Torch1.10.2 is slower than torch1.9.1
Labels: oncall: distributed, triaged, module: ddp

### 🐛 Describe the bug

I recently upgraded my pytorch version from 1.9.1 to 1.10.2 due to project needs. But found that the training speed dropped by 20% to 30% for the same multi-tasks model.

After profiling, I found the reason is that the code of torch1.10 has the following logic:   
https://github.com/pytorch/pytorch/blob/v1.10.2/torch/nn/parallel/distributed.py#L1378    
  
That means, When using DDP, the model will assign module buffers before each forward. Time will be spent in assigning buffers:  
https://github.com/pytorch/pytorch/blob/71f889c7d265b9636b93ede9d651c0a9c4bee191/torch/nn/parallel/distributed.py#L752   
  
When the model is complex, the time spent should be considerable.     

I would like to know how to avoid this part of the time overhead.   


### Versions

Python                  3.6.6 (default, Jan 26 2019, 16:53:05) [GCC 4.8.5 20150623 (Red Hat 4.8.5-36)]
numpy                   1.19.5
PyTorch                 1.10.2+cu111 @/mnt/data-1/kongtao.hu/environments/pytorch1102_cu111/lib/python3.6/site-packages/torch
PyTorch debug build     False
Torchvision             0.11.3+cu111 @/mnt/data-1/kongtao.hu/environments/pytorch1102_cu111/lib/python3.6/site-packages/torchvision
GPU available           Yes
GPU 0,1,2,3             TITAN V (arch=7.0)
Driver version          455.38
CUDA_HOME               /usr/local/cuda-11.1
----------------------  --------------------------------------------------------------------------------------------------------------------------------
PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.1
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
  - CuDNN 8.0.5
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, 

cc @pietern @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @SciPioneer @H-Huang @kwen2501 @ezyang @VitalyFedyunin @ngimel