self).__init__()
args=get_args()

self.bf16=args.bf16
self.fp32_residual_connection=args.fp32_residual_connection
self.pre_process=pre_process
self.post_process=post_process
self.input_tensor=None

#Storeactivationcheckpoitingflag.
self.activations_checkpoint_method=args.activations_checkpoint_method
self.activations_checkpoint_num_layers=args.activations_checkpoint_num_layers
self.distribute_checkpointed_activations=args.distribute_checkpointed_activations

#Numberoflayers.
self.num_layers=mpu.get_num_layers(#獲得本Transformer的具體層數(shù)
args,args.model_type==ModelType.encoder_and_decoder)

#Transformerlayers.
defbuild_layer(layer_number):
returnParallelTransformerLayer(#返回一層Transformmer
init_method,
output_layer_init_method,
layer_number,
layer_type=layer_type,
self_attn_mask_type=self_attn_mask_type)
ifargs.virtual_pipeline_model_parallel_sizeisnotNone:
#Numberoflayersineachmodelchunkisthenumberoflayersinthestage,
#dividedbythenumberofmodelchunksinastage.
self.num_layers=self.num_layers//args.virtual_pipeline_model_parallel_size
#With8layers,2stages,and4modelchunks,wewantanassignmentof
#layerstostageslike(eachlistisamodelchunk):
#Stage0:[0][2][4][6]
#Stage1:[1][3][5][7]
#With8layers,2stages,and2virtualstages,wewantanassignmentof
#layerstostageslike(eachlistisamodelchunk):
#Stage0:[0,1][4,5]
#Stage1:[2,3][6,7]
offset=mpu.get_virtual_pipeline_model_parallel_rank()*(
args.num_layers//args.virtual_pipeline_model_parallel_size)+
(mpu.get_pipeline_model_parallel_rank()*self.num_layers)
else:
#Eachstagegetsacontiguoussetoflayers.
offset=mpu.get_pipeline_model_parallel_rank()*self.num_layers

self.layers=torch.nn.ModuleList(#生成num_layers個Transformer
[build_layer(i+1+offset)foriinrange(self.num_layers)])

ifself.post_process:
#Finallayernormbeforeoutput.
self.final_layernorm=LayerNorm(
args.hidden_size,
eps=args.layernorm_epsilon,
no_persist_layer_norm=args.no_persist_layer_norm)

目前邏輯如下，我們假定有兩個 transformer：

4.2.3.1 獲取層數(shù)

這里一個重點就是獲取層數(shù)，即獲取本模型在并行處理狀況下，應(yīng)該擁有多少層。如果模型一共64層，流水線深度為16，則并行每個階段有4層，則本子模型擁有4層。

defget_num_layers(args,is_encoder_and_decoder_model):
"""Computethenumberoftransformerlayersresidentonthecurrentrank."""
ifget_pipeline_model_parallel_world_size()>1:
ifis_encoder_and_decoder_model:
assertargs.pipeline_model_parallel_split_rankisnotNone
num_ranks_in_encoder=args.pipeline_model_parallel_split_rank
num_ranks_in_decoder=get_pipeline_model_parallel_world_size()-num_ranks_in_encoder
ifis_pipeline_stage_before_split():
num_layers=args.num_layers//num_ranks_in_encoder
else:
num_layers=args.num_layers//num_ranks_in_decoder
else:
num_layers=args.num_layers//get_pipeline_model_parallel_world_size()
else:
num_layers=args.num_layers
returnnum_layers

get_pipeline_model_parallel_world_size 獲取本流水線組world size數(shù)目，就是流水線深度。

defget_pipeline_model_parallel_world_size():
"""Returnworldsizeforthepipelinemodelparallelgroup."""
global_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
if_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZEisnotNone:
return_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
returntorch.distributed.get_world_size(group=get_pipeline_model_parallel_group())

_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE 的意思是流水線深度 p，就是縱向切 p-1刀。比如一共 12 層，縱向切 5 刀，則有 6 個stage，每個 stage 有 2 層。

