Summary:
I have some test code in there as well, along with a script "test_libtorch" to run it. You'll need to modify `test_libtorch` to point to where you have `pytorch` built. I currently require that `pybind11` is included as a subdirectory of the test, but added it to the `.gitignore` to make this reviewable.
Currently, something like this works:
```cpp
struct Foo {
int x, y;
Foo(): x(2), y(5){}
Foo(int x_, int y_) : x(x_), y(y_) {}
void display() {
cout<<"x: "<<x<<' '<<"y: "<<y<<endl;
}
int64_t add(int64_t z) {
return (x+y)*z;
}
};
static auto test = torch::jit::class_<Foo>("Foo")
.def(torch::jit::init<int64_t, int64_t>())
.def("display", &Foo::display)
.def("add", &Foo::add)
.def("combine", &Foo::combine);
```
with
```py
torch.jit.script
def f(x):
val = torch._C.Foo(5, 3)
val.display()
print(val.add(3))
```
results in
```
x: 5 y: 3
24
```
Current issues:
- [x] The python class created by torchscript doesn't interactly properly with the surrounding code.
```
torch.jit.script
def f(x):
val = torch._C.Foo(5, 3)
return val
```
- [x] Doesn't properly take in non-pointer classes. Can't define this function signature in cpp (We don't want to support this I believe).
```cpp
void combine(Foo x) {
```
- [x] Has some issues with memory for blobs when constructing multiple objects (fix constant propagation pass to not treat capsules as the same object).
```py
torch.jit.script
def f(x):
val = torch._C.Foo(5, 3)
val2 = torch._C.Foo(100, 0)
val.display()
print(val.add(3))
```
- [ ] Can't define multiple constructors (need to define overload string. Currently not possible since we don't support overloaded methods).
- [x] `init` is a little bit different syntax than `pybind`. `.init<...>()` instead of `.def(py::init<>())`
- [x] I couldn't figure out how to add some files into the build so they'd be copied to the `include/` directories, so I symlinked them manually.
- [ ] Currently, the conversion from Python into Torchscript doesn't work.
- [ ] Torchbind also currently requires Python/Pybind dependency. Fixing this would probably involve some kind of macro to bind into Python when possible.
- [ ] We pass back into Python by value, currently. There's no way of passing by reference.
- [x] Currently can only register one method with the same type signature. This is because we create a `static auto opRegistry`, and the function is templated on the type signature.
Somewhat blocked on https://github.com/pytorch/pytorch/pull/21177. We currently use some structures that will be refactored by his PR (namely `return_type_to_ivalue` and `ivalue_to_arg_type`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21098
Differential Revision: D16634872
Pulled By: Chillee
fbshipit-source-id: 1408bb89ea649c27d560df59e2cf9920467fe1de
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23003
torch.quantization.fuse_module and torch.nn._intrinsic convRelu and LinearRelu
Fusion function to combine specific modules: (conv,bn) and (conv,bn,relu).
In all cases, replace modules in place. The first module is replaced with the _intrinsic fused module and the remaining modules are replaced by nn.Identity.
Support both training and eval. For training, the modules are "fused" with a sequential container. This is to allow for further module swaps for quantization aware training.
Also add: torch.nn._intrinsic for convRelu and LinearRelu.
TODO: Add tests for _intrinsic modules.
Conv BN fusion code is based on DsKhudia's implementation
Differential Revision: D16199720
fbshipit-source-id: 95fb9ffe72b361d280313b2ec57de2acd4f9dda2
Summary:
This is a modified version of https://github.com/pytorch/pytorch/pull/14705 since commit structure for that PR is quite messy.
1. Add `IterableDataset`.
3. So we have 2 data loader mods: `Iterable` and `Map`.
1. `Iterable` if the `dataset` is an instance of `IterableDataset`
2. `Map` o.w.
3. Add better support for non-batch loading (i.e., `batch_size=None` and `batch_sampler=None`). This is useful in doing things like bulk loading.
3. Refactor `DataLoaderIter` into two classes, `_SingleProcessDataLoaderIter` and `_MultiProcessingDataLoaderIter`. Rename some methods to be more generic, e.g., `get_batch` -> `get_data`.
4. Add `torch.utils.data.get_worker_info` which returns worker information in a worker proc (e.g., worker id, dataset obj copy, etc.) and can be used in `IterableDataset.__iter__` and `worker_init_fn` to do per-worker configuration.
5. Add `ChainDataset`, which is the analog of `ConcatDataset` for `IterableDataset`.
7. Import torch.utils.data in `torch/__init__.py`
9. data loader examples and documentations
10. Use `get_worker_info` to detect whether we are in a worker process in `default_collate`
Closes https://github.com/pytorch/pytorch/issues/17909, https://github.com/pytorch/pytorch/issues/18096, https://github.com/pytorch/pytorch/issues/19946, and some of https://github.com/pytorch/pytorch/issues/13023
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19228
Reviewed By: bddppq
Differential Revision: D15058152
fbshipit-source-id: 9e081a901a071d7e4502b88054a34b450ab5ddde
Summary:
https://github.com/pytorch/pytorch/pull/17072 breaks `model.to(xla_device)`, because moving `model` to XLA device involves changing its parameters' TensorImpl type, and the current implementation of `nn.Module.to()` doesn't support changing module parameters' TensorImpl type:
```python
# 6dc445e1a8/torch/nn/modules/module.py (L192-L208)
def _apply(self, fn):
...
for param in self._parameters.values():
if param is not None:
# Tensors stored in modules are graph leaves, and we don't
# want to create copy nodes, so we have to unpack the data.
param.data = fn(param.data) # NOTE: this doesn't allow changing `param.data`'s TensorImpl type
if param._grad is not None:
param._grad.data = fn(param._grad.data) # NOTE: this doesn't allow changing `param._grad.data`'s TensorImpl type
...
