Fixes #ISSUE_NUMBER
For the scenario where users inherit storageimpl to implement their own subclasses, the current storage creation method cannot correctly create storage objects.
Refer to the registration method of Allocator to expand the creation method of storageimpl, users can register their own custom storageimpl creation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100237
Approved by: https://github.com/albanD
### Description
Since the major changes for `_TypedStorage` and `_UntypedStorage` are now complete, they can be renamed to be public.
`TypedStorage._untyped()` is renamed to `TypedStorage.untyped()`.
Documentation for storages is improved as well.
### Issue
Fixes#82436
### Testing
N/A
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82438
Approved by: https://github.com/ezyang
This reverts commit 64c2a275c4d463b936b9469da948a666e016bbb8.
Reverted https://github.com/pytorch/pytorch/pull/79465 on behalf of https://github.com/zengk95 due to this broke X linux-xenial-py3.7-clang7-onnx / test (default, 1, 2, linux.2xlarge). Not sure why since it passed on pull.
Summary:
Move TH<C>GenerateByteType includes into torch/csrc (the only place they are used), and we can remove TH folder altogether!
The only thing left in THC are includes left for bc compatibility.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69929
Reviewed By: mruberry
Differential Revision: D33133013
Pulled By: ngimel
fbshipit-source-id: 78c87cf93d2d641631b0f71051ace318bf4ec3c1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69041
`TH_CONCAT_{N}` is still being used by THP so I've moved that into
it's own header but all the compiled code is gone.
Test Plan: Imported from OSS
Reviewed By: anjali411
Differential Revision: D32872477
Pulled By: ngimel
fbshipit-source-id: 06c82d8f96dbcee0715be407c61dfc7d7e8be47a
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
Summary:
This PR aims to reduce the import overhead and symbol noises from the `windows.h` headers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48009
Reviewed By: gchanan
Differential Revision: D25045840
Pulled By: ezyang
fbshipit-source-id: 01fda70f433ba2dd0cd2d7cd676ab6ffe9d98b90
Summary:
Following up on this: https://github.com/pytorch/pytorch/pull/35851 cross dtype storage copy is not being used internally, so I have not included cross dtype copy for complex.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35771
Differential Revision: D21319650
Pulled By: anjali411
fbshipit-source-id: 07c72996ee598eba0cf401ad61534494d6f5b5b3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18749
ghimport-source-id: 9026a037f5e11cdb9ccd386f4b6b5768b9c3259b
Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18751 Disallow changing the device of a tensor via set_.
* #18750 Use non-legacy constructors for tensor deserialization.
* **#18749 Add device and dtype to storage.**
The goal here is to fix our serialization, which currently depends on the legacy constructors. Having dtype and device on Storage allows us to use the non-legacy constructors.
This fits somewhat along our goal of removing Storage, my having Storage act like a Tensor.
Differential Revision: D14729516
fbshipit-source-id: bf4a3e8669ad4859931f4a3fa56df605cbc08dcb
Summary:
This is to fix#16141 and similar issues.
The idea is to track a reference to every shared CUDA Storage and deallocate memory only after a consumer process deallocates received Storage.
ezyang Done with cleanup. Same (insignificantly better) performance as in file-per-share solution, but handles millions of shared tensors easily. Note [ ] documentation in progress.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16854
Differential Revision: D13994490
Pulled By: VitalyFedyunin
fbshipit-source-id: 565148ec3ac4fafb32d37fde0486b325bed6fbd1
Summary:
This is the first commit from a series of planned changes in order to add boolean tensors to PyTorch. The whole plan looks like this:
0. Storage Implementation (this change)
1. Tensor Creation.
2. Tensor Conversions.
3. Tensor Indexing.
4. Tensor Operations.
5. Back compatibility related changes.
This feature was requested by the community:
https://github.com/pytorch/pytorch/issues/4764https://github.com/pytorch/pytorch/issues/4219https://github.com/pytorch/pytorch/issues/4288
**Change**:
Added boolean type to the Storage class for CPU and CUDA backends.
**Tested via**:
1. unit tests
2. running this:
-> import torch
-> torch.BoolStorage
<class 'torch.BoolStorage'>
-> torch.cuda.BoolStorage
<class 'torch.cuda.BoolStorage'>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16810
Reviewed By: gchanan
Differential Revision: D14087246
Pulled By: izdeby
fbshipit-source-id: 042642ced1cb0fd1bb6bff05f9ca871a5c54ee5e
Summary:
We've virtualized the destructor for storage, so we
no longer have to forward to a particular backend.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14897
Differential Revision: D13399216
Pulled By: ezyang
fbshipit-source-id: 531d29c3f278477cfa8759f30ab4f304d695b659
Summary:
Anywhere we used #include "foo.h", we now say #include <foo.h>
Paths are adjusted to be rooted out of aten/src, torch/lib, or
the root level directory.
I modified CMakeLists.txt by hand to remove TH and THC from
the include paths.