4.2.3.2 前向傳播

我們接著看看其前向傳播函數(shù)，這里主要就是調(diào)用內(nèi)部 ParallelTransformerLayer 的 forward 方法，如果是第一層或者最后一層，則做特殊處理。

defforward(self,hidden_states,attention_mask,
encoder_output=None,enc_dec_attn_mask=None,
inference_params=None):

ifself.pre_process:
#Dataformatchangetoavoidexplicittranposes:[bsh]-->[sbh].
#Iftheinputflagforfp32residualconnectionisset,convertforfloat.
ifself.fp32_residual_connection:
hidden_states=hidden_states.transpose(0,1).contiguous().float()
#Otherwise,leaveitasis.
else:
hidden_states=hidden_states.transpose(0,1).contiguous()
else:
#Seeset_input_tensor()
hidden_states=self.input_tensor

ifencoder_outputisnotNone:
encoder_output=encoder_output.transpose(0,1).contiguous()

ifself.activations_checkpoint_methodisnotNone:
hidden_states=self._checkpointed_forward(hidden_states,
attention_mask,
encoder_output,
enc_dec_attn_mask)
else:
forindexinrange(self.num_layers):
layer=self._get_layer(index)
hidden_states=layer(#調(diào)用ParallelTransformerLayer的forward函數(shù)
hidden_states,
attention_mask,
encoder_output=encoder_output,
enc_dec_attn_mask=enc_dec_attn_mask,
inference_params=inference_params)


#Finallayernorm.
ifself.post_process:
#Revertingdataformatchange[sbh]-->[bsh].
hidden_states=hidden_states.transpose(0,1).contiguous()
output=self.final_layernorm(hidden_states)
else:
output=hidden_states

returnoutput

4.3 get_model

現(xiàn)在讓我們回到 get_model，把生成模型的流程整理出來。

BERT之中含有多個transformer，所以直接按照層數(shù)切分，每一層是一模一樣的transformer layer。前面提到了，在我們樣例之中啟動了8個進程，每個進程里面有一個子模型，即原始BERT模型的部分層。但是怎么知道每個子模型包含了多少層？答案是：因為已經(jīng)建立了各種進程組，所以 get_model 方法會依據(jù)目前進程組情況進行處理。單個進程內(nèi)模型獲取如下：

如果是有 virtual 設(shè)置，則會遍歷 virtual size，生成對應(yīng)數(shù)目的模型（BertModel）。

否則如果是 encoder_and_decoder，則針對split進行配置。

設(shè)置 tensor model parallel 屬性。

把本模型放置到GPU之上。

如果需要數(shù)據(jù)并行，則配置DDP。

具體代碼如下：

defget_model(model_provider_func,model_type=ModelType.encoder_or_decoder,wrap_with_ddp=True):
"""Buildthemodel."""
args=get_args()
args.model_type=model_type

#Buildmodel.
ifmpu.get_pipeline_model_parallel_world_size()>1and
args.virtual_pipeline_model_parallel_sizeisnotNone:#有virtual設(shè)置，后續(xù)會提到
model=[]
foriinrange(args.virtual_pipeline_model_parallel_size):#遍歷virtual
#設(shè)置rank，主要是為了看是不是第一層，最后一層
mpu.set_virtual_pipeline_model_parallel_rank(i)
#Setpre_processandpost_processonlyaftervirtualrankisset.
pre_process=mpu.is_pipeline_first_stage()
post_process=mpu.is_pipeline_last_stage()
this_model=model_provider_func(#獲取原始模型BertModel
pre_process=pre_process,
post_process=post_process
)
this_model.model_type=model_type
model.append(this_model)#模型列表之中添加一個新的BertModel
else:
pre_process=mpu.is_pipeline_first_stage()#是不是第一層
post_process=mpu.is_pipeline_last_stage()#是不是最后一層
add_encoder=True
add_decoder=True
ifmodel_type==ModelType.encoder_and_decoder:
ifmpu.get_pipeline_model_parallel_world_size()>1:
rank=mpu.get_pipeline_model_parallel_rank()
split_rank=args.pipeline_model_parallel_split_rank
world_size=mpu.get_pipeline_model_parallel_world_size()
pre_process=rank==0orrank==split_rank#是不是第一層
post_process=(rank==(split_rank-1))or(#是不是最后一層
rank==(world_size-1))
add_encoder=mpu.is_pipeline_stage_before_split()
add_decoder=mpu.is_pipeline_stage_after_split()
model=model_provider_func(#獲取原始模型
pre_process=pre_process,
post_process=post_process,
add_encoder=add_encoder,
add_decoder=add_decoder)
else:
model=model_provider_func(#獲取原始模型
pre_process=pre_process,
post_process=post_process
)
model.model_type=model_type

