测试bert_base不同并行方式下的推理性能

测试bert_base不同并行方式下的推理性能

• 一.测试数据
• 二.测试步骤
• • 1.生成bert配置文件
  • 2.安装依赖
  • [3.deepspeed 4卡tp并行](#3.deepspeed 4卡tp并行)
  • [4.FSDP 4卡并行](#4.FSDP 4卡并行)
  • 5.手动将权值平均拆到4张卡,单进程多卡推理
  • 6.手动切分成4份,基于NCCL实现pipeline并行

本文测试了bert_base模型在不同并行方式下的推理性能

约束

• 1.当前服务器上GPU不支持P2P且链路仅为PCIE GEN1 X16

可参考的点

• deepspeed 推理的使用
• FSDP推理的使用
• 如果将权值拆到多卡
• 自定义pipeline并行(切分网络并插入自定义修改)
• 如何自动处理pytorch算子输入tensor不在同一个设备上的问题

一.测试数据

并行方式	QPS	GPU利用率
deepspeed 4卡tp并行	175.73	rank:0 util:100.00 rank:1 util:100.00 rank:2 util:97.00 rank:3 util:97.00
FSDP 4卡并行	137.80	rank:0 util:40.00 rank:1 util:40.00 rank:2 util:39.00 rank:3 util:40.00
手动将权值平均拆到4张卡,单进程多卡推理	29.34
手动切分成4份,基于NCCL实现pipeline并行	244.76	rank:1 util:40.00 rank:0 util:97.00 rank:2 util:39.00 rank:3 util:78.00

二.测试步骤

1.生成bert配置文件

tee ./config.json <<-'EOF'
{
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "directionality": "bidi",
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 0,
  "pooler_fc_size": 768,
  "pooler_num_attention_heads": 12,
  "pooler_num_fc_layers": 3,
  "pooler_size_per_head": 128,
  "pooler_type": "first_token_transform",
  "type_vocab_size": 2,
  "vocab_size": 21128
}
EOF

2.安装依赖

pip install nvidia-ml-py3

3.deepspeed 4卡tp并行

tee ds_bert_infer.py <<-'EOF'
import torch
import deepspeed
import os
from deepspeed.accelerator import get_accelerator
import time
import torch.distributed as dist
import pynvml
import numpy as np
import threading

#统计GPU利用率
class PynvmlGPUUtilizationThread(threading.Thread):
    def __init__(self,device,interval=1):
        super().__init__()
        self.interval = interval
        self.running = True
        self.device=device        
        self.handle = pynvml.nvmlDeviceGetHandleByIndex(device)
        self.utilizations=[]
        
    def run(self):
        while self.running:
            self.get_and_print_gpu_utilization()
            time.sleep(self.interval)
    
    def stop(self):
        self.running = False
    
    def get_and_print_gpu_utilization(self):
        utilization = pynvml.nvmlDeviceGetUtilizationRates(self.handle)
        self.utilizations.append(utilization.gpu)
        
    def data(self):
        return np.max(self.utilizations)
    
def inference():    
    deepspeed.init_distributed(dist_backend='nccl')
    world_size = torch.distributed.get_world_size()
    local_rank=int(os.environ['LOCAL_RANK'])
    rank=torch.distributed.get_rank()
    pynvml.nvmlInit()
    
    torch.manual_seed(1)
    from transformers import AutoModelForMaskedLM,BertConfig
    config=BertConfig.from_pretrained("./config.json")
    model = AutoModelForMaskedLM.from_config(config)
    model.eval()
    engine = deepspeed.init_inference(model,
                                        tensor_parallel={"tp_size": world_size},
                                        dtype=torch.float32,
                                        replace_with_kernel_inject=True)
    device=get_accelerator().current_device_name()
    input_tokens=torch.randint(0,config.vocab_size,(1,128)).to(device)
    epoch=1024
    gpu_thread = PynvmlGPUUtilizationThread(local_rank,interval=1)
    gpu_thread.start()    
    t0=time.time()
    for i in range(epoch):
        outputs = engine(input_tokens)
    dist.barrier()
    torch.cuda.synchronize()
    t1=time.time()
    gpu_thread.stop()
    gpu_thread.join()       
    time.sleep(0.2*rank)        
    if rank==0:
        qps=epoch/(t1-t0)
        print(f"default stream qps:{qps:.2f}")
    print(f"rank:{rank} util:{gpu_thread.data():.2f}")
    
    stream_nbs=[1,2,4,8]    
    for n in stream_nbs:
        dist.barrier()
        if rank==0:
            print("-----------------------------------------------")
        streams=[torch.cuda.Stream() for _ in range(n)]
        total_samples=0        
        gpu_thread = PynvmlGPUUtilizationThread(local_rank,interval=1)
        gpu_thread.start()
        t0=time.time()
        for _ in range(epoch//n):
            for i in range(n):
                with torch.cuda.stream(streams[i]):
                    total_samples+=1
                    outputs = engine(input_tokens)        
        dist.barrier()
        torch.cuda.synchronize()
        t1=time.time()
        gpu_thread.stop()
        gpu_thread.join()    
        time.sleep(0.2*rank)        
        if rank==0:
            qps=total_samples/(t1-t0)
            print(f"{n} streams qps:{qps:.2f}")                
        print(f"rank:{rank} util:{gpu_thread.data():.2f}")
        
if __name__ == "__main__":
    inference()

