Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46036
Previously, this function didn't do error-bounds checking on the GetItem (GET_ITEM) calls, which led to issues like https://github.com/pytorch/pytorch/issues/46020.
A better solution would be to use pybind, but given writing the file is going to dominate bounds checking, this is strictly better.
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D24228370
Pulled By: gchanan
fbshipit-source-id: f5d0a3d21ff12b4380beefe1e9954fa81ea2f567
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37776
* Remove type-specific size tracking in favor of byte size tracking in Storage and StorageImpl
* Changed numel() and set_numel() to nbytes() and set_nbytes()
* Added enum argument to Storage/StorageImpl constructor to indicate new meaning of the size parameter
* Update all callers of the changed API
Part of issue https://github.com/pytorch/pytorch/issues/33950
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37028
Differential Revision: D21171334
Pulled By: ezyang
fbshipit-source-id: 37329a379de9a3a83cc5e9007e455a3e1c2d10b8
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:
Stacked PRs
* #32958 - Make zip serialization the default
* **#32244 - Fix some bugs with zipfile serialization**
It includes the following changes:
* Split up tests so that we can test both serialization methods
* Loading something within a buffer doesn't work anymore, so those tests are only on the old serialization method (it's possible but introduces a big slowdown since it requires a linear scan of the entire zipfile to find the magic number at the end)
* Call `readinto` on a buffer if possible instead of `read` + a copy
* Disable CRC-32 checks on read (there was some issue where miniz said the CRC was wrong but `zipinfo` and `unzip` said the zip file was fine)
](https://our.intern.facebook.com/intern/diff/19418935/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32244
Pulled By: driazati
Reviewed By: eellison
Differential Revision: D19418935
fbshipit-source-id: df140854f52ecd04236225417d625374fd99f573
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25527
Master GH issue: https://github.com/pytorch/pytorch/issues/23110.
This change builds upon https://github.com/pytorch/pytorch/pull/24876 and
provides all the autograd hooks needed for a forward pass with distributed rpc
for builtin operators. This change does not address distributed rpc for python
UDFs and that will be addressed in follow up PRs.
Summary of changes:
1. Attach send autograd functions when a request is sent from the client and
response is sent from the server.
2. Attach receive autograd functions when a request is received on the server
and a response is received on the client.
3. Generate a globally unique autograd_message_id for each send/recv autograd
function pair to uniquely identify them.
ghstack-source-id: 91240466
Test Plan: unit tests.
Differential Revision: D17148077
fbshipit-source-id: 192d8a3f552ed7cc939f55dcca332965c9bd3233
Summary:
Fixes ~5000 lines of warnings like:
```
In file included from ../aten/src/TH/TH.h:4,
from ../torch/csrc/Storage.cpp:11:
../torch/csrc/Storage.h:6:39: warning: cast between incompatible function types from ‘PyObject* (*)(THPStorage*)’ {aka ‘_object* (*)(THPStorage*)’} to ‘getter’ {aka ‘_object* (*)(_object*, void*)’} [-Wcast-function-type]
6 | #define THPStorage_(NAME) TH_CONCAT_4(THP,Real,Storage_,NAME)
| ^~~
caffe2/aten/src/TH/THGeneral.h:154:37: note: in definition of macro ‘TH_CONCAT_4_EXPAND’
154 | #define TH_CONCAT_4_EXPAND(x,y,z,w) x ## y ## z ## w
| ^
../torch/csrc/Storage.h:6:27: note: in expansion of macro ‘TH_CONCAT_4’
6 | #define THPStorage_(NAME) TH_CONCAT_4(THP,Real,Storage_,NAME)
| ^~~~~~~~~~~
../torch/csrc/generic/Storage.cpp:299:22: note: in expansion of macro ‘THPStorage_’
299 | {"device", (getter)THPStorage_(device), nullptr, nullptr, nullptr},
| ^~~~~~~~~~~
../torch/csrc/Storage.h:6:39: warning: cast between incompatible function types from ‘PyObject* (*)(THPStorage*)’ {aka ‘_object* (*)(THPStorage*)’} to ‘getter’ {aka ‘_object* (*)(_object*, void*)’} [-Wcast-function-type]
6 | #define THPStorage_(NAME) TH_CONCAT_4(THP,Real,Storage_,NAME)
| ^~~
caffe2/aten/src/TH/THGeneral.h:154:37: note: in definition of macro ‘TH_CONCAT_4_EXPAND’
154 | #define TH_CONCAT_4_EXPAND(x,y,z,w) x ## y ## z ## w
| ^
../torch/csrc/Storage.h:6:27: note: in expansion of macro ‘TH_CONCAT_4’
6 | #define THPStorage_(NAME) TH_CONCAT_4(THP,Real,Storage_,NAME)
| ^~~~~~~~~~~
```
This issue and the fix is very similar to how CPython fixed it, see https://bugs.python.org/issue33012.