I used the following script to do the canonicalization:
```
import subprocess
import re
import os.path
files = subprocess.check_output(['git', 'ls-files']).decode('utf-8').rstrip().split('\n')
for fn in files:
if not any(fn.endswith(suff) for suff in ['.cu', '.cpp', '.in', '.h', '.hpp', '.cu', '.cuh', '.cc']):
continue
if not any(fn.startswith(pref) for pref in ["aten/", "torch/"]):
continue
with open(fn, 'r') as f:
c = f.read()
def fmt(p):
return "#include <{}>".format(p)
def repl(m):
p = m.group(1)
if p in ["dlfcn.h", "unistd.h", "nvrtc.h", "cuda.h", "cuda_runtime.h", "cstdint", "cudnn.h", "Python.h", "cusparse.h", "cuda_runtime_api.h", "cuda_fp16.h", "cublas_v2.h", "stdint.h", "curand_kernel.h"]:
return fmt(p)
if any(p.startswith(pref) for pref in ["torch/csrc", "c10/", "ATen/", "caffe2/", "TH/", "THC/", "Eigen/", "gtest/", "zdl/", "gloo/", "onnx/", "miopen/"]):
return fmt(p)
for root in ["aten/src", "torch/lib", ""]:
for bad_root in [os.path.dirname(fn), "aten/src/TH", "aten/src/THC", "torch/csrc"]:
new_p = os.path.relpath(os.path.join(bad_root, p), root)
if not new_p.startswith("../") and (os.path.exists(os.path.join(root, new_p)) or os.path.exists(os.path.join(root, new_p + ".in"))):
return fmt(new_p)
print("ERROR: ", fn, p)
return m.group(0)
new_c = re.sub(r'#include "([^"]+)"', repl, c)
if new_c != c:
print(fn)
with open(fn, 'w') as f:
f.write(new_c)
```
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14849
Reviewed By: dzhulgakov
Differential Revision: D13363445
Pulled By: ezyang
fbshipit-source-id: 52361f878a672785f9306c9e9ab2513128092b68
Summary:
```
This removes PyObjectFinalizer. We were seeing SIGSEGV at exit in some
programs that use multiprocessing. The backtrace pointed to
StorageRef.__del__ being called from subtype_dealloc. My guess is that
the Python interpreter was shutdown before all C++ Storage objects were
deallocated. Deallocating the C++ Storage called the finalizer which
called back into Python after it was no longer safe to do so.
This avoids a callback from C++ into Python during Storage finalization.
Instead, dead Storage objects (expired weak references) are collected
periodically when shared_cache exceeds a limit. The limit is scaled with
2x the number of live references, which places an upper bound on the
amount of extra memory held by dead Storage objects. In practice, this
should be very small.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10407
Differential Revision: D9272400
Pulled By: colesbury
fbshipit-source-id: ecb14d9c6d54ffc91e134c34a4e770a4d09048a2
Summary:
See Note [Supervisor deleter] for how SupervisedPtr works.
This design is not the obvious one, but there were a lot of
constraints feeding into it:
- It must support the reallocation usage-pattern, where, given
an existing Storage, we allocate a new region of memory,
copy the existing data to it, and then deallocate the old
region of memory.
- Creation of a deleter for memory MUST avoid dynamic allocations
in the common case. We've done some benchmarking in Caffe2
where dynamic allocation for deleters is ruinously expensive,
and it's really hard to avoid these performance tarpits in
very general function wrappers like std::function or
folly::Function (while benchmarking this, we discovered that
folly::Function's move constructor was way more expensive
than it should be).
- We need to be able to deallocate data that comes from external
sources, e.g., dlpack and numpy tensors. Most notably,
you often cannot deallocate these with merely the void*
data pointer; you need some extra, out-of-band information
(e.g., the managing struct) to deallocate it. Sometimes,
you may even want to resize data living in an external source!
- The "core" allocators need to support being wrapped in a Thrust
allocator, so you need to be implement the following two functions:
char* allocate(size_t);
void deallocate(char*, size_t);
- We need to support tensors which contain non-POD, non-trivially
copyable data; specifically tensors of std::string. This is
an upcoming requirement from Caffe2. It's dirty AF, but
it's really useful.
- It should use C++ standard library types like std::unique_ptr
(which is hugely problematic because std::unique_ptr doesn't
call the deleter when the pointer is null.)
Here is the billing of changes:
- Built-in support for realloc() has been DROPPED ENTIRELY.
Instead, you're expected to allocate and then copy from
the old memory to the new memory if you want to do a
reallocation. This is what you'd generally have expected
to occur; and axing realloc() from the design lets us avoid
some tricky correctness issues with std::realloc(), namely
the fact that we must refuse the realloc if the type of the
elements are not trivially copyeable. If it really matters,
we can add this back, but there really needs to be a good
explanation WHY you need fast resizing reallocations (by in
large, people don't resize their storages, and it should
be acceptable to have a performance degradation when they
do).
- TH_STORAGE_FREEMEM is no more; instead, if you want a
storage which doesn't free its result, you just give it
an empty deleter.
- What we used to call an "allocator" (really, a combined
object for allocating/deleting) has been split into two
concepts, an allocator, and a smart pointer (SupervisedPtr)
which knows how to delete data.