ifnotisinstance(model,list):
model=[model]

#Settensormodelparallelattributesifnotset.
#Onlyparametersthatarealreadytensormodelparallelhavethese
#attributessetforthem.Weshouldmakesurethedefaultattributes
#aresetforallparamssotheoptimizercanusethem.
formodel_moduleinmodel:
forparaminmodel_module.parameters():
mpu.set_defaults_if_not_set_tensor_model_parallel_attributes(param)

#GPUallocation.
formodel_moduleinmodel:#把本模型放置到GPU之上
model_module.cuda(torch.cuda.current_device())

#Fp16conversion.
ifargs.fp16orargs.bf16:
model=[Float16Module(model_module,args)formodel_moduleinmodel]

ifwrap_with_ddp:#如果需要數(shù)據(jù)并行，則配置DDP
ifargs.DDP_impl=='torch':
i=torch.cuda.current_device()
model=[torchDDP(model_module,device_ids=[i],output_device=i,
process_group=mpu.get_data_parallel_group())
formodel_moduleinmodel]

elifargs.DDP_impl=='local':
model=[LocalDDP(model_module,
args.accumulate_allreduce_grads_in_fp32,
args.use_contiguous_buffers_in_local_ddp)
formodel_moduleinmodel]

else:
raiseNotImplementedError('UnknownDDPimplementationspecified:'
'{}.Exiting.'.format(args.DDP_impl))

returnmodel

單個進程內(nèi)的邏輯大致如下，這里 torchDDP 的意思是把 BertModel 之中的 module 用 torchDDP 來封裝。

0x05 數(shù)據(jù)并行

5.1 設(shè)置數(shù)據(jù)

build_train_valid_test_data_iterators 方法會對數(shù)據(jù)進行處理，提供了 train，valid，test 三種不同的數(shù)據(jù)集。

defbuild_train_valid_test_data_iterators(
build_train_valid_test_datasets_provider):
"""XXX"""
args=get_args()
(train_dataloader,valid_dataloader,test_dataloader)=(None,None,None)


#Backwardcompatibility,assumefixedbatchsize.
ifargs.iteration>0andargs.consumed_train_samples==0:
args.consumed_train_samples=args.iteration*args.global_batch_size
ifargs.iteration>0andargs.consumed_valid_samples==0:
ifargs.train_samplesisNone:
args.consumed_valid_samples=(args.iteration//args.eval_interval)*
args.eval_iters*args.global_batch_size

#Dataloaderonlyonrank0ofeachmodelparallelgroup.
ifmpu.get_tensor_model_parallel_rank()==0:

#Numberoftrain/valid/testsamples.
ifargs.train_samples:
train_samples=args.train_samples
else:
train_samples=args.train_iters*args.global_batch_size
eval_iters=(args.train_iters//args.eval_interval+1)*
args.eval_iters
test_iters=args.eval_iters
train_val_test_num_samples=[train_samples,
eval_iters*args.global_batch_size,
test_iters*args.global_batch_size]

#Buildthedatasets.
train_ds,valid_ds,test_ds=build_train_valid_test_datasets_provider(
train_val_test_num_samples)