There's still more of these warnings left, but this fixes the majority of them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25483
Differential Revision: D17149824
Pulled By: ezyang
fbshipit-source-id: 353560a4f76070fa7482608e9532b60205d16798
Summary:
Fixed an issue where models can not be loaded in a 32-bit environment like Raspbian.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20900
Differential Revision: D15696709
Pulled By: ezyang
fbshipit-source-id: 37a81f05f235d3b9fc6244e12d3320ced3d1465e
Summary:
This addresses #18436
The logic replicates the essence of closing file descriptors in numpy:
bf20e30340/numpy/core/include/numpy/npy_3kcompat.h (L278)
This stores the position of the file descriptor before resetting it to the Python handle offset, then resets to the original position before exit. The Python-side handle is then updated to reflect the new position. Also added somewhat more demanding tests to cover this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20270
Differential Revision: D15275902
Pulled By: soumith
fbshipit-source-id: 5ca8a52b61c7718d2e69571f72f80b1350b0acdb
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:
When compiling for `TORCH_CUDA_ARCH_LIST=7.5` we were getting ptxas warnings (https://github.com/pytorch/pytorch/issues/14310). This was because we had some hardcoded values when using launch_bounds in kernels. The maximum number of threads per multiprocessor is 1024 for Turing architecture (7.5) but 2048 for previous architectures. The hardcoded launch_bounds in the kernel were requesting for 2048 threads when compiling for Turing and hence were generating the warning.
This PR adds a macro that checks for the bounds on the launch bounds value supplied. The max number of threads per block across all architectures is 1024. If a user supplies more than 1024, I just clamp it down to 512. Depending on this value, I set the minimum number of blocks per sm. This PR should resolve https://github.com/pytorch/pytorch/issues/14310. The gradient computation being wrong reported in that PR is probably due to the faulty card.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15461
Differential Revision: D13633952
Pulled By: soumith
fbshipit-source-id: 795aa151109f343ab5433bf3cb070cb6ec896fff
Summary:
This is necessary to allow us to use the complex header
which defines real (and is very sad if real is macro'ed).
We should also fix accreal, ureal, Real and REAL, but
only 'real' is the real blocker.
```
codemod -d aten/src/TH --extensions c,cc,cpp,cu,cuh,h,TARGETS,py,hpp '\breal\b' scalar_t
codemod -d aten/src/THC --extensions c,cc,cpp,cu,cuh,h,TARGETS,py,hpp '\breal\b' scalar_t
codemod -d aten/src/THNN --extensions c,cc,cpp,cu,cuh,h,TARGETS,py,hpp '\breal\b' scalar_t
codemod -d aten/src/THCUNN --extensions c,cc,cpp,cu,cuh,h,TARGETS,py,hpp '\breal\b' scalar_t
```
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11163
Reviewed By: SsnL
Differential Revision: D9619906
Pulled By: ezyang
fbshipit-source-id: 922cb3a763c0bffecbd81200c1cefc6b8ea70942
Summary:
How did we get so many uses of `NULL` again?
ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11047
Differential Revision: D9566799
Pulled By: goldsborough
fbshipit-source-id: 83469f352ac69aa65bdaf1a1a21f922d892e0db3
Summary:
Storage views were previously used to implement CUDA IPC sharing,
but they weren't necessary. The new strategy is described in
Note [CUDA IPC and the caching allocator].
This also fixes an unrelated bug, where we weren't actually using
the Tensor forking pickler, because we didn't register a pickler
for torch.Tensor.
Fixes#9447. Fixes#46.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
CC apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9466
Reviewed By: apaszke
Differential Revision: D8859698
Pulled By: ezyang
fbshipit-source-id: 3362cb92f6ae4aa37084c57d79b31004bd0b4a97
* 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()
