* Don't override Tensor, Storage macros defined outside torch/csrc in torch/csrc.
This PR does the following:
1) Removes THSTensor macros in torch/csrc, which aren't used.
2) For macros defined outside of torch/csrc (THTensor, THTensor_, THStorage, THStorage_):
a) No longer override them, i.e. previously THTensor could actually be THCTensor if a generic file was included from a file including THCP.h.
b) Instead, introduce new macros THW* (e.g. THWTensor) to represent a (potentially empty) wildcard character.
In addition to making this code easier to read and codemod, this allows us to more freely change TH/THC; for example:
currently in the THC random code, the state is casted to THByteTensor*; this happens to work because the macros don't happen to override THByteTensor.
But if THByteTensor just becomes an alias of THTensor (which is the plan for a single tensor type), then this no longer works.
The whole thing is a bit of a mess previously because you really have to understand which macros and redefined and which aren't.
We could also rename the macros that live in torch/csrc (e.g. the THPTensor macros), but since that is more self contained, I punted for now.
* Don't change the plugin.
* Make THStorage / THCStorage have void* data ptr.
This is the initial step in unifying the ATen and TH tensor representations, next is to only generate a single THStorage / THCStorage type.
The major changes here are:
1) data has been renamed to data_ptr and made void* in THStorage/THCStorage.
2) THStorage / THCStorage stores a at::ScalarType representing its data type (This will be useful when we generate a single THStorage/THCStorage).
3) APIs for Accessing the data as a real*:
a) storage->data<real>() -- this does runtime-type checking (checks that the at::ScalarType is correct).
b) storage->unsafeData<real>() -- as above, but no runtime-type checking (used in inner loops / fast code paths).
c) THStorage_(data)(storage) -- this already existed, just calls storage->data<real>().
* Add include.
* Attempt to fix clang build issues.
* Clarify comment and remove extra character.
* Rename unsafeData -> unsafe_data.
* Remove unnecessary 'to' function to get compile time rather than link time errors.
CUDA IPC only works with Python 3 using the "spawn" start method. You
can select the start method using the get_context method:
import torch.multiprocessing as mp
ctx = mp.get_context('spawn')
queue = ctx.Queue()
event = ctx.Event()
The from_buffer is similar to numpy's frombuffer. It decodes a Python
buffer object into a Storage object. For byte and char storages, it
simply copies the bytes.