EOF
deepspeed --num_gpus=4 ds_bert_infer.py

输出

------------------------------------------------------
default stream qps: 147.10
rank:0 util:90.00
rank:1 util:86.00
rank:2 util:89.00
rank:3 util:89.00
-----------------------------------------------
1 streams qps:162.62
rank:0 util:100.00
rank:1 util:100.00
rank:2 util:92.00
rank:3 util:88.00
-----------------------------------------------
2 streams qps:177.31
rank:0 util:100.00
rank:1 util:100.00
rank:2 util:99.00
rank:3 util:98.00
-----------------------------------------------
4 streams qps:176.11
rank:0 util:100.00
rank:1 util:100.00
rank:2 util:98.00
rank:3 util:97.00
-----------------------------------------------
8 streams qps:175.73
rank:0 util:100.00
rank:1 util:100.00
rank:2 util:97.00
rank:3 util:97.00

4.FSDP 4卡并行

tee fsdp_bert_infer.py <<-'EOF'
import time
import os
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
import torchvision.models as models
import torch.nn as nn
import torch.nn.init as init
import time
import pynvml
import numpy as np
import threading

class PynvmlGPUUtilizationThread(threading.Thread):
    def __init__(self,device,interval=1):
        super().__init__()
        self.interval = interval
        self.running = True
        self.device=device        
        self.handle = pynvml.nvmlDeviceGetHandleByIndex(device)
        self.utilizations=[]
        
    def run(self):
        while self.running:
            self.get_and_print_gpu_utilization()
            time.sleep(self.interval)
    
    def stop(self):
        self.running = False
    
    def get_and_print_gpu_utilization(self):
        utilization = pynvml.nvmlDeviceGetUtilizationRates(self.handle)
        self.utilizations.append(utilization.gpu)
        
    def data(self):
        return np.max(self.utilizations)
        
def cleanup():
    dist.destroy_process_group()

def demo_fsdp(rank, world_size,multi_stream):
    
    pynvml.nvmlInit()
    device = torch.device(f"cuda:{rank}")
    torch.manual_seed(1)
    from transformers import AutoModelForMaskedLM,BertConfig
    config=BertConfig.from_pretrained("./config.json")
    model = AutoModelForMaskedLM.from_config(config)
    model.eval()
    
    fsdp_model = FSDP(model,forward_prefetch=True).to(device)
    input_tokens=torch.randint(0,config.vocab_size,(1,128)).to(device)
    epoch_sz=1024
    gpu_thread = PynvmlGPUUtilizationThread(local_rank,interval=1)
    gpu_thread.start()    
    sz=8
    total_sample=0
    streams=[torch.cuda.Stream() for _ in range(sz)]
    t0=time.time()
    for epoch in range(epoch_sz):
        with torch.no_grad():
            outputs=[]
            if multi_stream:
                for i in range(sz):
                    with torch.cuda.stream(streams[i]):
                        total_sample+=1
                        outputs.append(fsdp_model(input_tokens))
            else:
                output = fsdp_model(input_tokens)
                total_sample+=1
    torch.cuda.synchronize(rank)
    t1=time.time()
    gpu_thread.stop()
    gpu_thread.join()       
    time.sleep(0.2*rank)        
    if rank==0:
        qps=total_sample/(t1-t0)
        print(f"qps:{qps:.2f}")
    print(f"rank:{rank} util:{gpu_thread.data():.2f}")
    cleanup()

if __name__ == "__main__":
    dist.init_process_group(backend='nccl')
    world_size = torch.distributed.get_world_size()
    rank=torch.distributed.get_rank()
    local_rank=int(os.environ['LOCAL_RANK'])
    torch.cuda.set_device(local_rank)
    demo_fsdp(local_rank,world_size,True)
EOF
torchrun -m --nnodes=1 --nproc_per_node=4 fsdp_bert_infer

输出

qps:137.80
rank:0 util:40.00
rank:1 util:40.00
rank:2 util:39.00
rank:3 util:40.00

5.手动将权值平均拆到4张卡,单进程多卡推理

tee split_bert_infer.py <<-'EOF'
import torch
import os
import time
from torch.utils._python_dispatch import TorchDispatchMode
from dataclasses import dataclass
from typing import Any

