pytest学习-pytorch单元测试

pytorch单元测试

希望测试pytorch各种算子、block、网络等在不同硬件平台,不同软件版本下的计算误差、耗时、内存占用等指标.

本文基于torch.testing._internal

一.公共模块[common.py]

import torch
from torch import nn
import math
import torch.nn.functional as F
import time
import os
import socket
import sys
from datetime import datetime
import numpy as np
import collections
import math
import json
import copy
import traceback
import subprocess
import unittest
import torch
import inspect
from torch.testing._internal.common_utils import TestCase, run_tests,parametrize,instantiate_parametrized_tests
from torch.testing._internal.common_distributed import MultiProcessTestCase
import torch.distributed as dist

os.environ["MASTER_ADDR"]="localhost"
os.environ["MASTER_PORT"]="29500"
os.environ["RANDOM_SEED"]="0" 

device="cpu"
device_type="cpu"
device_name="cpu"try:if torch.cuda.is_available():     
        device_name=torch.cuda.get_device_name().replace(" ","")
        device="cuda:0"
        device_type="cuda"
        ccl_backend='nccl'except:pass

host_name=socket.gethostname()    
sdk_version=os.getenv("SDK_VERSION","")#从环境变量中获取sdk版本号
metric_data_root=os.getenv("TORCH_UT_METRICS_DATA","./ut_data")#日志存放的目录
device_count=torch.cuda.device_count()ifnot os.path.exists(metric_data_root):
    os.makedirs(metric_data_root)defdevice_warmup(device):'''设备warmup,确保设备已经正常工作,排除设备初始化的耗时'''
    left = torch.rand([128,512], dtype = torch.float16).to(device)
    right = torch.rand([512,128], dtype = torch.float16).to(device)
    out=torch.matmul(left,right)
    torch.cuda.synchronize()

torch.manual_seed(1) 
np.random.seed(1)defloop_decorator(loops,rank=0):'''循环装饰器，用于统计函数的执行时间，内存占用等'''defdecorator(func):defwrapper(*args,**kwargs):
            latency=[]
            memory_allocated_t0=torch.cuda.memory_allocated(rank)for _ inrange(loops):
                input_copy=[x.clone()for x in args]
                beg= datetime.now().timestamp()*1e6
                pred= func(*input_copy)
                gt=kwargs["golden"]
                torch.cuda.synchronize()
                end=datetime.now().timestamp()*1e6
                mse = torch.mean(torch.pow(pred.cpu().float()- gt.cpu().float(),2)).item()
                latency.append(end-beg)
            memory_allocated_t1=torch.cuda.memory_allocated(rank)
            avg_latency=np.mean(latency[len(latency)//2:]).round(3)
            first_latency=latency[0]return{"first_latency":first_latency,"avg_latency":avg_latency,"memory_allocated":memory_allocated_t1-memory_allocated_t0,"mse":mse}return wrapper
    return decorator

classTorchUtMetrics:'''用于统计测试结果，比较之前的最小值'''def__init__(self,ut_name,thresold=0.2,rank=0):
        self.ut_name=f"{ut_name}_{rank}"
        self.thresold=thresold
        self.rank=rank
        self.data={"ut_name":self.ut_name,"metrics":[]}
        self.metrics_path=os.path.join(metric_data_root,f"{self.ut_name}_{self.rank}.jon")try:withopen(self.metrics_path,"r")as f:
                self.data=json.loads(f.read())except:passdef__enter__(self):
        self.beg= datetime.now().timestamp()*1e6return self

    def__exit__(self, exc_type, exc_val, exc_tb):        
        self.report()
        self.save_data()defsave_data(self):withopen(self.metrics_path,"w")as f:
            f.write(json.dumps(self.data,indent=4))defset_metrics(self,metrics):
        self.end=datetime.now().timestamp()*1e6
        item=collections.OrderedDict()
        item["time"]=datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
        item["sdk_version"]=sdk_version
        item["device_name"]=device_name
        item["host_name"]=host_name
        item["metrics"]=metrics
        item["metrics"]["e2e_time"]=self.end-self.beg
        self.cur_item=item
        self.data["metrics"].append(self.cur_item)defget_metric_names(self):return self.data["metrics"][0]["metrics"].keys()defget_min_metric(self,metric_name,devicename=None):
        min_value=0
        min_value_index=-1for idx,item inenumerate(self.data["metrics"]):if devicename and(devicename!=item['device_name']):continue            
            val=float(item["metrics"][metric_name])if min_value_index==-1or val<min_value:
                min_value=val
                min_value_index=idx
        return min_value,min_value_index

