Repository: pytorch
Issue: C++ torch::cuda::synchronize speeds up training
Labels: module: cuda,triaged

## 🐛 Bug

There are cases where calling _torch::cuda::synchronize()_ appears to speed up training.
There have been some other issues on this (#43947  and #44103 )
I've noticed the counter-intuitive slow downs from the c++ side when replacing _tensor.item<float>_ calls with non-synchronizing accumulation and seeing slower training speeds.

## To Reproduce

```
struct MImpl : torch::nn::Module {
 MImpl(int64_t n,int64_t h,int64_t k) {
  lstm=register_module("lstm", torch::nn::LSTM(torch::nn::LSTMOptions(n,h).num_layers(2).batch_first(true)));
  linear=register_module("linear", torch::nn::Linear(h,k));
 }
 torch::Tensor forward(torch::Tensor x) {
  auto r=lstm->forward(x);
  return linear->forward(std::get<0>(r).select(1,-1));
 }
 torch::nn::LSTM lstm=nullptr;
 torch::nn::Linear linear=nullptr;
};
TORCH_MODULE(M);
```

Using the above LSTM module, running the following training code on a GTX 1080 gpu is about 50% faster with the synchronize call (?)

``` auto m=M(28,128,10); m->to(torch::kCUDA);
 auto l=torch::nn::CrossEntropyLoss();
 auto o=torch::optim::AdamW(m->parameters());
 for(size_t i=0; i<2000; ++i) {
  o.zero_grad();
  auto x=torch::randn({30,28,28},torch::kCUDA);
  auto y=torch::randint(9,30,torch::TensorOptions().device(torch::kCUDA).dtype(torch::kLong));
  auto t=l->forward(m->forward(x),y);
  t.backward();
  torch::cuda::synchronize(-1);
  o.step();
 }
```

## Environment

Output from torch::show_config from a linux test machine with libtorch v1.9.0

```
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 v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - 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-declarations -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -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=1.9.0, 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 @ngimel