@dataclass
class _ProfilerState:
    cls: Any
    object: Any = None

class EmptyModule(torch.nn.Module):
    def __init__(self):
        super(EmptyModule, self).__init__()
        pass
    def forward(self,x):
        return x
        
class TorchDumpDispatchMode(TorchDispatchMode):
    def __init__(self,parent):
        super().__init__()
        self.parent=parent
        self.op_index=0
        self.cvt_count=0

    def get_max_gpu_id(self,tensors):
        max_gpu_id = -1
        max_index = -1
        tensor_index=[]
        for i, tensor in enumerate(tensors):
            if not isinstance(tensor, torch.Tensor):
                continue
            tensor_index.append(i)
            if tensor.is_cuda:
                gpu_id = tensor.get_device()
                if gpu_id > max_gpu_id:
                    max_gpu_id = gpu_id
                    max_index = i
        if max_gpu_id == -1:
            return None, None,tensor_index
        return max_index, max_gpu_id,tensor_index

    def convert(self,op_type,tensor_list):
        index, gpu_id,tensor_index = self.get_max_gpu_id(tensor_list)
        if index is None:
            return
        keep_index=set(tensor_index)-set([index])
        device=torch.device(f"cuda:{gpu_id}")
        for i in keep_index:
            if tensor_list[i].device!=device:
                #print(f"{op_type} {i} {tensor_list[i].device} -> {device}")
                tensor_list[i].data=tensor_list[i].data.to(device,non_blocking=True) 
                #卡间通信是串行的,所有多stream并不能充分提升性能

    def __torch_dispatch__(self, func, types, args=(),kwargs=None):
        func_packet = func._overloadpacket
        if kwargs is None:
            kwargs = {}
        op_type=f"{func}"
        self.op_index+=1
        if isinstance(args, list) or isinstance(args, tuple):
            self.convert(op_type,args)
        elif isinstance(args[0], list) or isinstance(args[0], tuple):
            self.convert(op_type,args[0])
        else:
            print(op_type)
        output= func(*args,**kwargs)
        return output

class TorchDumper:
    def __init__(self,**kwargs):
        self.p= _ProfilerState(TorchDumpDispatchMode)
        self.kwargs=kwargs

    def __enter__(self):
        if self.p.object is None:
            o = self.p.cls(self,**self.kwargs)
            o.__enter__()
            self.p.object = o
        else:
            self.p.object.step()
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        TorchDumper._CURRENT_Dumper = None
        if self.p.object is not None:
            self.p.object.__exit__(exc_type, exc_val, exc_tb)
            del self.p.object
            
torch.manual_seed(1)
from transformers import AutoModelForMaskedLM,BertConfig
config=BertConfig.from_pretrained("./config.json")
model = AutoModelForMaskedLM.from_config(config)
model.eval()

cur_dev=0
from collections import OrderedDict

param_size=OrderedDict()
total_size=0
for name, param in model.named_parameters():
    #print(f"{name} {param.device} {param.shape}")
    sz=param.numel()*param.element_size()
    key=".".join(name.split(".")[:-1])
    if key not in param_size:
        param_size[key]=0
    param_size[key]+=sz
    total_size+=sz

for name, param in model.named_buffers():
    #print(name,param.device)
    sz=param.numel()*param.element_size()
    key=".".join(name.split(".")[:-1])
    if key not in param_size:
        param_size[key]=0
    param_size[key]+=sz
    total_size+=sz

sz_per_dev=total_size/4
cur_size=0

dev_map=OrderedDict()
for k,v in param_size.items():
    sz=v
    cur_size+=sz
    if cur_size>=sz_per_dev:
        print(cur_dev,cur_size)
        cur_size=0
        cur_dev+=1
    dev_map[k]=cur_dev

for name, param in model.named_parameters():
    key=".".join(name.split(".")[:-1])
    op=dict(model.named_parameters())[name]
    device=f"cuda:{dev_map[key]}"
    op.data=op.data.to(device)

for name, param in model.named_buffers():
    key=".".join(name.split(".")[:-1])
    op=dict(model.named_buffers())[name]
    device=f"cuda:{dev_map[key]}"
    op.data=op.data.to(device)

with TorchDumper():
    sz=4
    input_tokens=torch.randint(0,config.vocab_size,(1,128)).to("cuda:0")
    streams=[torch.cuda.Stream() for _ in range(sz)]
    batch_size=0
    t0=time.time()
    for epoch in range(1024):
        outputs=[]
        for i in range(sz):
            with torch.cuda.stream(streams[i]):
                batch_size+=1
                outputs.append(model(input_tokens))
    torch.cuda.synchronize()
    t1=time.time()
    print("qps:",batch_size/(t1-t0))
EOF
python split_bert_infer.py