#Builddataloders.
train_dataloader=build_pretraining_data_loader(
train_ds,args.consumed_train_samples)
valid_dataloader=build_pretraining_data_loader(
valid_ds,args.consumed_valid_samples)
test_dataloader=build_pretraining_data_loader(test_ds,0)

#Flagstoknowifweneedtodotraining/validation/testing.
do_train=train_dataloaderisnotNoneandargs.train_iters>0
do_valid=valid_dataloaderisnotNoneandargs.eval_iters>0
do_test=test_dataloaderisnotNoneandargs.eval_iters>0
#Needtobroadcastnum_tokensandnum_type_tokens.
flags=torch.cuda.LongTensor(
[int(do_train),int(do_valid),int(do_test)])
else:
flags=torch.cuda.LongTensor([0,0,0])

#Broadcastnumtokens.
torch.distributed.broadcast(flags,
mpu.get_tensor_model_parallel_src_rank(),
group=mpu.get_tensor_model_parallel_group())
args.do_train=flags[0].item()
args.do_valid=flags[1].item()
args.do_test=flags[2].item()

#Builditerators.
dl_type=args.dataloader_type

iftrain_dataloaderisnotNone:
train_data_iterator=iter(train_dataloader)ifdl_type=='single'
elseiter(cyclic_iter(train_dataloader))
else:
train_data_iterator=None

ifvalid_dataloaderisnotNone:
valid_data_iterator=iter(valid_dataloader)ifdl_type=='single'
elseiter(cyclic_iter(valid_dataloader))
else:
valid_data_iterator=None

iftest_dataloaderisnotNone:
test_data_iterator=iter(test_dataloader)ifdl_type=='single'
elseiter(cyclic_iter(test_dataloader))
else:
test_data_iterator=None

returntrain_data_iterator,valid_data_iterator,test_data_iterator

5.2 DDP

在 get_model 之中，有如下代碼使用 DDP。

frommegatron.modelimportDistributedDataParallelasLocalDDP
fromtorch.nn.parallel.distributedimportDistributedDataParallelastorchDDP

ifwrap_with_ddp:
ifargs.DDP_impl=='torch':
i=torch.cuda.current_device()
model=[torchDDP(model_module,device_ids=[i],output_device=i,
process_group=mpu.get_data_parallel_group())
formodel_moduleinmodel]

elifargs.DDP_impl=='local':
model=[LocalDDP(model_module,
args.accumulate_allreduce_grads_in_fp32,
args.use_contiguous_buffers_in_local_ddp)
formodel_moduleinmodel]

else:
raiseNotImplementedError('UnknownDDPimplementationspecified:'
'{}.Exiting.'.format(args.DDP_impl))

所以我們看看 megatron 自己的 DDP實現(xiàn)。

5.2.1 定義

定義只有注釋可以看看，使用連續(xù)的（contiguous）內(nèi)存來存儲和累積梯度，每一種類型的張量屬于一個統(tǒng)一的內(nèi)存，可以統(tǒng)一做 allreduce。

classDistributedDataParallel(DistributedDataParallelBase):
"""DDPwithcontiguousbuffersoptionstostorreandaccumulategradients.
Thisclass:
-hasthepotentialtoreducememoryfragmentation.
-providestheoptiontodothegradientaccumulation
inatypeotherthantheparamstype(forexamplefp32)

Arguments:
module:inputmodel.
accumulate_allreduce_grads_in_fp32:iftruedothegradientaccumulation
andthegradientall-reduceallininfloat32.Ifthisoptionis
true,werequire`use_contiguous_buffers`tobetruetoo.
use_contiguous_buffers:iftrue,useacontiguousbuffertostorethe
gradients.
"""

5.2.2 初始化

初始化方法的目的是把同類型梯度連續(xù)存儲。

def__init__(self,module,
accumulate_allreduce_grads_in_fp32,
use_contiguous_buffers):

super(DistributedDataParallel,self).__init__(module)

self.accumulate_allreduce_grads_in_fp32
=accumulate_allreduce_grads_in_fp32
self.use_contiguous_buffers=use_contiguous_buffers
#Ifweareusingfp32-accumulate-allreduceexplicitly
#thismeansweneedmaingradsinacontinousbuffer.
ifself.accumulate_allreduce_grads_in_fp32:
assertself.use_contiguous_buffers