    defget_metric_info(self,index):
        metrics=self.data["metrics"][index]returnf'{metrics["device_name"]}@{metrics["sdk_version"]}'defreport(self):assertlen(self.data["metrics"])>0for metric_name in self.get_metric_names():
            min_value,min_value_index=self.get_min_metric(metric_name)
            min_value_same_dev,min_value_index_same_dev=self.get_min_metric(metric_name,device_name)
            cur_value=float(self.cur_item["metrics"][metric_name])print(f"-------------------------------{metric_name}-------------------------------")print(f"{cur_value}#{device_name}@{sdk_version}")if min_value_index_same_dev>=0:print(f"{min_value_same_dev}#{self.get_metric_info(min_value_index_same_dev)}")if min_value_index>=0:print(f"{min_value}#{self.get_metric_info(min_value_index)}")

二.普通算子测试[test_clone.py]

from common import*classTestCaseClone(TestCase):#如果不满足条件,则跳过这个测试@unittest.skipIf(device_count>1,"Not enough devices")deftest_todo(self):print(".TODO")#框架会自动遍历以下参数组合@parametrize("shape",[(10240,20480),(128,256)])@parametrize("dtype",[torch.float16,torch.float32])deftest_clone(self,shape,dtype):#让这个函数循环执行loops次,统计第一次执行的耗时、后半段的平均时间、整个执行过程总的GPU内存使用量@loop_decorator(loops=5)defrun(input_dev):
            output=input_dev.clone()return output
        
        #记录整个测试的总耗时,保存统计量,输出摘要(self._testMethodName:测试方法,result:函数返回值,metrics:统计量)with TorchUtMetrics(ut_name=self._testMethodName,thresold=0.2)as m:
            input_host=torch.ones(shape,dtype=dtype)*np.random.rand()
            input_dev=input_host.to(device)
            metrics=run(input_dev,golden=input_host.cpu())
            m.set_metrics(metrics)assert(metrics["mse"]==0)
        
instantiate_parametrized_tests(TestCaseClone)if __name__ =="__main__":
    run_tests()

三.集合通信测试[test_ccl.py]

from common import*classTestCCL(MultiProcessTestCase):'''CCL测试用例'''def_create_process_group_vccl(self, world_size, store):
        dist.init_process_group(
            ccl_backend, world_size=world_size, rank=self.rank, store=store
        )        
        pg = dist.distributed_c10d._get_default_group()return pg

    defsetUp(self):super().setUp()
        self._spawn_processes()deftearDown(self):super().tearDown()try:
            os.remove(self.file_name)except OSError:pass@propertydefworld_size(self):return4#框架会自动遍历以下参数组合@unittest.skipIf(device_count<4,"Not enough devices")@parametrize("op",[dist.ReduceOp.SUM])@parametrize("shape",[(1024,8192)])@parametrize("dtype",[torch.int64])deftest_allreduce(self,op,shape,dtype):if self.rank >= self.world_size:return
        
        store = dist.FileStore(self.file_name, self.world_size)
        pg = self._create_process_group_vccl(self.world_size, store)ifnot torch.distributed.is_initialized():return
    
        torch.cuda.set_device(self.rank)
        device = torch.device(device_type,self.rank)
        device_warmup(device)#让这个函数循环执行loops次,统计第一次执行的耗时、后半段的平均时间、整个执行过程总的GPU内存使用量@loop_decorator(loops=5,rank=self.rank)defrun(input_dev):
            dist.all_reduce(input_dev, op=op)return input_dev
        
        #记录整个测试的总耗时,保存统计量,输出摘要(self._testMethodName:测试方法,result:函数返回值,metrics:统计量)with TorchUtMetrics(ut_name=self._testMethodName,thresold=0.2,rank=self.rank)as m:
            input_host=torch.ones(shape,dtype=dtype)*(100+self.rank)
            gt=[torch.ones(shape,dtype=dtype)*(100+i)for i inrange(self.world_size)]
            gt_=gt[0]for i inrange(1,self.world_size):
                gt_=gt_+gt[i]
            input_dev=input_host.to(device)
            metrics=run(input_dev,golden=gt_)
            m.set_metrics(metrics)assert(metrics["mse"]==0)
        dist.destroy_process_group(pg)
    
instantiate_parametrized_tests(TestCCL)if __name__ =="__main__":
    run_tests()

四.测试命令

# 运行所有的测试
pytest -v-s-p no:warnings --html=torch_report.html --self-contained-html --capture=sys ./

# 运行某一个测试
python3 test_clone.py -k"test_clone_shape_(128, 256)_float32"

五.测试报告