For tensor with non-zero offset, it must be multiplied by element size

Add regression test by creating Tensor in array of 6 elements with offset 3, which before the fix crashed with
```
C++ exception with description "setStorage: sizes [3, 3], strides [0, 1], storage offset 3, and itemsize 4 requiring a storage size of 24 are out of bounds for storage of size 15
Exception raised from checkInBoundsForStorage at /Users/nshulga/git/pytorch/pytorch/aten/src/ATen/native/Resize.h:123 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>) + 56 (0x104a9cd44 in libc10.dylib)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&) + 120 (0x104a9a05c in libc10.dylib)
frame #2: void at::native::checkInBoundsForStorage<long long>(c10::ArrayRef<long long>, c10::ArrayRef<long long>, long long, caffe2::TypeMeta const&, c10::Storage const&) + 656 (0x111dbd314 in libtorch_cpu.dylib)
frame pytorch#3: void at::native::setStrided<long long>(at::Tensor const&, c10::ArrayRef<long long>, c10::ArrayRef<long long>, long long) + 152 (0x111dcd22c in libtorch_cpu.dylib)
frame pytorch#4: at::native::as_strided_tensorimpl(at::Tensor const&, c10::ArrayRef<long long>, c10::ArrayRef<long long>, std::__1::optional<long long>) + 312 (0x111dccf98 in libtorch_cpu.dylib)
frame pytorch#5: c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor (at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::ArrayRef<c10::SymInt>, std::__1::optional<c10::SymInt>), &at::(anonymous namespace)::(anonymous namespace)::wrapper_CPU__as_strided(at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::ArrayRef<c10::SymInt>, std::__1::optional<c10::SymInt>)>, at::Tensor, c10::guts::typelist::typelist<at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::ArrayRef<c10::SymInt>, std::__1::optional<c10::SymInt>>>, at::Tensor (at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::ArrayRef<c10::SymInt>, std::__1::optional<c10::SymInt>)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::ArrayRef<c10::SymInt>, std::__1::optional<c10::SymInt>) + 104 (0x1129a1e94 in libtorch_cpu.dylib)
frame pytorch#6: at::_ops::as_strided::call(at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::ArrayRef<c10::SymInt>, std::__1::optional<c10::SymInt>) + 476 (0x112200ad0 in libtorch_cpu.dylib)
frame pytorch#7: at::Tensor::as_strided(c10::ArrayRef<long long>, c10::ArrayRef<long long>, std::__1::optional<long long>) const + 236 (0x1115db098 in libtorch_cpu.dylib)
frame pytorch#8: at::native::expand(at::Tensor const&, c10::ArrayRef<long long>, bool) + 348 (0x111dcc0d4 in libtorch_cpu.dylib)
frame pytorch#9: c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, bool), &torch::ADInplaceOrView::(anonymous namespace)::expand(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, bool)>, at::Tensor, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, bool>>, at::Tensor (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, bool)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, bool) + 116 (0x1157ac410 in libtorch_cpu.dylib)
frame pytorch#10: c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, bool), &torch::autograd::VariableType::(anonymous namespace)::expand(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, bool)>, at::Tensor, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, bool>>, at::Tensor (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, bool)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, bool) + 992 (0x114e8b010 in libtorch_cpu.dylib)
frame pytorch#11: at::_ops::expand::call(at::Tensor const&, c10::ArrayRef<c10::SymInt>, bool) + 316 (0x112743c90 in libtorch_cpu.dylib)
frame pytorch#12: at::expand_size(at::Tensor const&, c10::ArrayRef<long long>) + 164 (0x1047d82b4 in basic)
frame pytorch#13: BasicTest_TestForBlobResizeCPU_Test::TestBody() + 284 (0x1047d8048 in basic)
```
Pull Request resolved: pytorch#158690
Approved by: https://github.com/angelayi

)

Summary:
This diff fixes two things which come up when testing a tgif-published pt2 model remote net:
1) Updates isSameDevice to handle meta device to avoid this error:
```
what():  Unsupported device typemeta and meta
Exception raised from isSameDevice at fbcode/caffe2/torch/nativert/executor/PlacementUtils.cpp:20
```

2. Updates xl weight v2 loading logic in Weights.cpp to handle non-TBE xl-weights. Today, we enforce the device is the same for an old weight and new weight when replacing with ModelRunnerAdapter.setAttr(). However, the way we replace non-TBE xl weights is to find any weights on "meta" device and then replace them with their correct weight with real device from xl_weights folder. Therefore, the new weight and old weight will always have different devices and the device check is invalid. I don't think we've run into this so far bc non-TBE xl weights have not been thoroughly tested until now.

Test Plan:
Run MRS you model merge net, which uses non-TBE xl weights. Confirm that before change #1 we get error:
```
Unsupported device typemeta and meta
```
Then after change #1 and before change #2 we get:
```
what():  Mismatched device for merge.user_tower.linear.weight: meta vs cpu
Exception raised from validateValue at fbcode/caffe2/torch/nativert/executor/Weights.cpp:374
```
After change run is successful
Command:
```
MODEL_ENTITY_ID=921242082
SNAPSHOT_ID=1269
module_name=merge
SAMPLE_INPUT_DIR=/data/users/georgiaphillips/models/921242082/${SNAPSHOT_ID}/${module_name}_archive/package/data/sample_inputs
buck2 run mode/dev-nosan -c fbcode.nvcc_arch=h100,a100 -c fbcode.enable_gpu_sections=true caffe2/torch/fb/model_transform/fx2trt/packaging:load_net_predictor -- --loadMode=Benchmark --inputNetFile=/data/users/$USER/models/${MODEL_ENTITY_ID}/${SNAPSHOT_ID}/${MODEL_ENTITY_ID}_${SNAPSHOT_ID}.predictor.${module_name} --moduleName=${module_name} --submodToDevice="merge|cuda0"  --benchmarkEnableProfiling=false --disableStaticRuntime=true --doNotRandomizeSampleInputs=true --benchmarkDontRebatchSamples=true --pytorch_predictor_sigmoid_static_dispatch_enable=false --pytorch_predictor_sigmoid_graph_passes_enable=false --sampleInputFilePath=${SAMPLE_INPUT_DIR}/${module_name}.pt
```

Rollback Plan:

Differential Revision: D80713052

Pull Request resolved: pytorch#162842
Approved by: https://github.com/henryoier