#===================================
#Restofthispartappliesonlyto
#thecaseweusecontinuousbuffers.
#===================================
self._grad_buffers=None
ifself.use_contiguous_buffers:#這里只考慮連續(xù)內(nèi)存
self._grad_buffers={}#定義buffer

#Simplefunctiontodefinebuffertype.
def_get_buffer_type(param):#返回buffer類型
returntorch.floatif
self.accumulate_allreduce_grads_in_fp32elseparam.dtype

#Firstcalculatetotalnumberofelementspertype.
type_num_elements={}
forparaminself.module.parameters():#遍歷模型參數(shù)
ifparam.requires_grad:#如果需要計算梯度
dtype=_get_buffer_type(param)#獲取參數(shù)類型
type_num_elements[dtype]=type_num_elements.get(dtype,0)
+param.data.nelement()#該類型參數(shù)數(shù)目做相應(yīng)增加

#目前type_num_elements是各種類型參數(shù)的個數(shù)
#Allocatethebuffer.
fordtype,num_elementsintype_num_elements.items():#遍歷各種類型
self._grad_buffers[dtype]=MemoryBuffer(num_elements,dtype)#分配內(nèi)存

#這里是假定反向傳播是參數(shù)的反方向，存儲每個參數(shù)梯度的起始位置
#Assumethebackproporderisreversetheparamsorder,
#storethestartindexforthegradients.
forparaminself.module.parameters():#遍歷模型參數(shù)
ifparam.requires_grad:#如果需要計算梯度
dtype=_get_buffer_type(param)#獲取參數(shù)類型
type_num_elements[dtype]-=param.data.nelement()#減少size
#確定該參數(shù)在MemoryBuffer的位置
param.main_grad=self._grad_buffers[dtype].get(#獲取該參數(shù)對應(yīng)的內(nèi)存
param.data.shape,type_num_elements[dtype])

#Backwardhook.
#Accumalationfunctionforthegradients.Weneed
#tostorethemsotheydon'tgooutofscope.
self.grad_accs=[]
#Loopoveralltheparametersinthemodel.
forparaminself.module.parameters():#遍歷模型參數(shù)
ifparam.requires_grad:#如果需要計算梯度
#Expandsowegetaccesstograd_fn.
param_tmp=param.expand_as(param)
#Getthegradientaccumulatorfuncttion.
grad_acc=param_tmp.grad_fn.next_functions[0][0]#得到參數(shù)對應(yīng)的梯度函數(shù)
grad_acc.register_hook(self._make_param_hook(param))#注冊了hook
self.grad_accs.append(grad_acc)#統(tǒng)一管理梯度函數(shù)，其實就是bookkeeping作用

5.2.3 內(nèi)存

MemoryBuffer 是內(nèi)存抽象。

classMemoryBuffer:

def__init__(self,numel,dtype):
self.numel=numel
self.dtype=dtype
self.data=torch.zeros(self.numel,#初始化內(nèi)存
dtype=self.dtype,
device=torch.cuda.current_device(),
requires_grad=False)


defzero(self):
"""Resetthebuffertozero."""
self.data.zero_()


defget(self,shape,start_index):
"""Returnatensorwiththeinput`shape`asaviewintothe
1-Ddatastartingat`start_index`."""
end_index=start_index+shape.numel()#定位到該張量在內(nèi)存buffer之中的位置
assertend_index<=?self.numel,?
????????????'requested?tensor?is?out?of?the?buffer?range.'
????????buffer_tensor?=?self.data[start_index:end_index]?#?拿到內(nèi)存
????????buffer_tensor?=?buffer_tensor.view(shape)
????????return?buffer_tensor?#?