- Unlike previously, where THAllocator/THCDeviceAllocator
could have a per-tensor context storing extra information
(e.g., a pointer to the metadata you need to actually
free the tensor), there is no context in the allocator or
the deleter of the smart pointer; instead, the smart
pointer directly holds an owning reference to the
metadata necessary to free the data. This metadata
is *freshly manufactured* upon every allocation, which
permits us to resize tensors even in the absence of
built-in support for realloc().
- By default, allocators don't support "raw" allocations
and deallocations with raw pointers. This is because
some allocations may return a different context every
time, in which case you need to reconstruct the context
at delete time (because all you got was a void*, not
a unique_ptr that carries the deleter).
- The diff between at::Allocator and THCDeviceAllocator is a
bit larger:
- It used to return a cudaError_t. Now, allocators
are expected to check the error status immediately and throw
an exception if there was an error. It turns out that this
is what was immediately done after all occurrences of
allocate/release, so it wasn't a big deal (although some
subsidiary interfaces had to themselves be converted to
not return cudaError_t).
There is one notable exception to this, and it is how
we handle CUDA OOM: if this occurs, we attempt to return
unused memory to the system and try again. This is now
handled by a catch-all try-catch block. The cost of
catching the exception is probably the least of your worries
if you're about to OOM.
- It used to take the CUDA stream to perform the allocation
on as an argument. However, it turned out that all call
sites, this stream was the stream for the current device.
So we can push this into the allocator (and the choice,
in the future, could be made explicitly by twiddling
thread local state.)
- It held two extra methods, emptyCache and cacheInfo, specifically
for interacting with some state in THCCachingAllocator.
But this "generality" was a lie, since THCCachingAllocator
was the only allocator that actually implemented these
methods, and there is actually a bunch of code in THC
which assumes that it is the caching allocator that is
the underlying allocator for CUDA allocations. So I
folded these two methods into this interface as
THCCachingAllocator_emptyCache and THCCachingAllocator_cacheInfo.
- It held its context directly inside the THCDeviceAllocator
struct. This context has been moved out into whatever
is holding the at::Allocator*.
- The APIs for getting at allocators/deleters is now a little different.
- Previously there were a bunch of static variables you could get
the address of (e.g., &THDefaultAllocator); now there is a
function getTHDefaultAllocator().
- Some "allocators" didn't actually know how to allocate (e.g.,
the IPC "allocator"). These have been deleted; instead, you
can wrap the produced pointers into SupervisedPtr using
an appropriate makeSupervisedPtr() static method.
- Storage sharing was a lot of work to wrangle, but I think I've
tamed the beast.
- THMapAllocator and its "subclasses" have been refactored to
be proper, honest to goodness C++ classes. I used the enum
argument trick to get "named" constructors. We use inheritance
to add refcounting and management (in libshm). What we previously
called the "Context" class (Context has been dropped from the name)
is now the supervisor for the data.
- Sometimes, we need to pull out the file descriptor from a
tensor. Previously, it was pulled out of the allocator context.
Now, we pull it out of the supervisor of the SupervisorPtr,
using the static method fromSupervisedPtr(), which uses the
deleter as the typeid, and refines the type if it matches.
- I renamed the std::function deleter into
InefficientStdFunctionSupervisor, to emphasize the fact that it does
a dynamic allocation to save the std::function deleter.
TODO:
- Windows libshm is in shambles and needs to be fixed.
Perhaps for the future:
- newFromFd is now unconditionally calling cudaPointerGetAttributes
even though this is unnecessary, because we know what the device
is from higher up in the callstack. We can fix this by making
newWithDataAndAllocator also take an explicit device argument.
- Consider statically distinguishing between allocators that
support raw_allocate/raw_deallocate, and those which don't.
The Thrust constraint applies only to the CUDA device allocator;
you never need to allocate CPU memory this way
- Really want to get rid of storage views. Ugh.
Nontrivial bugs I noticed when preparing this patch:
- I forgot to placement-new unique pointers and attempted to
assign them directly on uninitialized memory; very bad! Sam
Gross has encouraged me to replace this with a proper constructor
but I keep putting it off, because once everything goes in
StorageImpl there really will be a proper constructor.
- I rewrote a number of APIs to use newWithDataAndAllocator
instead of newWithAllocator, calling the allocator at the
call site (because they required "allocation context" which
we no longer give to "allocators"). When I did this, I forgot
to insert the multiplication with sizeof(real) to scale from
numels to number of bytes.
- The implementation of swap on storages was missing it for
scalarType and backend. It was benign (because the only case
we call swap is when these are the same), but I fixed it anyway.
- I accidentally returned a nullptr unique_ptr with no deleter,
even though there was a legitimate one. This matters, because
some code still shoves its hands in the deleter context to
get extra metadata about the function.
- I used std::move() on a unique_ptr, and then did a boolean
test on the pointer aftewards (always false!)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9358
Reviewed By: SsnL
Differential Revision: D8811822
Pulled By: ezyang
fbshipit-source-id: 4befe2d12c3e7fd62bad819ff52b054a9bf47c75