输出

qps: 29.34

6.手动切分成4份,基于NCCL实现pipeline并行

tee pp_bert_infer.py <<-'EOF'
import torch
import os
import time
from collections import OrderedDict
import torch.distributed as dist
import torch.nn as nn
import torch.nn.init as init
import numpy as np
import time
import pynvml
import numpy as np
import threading

class EmptyModule(torch.nn.Module):
    def __init__(self):
        super(EmptyModule, self).__init__()
        pass
    def forward(self,x):
        return x[0]

class PynvmlGPUUtilizationThread(threading.Thread):
    def __init__(self,device,interval=1):
        super().__init__()
        self.interval = interval
        self.running = True
        self.device=device        
        self.handle = pynvml.nvmlDeviceGetHandleByIndex(device)
        self.utilizations=[]
        
    def run(self):
        while self.running:
            self.get_and_print_gpu_utilization()
            time.sleep(self.interval)
    
    def stop(self):
        self.running = False
    
    def get_and_print_gpu_utilization(self):
        utilization = pynvml.nvmlDeviceGetUtilizationRates(self.handle)
        self.utilizations.append(utilization.gpu)
        
    def data(self):
        return np.max(self.utilizations)

pynvml.nvmlInit()
        
dist.init_process_group(backend='nccl')
world_size = torch.distributed.get_world_size()
rank=torch.distributed.get_rank()
local_rank=int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)

torch.manual_seed(1)
from transformers import AutoModelForMaskedLM,BertConfig
config=BertConfig.from_pretrained("./config.json")
model = AutoModelForMaskedLM.from_config(config)
model.eval()

divided=[]
#查看modeling_bert.py找到相关的名字
submodules=[]
submodules.append(("embeddings",model.bert.embeddings))        
for i,m in enumerate(model.bert.encoder.layer[:3]):
    submodules.append((f"{i}",m))
    submodules.append((f"{i}-1",EmptyModule()))   
divided.append(submodules)

submodules=[]
for i,m in enumerate(model.bert.encoder.layer[3:7]):
    submodules.append((f"{i}",m))
    submodules.append((f"{i}-1",EmptyModule()))   
divided.append(submodules)

submodules=[]
   
for i,m in enumerate(model.bert.encoder.layer[7:11]):
    submodules.append((f"{i}",m))
    submodules.append((f"{i}-1",EmptyModule()))   
divided.append(submodules)

submodules=[]
   
for i,m in enumerate(model.bert.encoder.layer[11:]):
    submodules.append((f"{i}",m))
    submodules.append((f"{i}-1",EmptyModule()))
submodules.append(("cls",model.cls))        
divided.append(submodules)

device=f"cuda:{local_rank}"
example_input=torch.randint(0,config.vocab_size,(1,128)).to(device)
submodule=torch.nn.Sequential(OrderedDict(divided[local_rank])).to(device)

sreq=None
ts=[]

gpu_thread = PynvmlGPUUtilizationThread(local_rank,interval=1)
gpu_thread.start()
t0=time.time()
for epoch in range(1000):
    if sreq is not None and not sreq.is_completed():
        sreq.wait()
        sreq=None
    if local_rank!=0:
        tensor_size = torch.empty((3,), dtype=torch.int64).to(device)
        torch.distributed.recv(tensor_size,local_rank-1)
        example_input = torch.empty(tensor_size.tolist()).to(device)
        torch.distributed.recv(example_input,local_rank-1)
        #print("recv:",local_rank-1,example_input.shape)
    else:
        torch.manual_seed(1)
    output=submodule(example_input)
    if local_rank<world_size-1:
        #print("local_rank out:",output.shape)
        tensor_size = torch.tensor(output.size(), dtype=torch.int64).to(device)
        torch.distributed.isend(tensor_size,local_rank+1)
        sreq=torch.distributed.isend(output,local_rank+1)
        #torch.distributed.send(output,local_rank+1)
    elif local_rank==world_size-1:
        ts.append(time.time())
dist.barrier()
t1=time.time()

gpu_thread.stop()
gpu_thread.join()   
time.sleep(0.2*local_rank)        
print(f"rank:{local_rank} util:{gpu_thread.data():.2f}")
    
if local_rank==world_size-1:
    ts=ts[len(ts)//2:]
    print("latency:",ts[1]-ts[0],"qps:",len(ts)/(ts[-1]-ts[0]),1000/(t1-t0))
EOF
torchrun -m --nnodes=1 --nproc_per_node=4 pp_bert_infer	

输出:

rank:1 util:40.00
rank:0 util:97.00
rank:2 util:39.00
rank:3 util:78.00
latency: 0.002396106719970703 qps: 408.6954420411698 244.76515394402227