5.2.4 支撐函數(shù)

下面是兩個支撐函數(shù)，分別是用于拷貝梯度和將buffer清零。

def_make_param_hook(self,param):
"""Createtheall-reducehookforbackprop."""
#Hookusedforback-prop.
defparam_hook(*unused):
#Addthegradienttothebuffer.
ifparam.grad.dataisnotNone:
param.main_grad.add_(param.grad.data)#把梯度拷貝到連續(xù)內(nèi)存之中
#Nowwecandeallocategradmemory.
param.grad=None
returnparam_hook

defzero_grad_buffer(self):
"""Setthegradbufferdatatozero.Needstobecalledatthe
beginingofeachiteration."""
assertself._grad_buffersisnotNone,'buffersarenotinitialized.'
for_,buffer_inself._grad_buffers.items():
buffer_.zero()

我們假定模型有6個參數(shù)，3個 fp32，3 個 fp16，所以被組合成兩個連續(xù)內(nèi)存 MemoryBuffer。

5.2.5 梯度規(guī)約

allreduce_gradients 是 DDP 對外提供的 API，在后面 train step 之中會調(diào)用到。

defallreduce_gradients(self):
"""Reducegradientsacrossdataparallelranks."""
#Ifwehavebuffers,simplyreducethedatainthebuffer.
ifself._grad_buffersisnotNone:
#連續(xù)內(nèi)存
for_,buffer_inself._grad_buffers.items():#遍歷各種類型的buffer
buffer_.data/=mpu.get_data_parallel_world_size()
torch.distributed.all_reduce(#統(tǒng)一歸并
buffer_.data,group=mpu.get_data_parallel_group())
else:
#Otherwise,bucketizeandall-reduce
buckets={}#否則還是用桶來歸并
#Packthebuckets.
forparaminself.module.parameters():#遍歷梯度
ifparam.requires_gradandparam.gradisnotNone:
tp=param.data.type()
iftpnotinbuckets:
buckets[tp]=[]
buckets[tp].append(param)#同類型的梯度放到對應(yīng)類型的桶之中
param.main_grad=param.grad

#Foreachbucket,all-reduceandcopyall-reducedgrads.
fortpinbuckets:
bucket=buckets[tp]
grads=[param.grad.dataforparaminbucket]#把桶里的梯度拿出來
coalesced=_flatten_dense_tensors(grads)#打平梯度
coalesced/=mpu.get_data_parallel_world_size()
torch.distributed.all_reduce(#歸并
coalesced,group=mpu.get_data_parallel_group())
forbuf,syncedinzip(grads,_unflatten_dense_tensors(
coalesced,grads)):
buf.copy_(synced)

運行時候，分別對兩種類型的連續(xù)內(nèi)存做 AllReduce。

0x06 訓(xùn)練

Pretrain 之中會調(diào)用 train 來進行訓(xùn)練。

ifargs.do_trainandargs.train_iters>0:
iteration=train(forward_step_func,
model,optimizer,lr_scheduler,
train_data_iterator,valid_data_iterator)

6.1 訓(xùn)練主體

train 是常規(guī)的套路，大家基本上按照名字就可以理解。

deftrain(forward_step_func,model,optimizer,lr_scheduler,
train_data_iterator,valid_data_iterator):
"""Trainthemodelfunction."""
args=get_args()
timers=get_timers()

#Writeargstotensorboard
write_args_to_tensorboard()

#Turnontrainingmodewhichenablesdropout.
formodel_moduleinmodel:
model_module.train()#

#Trackingloss.
total_loss_dict={}

#Iterations.
iteration=args.iteration

report_memory_flag=True
whileiterationargs.exit_duration_in_mins])
torch.distributed.all_reduce(
done_cuda,op=torch.distributed.ReduceOp.MAX)
done=done_cuda.item()
ifdone:
ifnotsaved_checkpoint:
save_checkpoint_and_time(iteration,model,optimizer,
lr_scheduler)
sys.exit()

#Exitingbasedoniterations
ifargs.exit_intervalanditeration%args.exit_interval==0:
ifnotsaved_checkpoint:
save_checkpoint_and_time(iteration,model,optimizer,
lr_scheduler)
torch.distributed.barrier()
sys.exit()

returniteration

6.2 訓(xùn)練step

train_step 會獲取 get_forward_backward_func 得到 schedule，因為是流水線并行，所以需要 schedule 如何具體訓(xùn)練。

deftrain_step(forward_step_func,data_iterator,
model,optimizer,lr_scheduler):
"""Singletrainingstep."""
args=get_args()
timers=get_timers()

#Setgradtozero.
ifargs.DDP_impl=='local'andargs.use_contiguous_buffers_in_local_ddp:
forpartitioninmodel:
partition.zero_grad_buffer()
optimizer.zero_grad()

#獲取訓(xùn)練schedule
forward_backward_func=get_forward_backward_func()
losses_reduced=forward_backward_func(#進行訓(xùn)練
forward_step_func,data_iterator,model,
optimizer,timers,forward_only=False)

#Emptyunusedmemory
ifargs.empty_unused_memory_level>=1:
torch.cuda.empty_cache()

#All-reduceifneeded.
ifargs.DDP_impl=='local':
formodel_moduleinmodel:
model_module.allreduce_gradients()

#All-reduceword_embeddings'gradacrossfirstandlaststagestoensure
#thatword_embeddingsparametersstayinsync.
#Thisshouldonlyrunformodelsthatsupportpipelinedmodelparallelism
#(BERTandGPT-2).
ifmpu.is_rank_in_embedding_group(ignore_virtual=True)and
mpu.get_pipeline_model_parallel_world_size()>1:
ifmpu.is_pipeline_first_stage(ignore_virtual=True):
unwrapped_model=model[0]
elifmpu.is_pipeline_last_stage(ignore_virtual=True):
unwrapped_model=model[-1]
else:#WedonotsupporttheinterleavedscheduleforT5yet.
unwrapped_model=model[0]
unwrapped_model=unwrap_model(
unwrapped_model,(torchDDP,LocalDDP,Float16Module))

ifunwrapped_model.share_word_embeddings:
word_embeddings_weight=unwrapped_model.word_embeddings_weight()
ifargs.DDP_impl=='local':
grad=word_embeddings_weight.main_grad
else:
grad=word_embeddings_weight.grad
torch.distributed.all_reduce(grad,group=mpu.get_embedding_group())

#Updateparameters.
update_successful,grad_norm,num_zeros_in_grad=optimizer.step()

#Updatelearningrate.
ifupdate_successful:
increment=get_num_microbatches()*
args.micro_batch_size*
args.data_parallel_size
lr_scheduler.step(increment=increment)
skipped_iter=0
else:
skipped_iter=1

#Emptyunusedmemory
ifargs.empty_unused_memory_level>=2:
torch.cuda.empty_cache()

ifmpu.is_pipeline_last_stage(ignore_virtual=True):
#Averagelossacrossmicrobatches.
loss_reduced={}
forkeyinlosses_reduced[0]:
losses_reduced_for_key=[x[key]forxinlosses_reduced]
loss_reduced[key]=sum(losses_reduced_for_key)/len(losses_reduced_for_key)
returnloss_reduced,skipped_iter,grad_norm,num_zeros_in_grad
return{},skipped_iter,grad_norm,num_zeros_in_grad

6.3 獲取schedule

get_forward_backward_func 獲取 pipeline 的schedule，這里分為 flush 和 interleaving 兩種，我們后續(xù)會分析這兩種schedule。

defget_forward_backward_func():
args=get_args()
ifmpu.get_pipeline_model_parallel_world_size()>1:
ifargs.virtual_pipeline_model_parallel_sizeisnotNone:
forward_backward_func=forward_backward_pipelining_with_interleaving
else:
forward_backward_func=forward_backward_pipelining_without_interleaving
else:
forward_backward_func=forward_backward_no_pipelining
returnforward_backward_func

訓(xùn)練邏輯大體拓展為：

至此，Megatron 基本架構(gòu)分析完畢，下一篇我們介紹模型并行設(shè)置。

審核編輯：湯梓紅