```
yf225 TODO: fix the description here when we finish the implementation
To fix this problem, we introduce a new API `model.to_()` that always assign new tensors to the parameters (thus supporting changing the parameters to any TensorImpl type), and also bump the version counter of the original parameters correctly so that they are invalidated in any autograd graph they participate in.
We also add warning to the current `model.to()` API to inform users about the upcoming behavior change of `model.to()`: in future releases, it would create and return a new model instead of in-place updating the current model.
This unblocks adding XLA to our CI test suite, which also allows XLA to catch up with other changes in our codebase, notably the c10 dispatcher.
[xla ci]
cc. resistor ailzhang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21613
Differential Revision: D15895387
Pulled By: yf225
fbshipit-source-id: b79f230fb06019122a37fdf0711bf2130a016fe6
Summary:
* `torch.hub.list('pytorch/vision')` - show all available hub models in `pytorch/vision`
* `torch.hub.show('pytorch/vision', 'resnet18')` - show docstring & example for `resnet18` in `pytorch/vision`
* Moved `torch.utils.model_zoo.load_url` to `torch.hub.load_state_dict_from_url` and deprecate `torch.utils.model_zoo`
* We have too many env to control where the cache dir is, it's not very necessary. I actually want to unify `TORCH_HUB_DIR`, `TORCH_HOME` and `TORCH_MODEL_ZOO`, but haven't done it. (more suggestions are welcome!)
* Simplify `pytorch/vision` example in doc, it was used to show how how hub entrypoint can be written so had some confusing unnecessary args.
An example of hub usage is shown below
```
In [1]: import torch
In [2]: torch.hub.list('pytorch/vision', force_reload=True)
Downloading: "https://github.com/pytorch/vision/archive/master.zip" to /private/home/ailzhang/.torch/hub/master.zip
Out[2]: ['resnet18', 'resnet50']
In [3]: torch.hub.show('pytorch/vision', 'resnet18')
Using cache found in /private/home/ailzhang/.torch/hub/vision_master
Resnet18 model
pretrained (bool): a recommended kwargs for all entrypoints
args & kwargs are arguments for the function
In [4]: model = torch.hub.load('pytorch/vision', 'resnet18', pretrained=True)
Using cache found in /private/home/ailzhang/.torch/hub/vision_master
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18758
Differential Revision: D14883651
Pulled By: ailzhang
fbshipit-source-id: 6db6ab708a74121782a9154c44b0e190b23e8309
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598
ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18598 Turn on F401: Unused import warning.**
This was requested by someone at Facebook; this lint is turned
on for Facebook by default. "Sure, why not."
I had to noqa a number of imports in __init__. Hypothetically
we're supposed to use __all__ in this case, but I was too lazy
to fix it. Left for future work.
Be careful! flake8-2 and flake8-3 behave differently with
respect to import resolution for # type: comments. flake8-3 will
report an import unused; flake8-2 will not. For now, I just
noqa'd all these sites.
All the changes were done by hand.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14687478
fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
Summary:
`SobolEngine` is a quasi-random sampler used to sample points evenly between [0,1]. Here we use direction numbers to generate these samples. The maximum supported dimension for the sampler is 1111.
Documentation has been added, tests have been added based on Balandat 's references. The implementation is an optimized / tensor-ized implementation of Balandat 's implementation in Cython as provided in #9332.
This closes#9332 .
cc: soumith Balandat
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10505
Reviewed By: zou3519
Differential Revision: D9330179
Pulled By: ezyang
fbshipit-source-id: 01d5588e765b33b06febe99348f14d1e7fe8e55d
Summary:
They are previously merged to resolve#17051. However, since it was resolved by the upstream, and it was causing some issues like https://github.com/abjer/tsds/issues/8, I think it's time to revert these changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17567
Differential Revision: D14265241
Pulled By: kostmo
fbshipit-source-id: 7fa2b7dd4ebc5148681acb439cf82d983898694e
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:
Rehash of previous attempts. This tries a different approach where we accept the install as specified in cmake (leaving bin/ include/ and lib/ alone), and then try to adjust the rest of the files to this more standard layout.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16414
Differential Revision: D13863635
Pulled By: zdevito
fbshipit-source-id: 23725f5c64d7509bf3ca8f472dcdcad074de9828
Summary:
We have:
- This is an initial stab at creating a type stub `torch/__init__.pyi` .
- This is only tested on Python 3, since that's the only Python version mypy
works on.
- So far, we only aim at doing this for torch functions and torch.Tensor.
- Quite a few methods and functions have to be typed manually. These are
done in `torch/__init__.pyi.in`
For me, PyCharm (the non-paid one) didn't seem to indicate errors in the .pyi when opening and seemed to be able to get the type hint for the few functions I tried, but I don't use PyCharm for my usual PyTorch activities, so I didn't extensively try this out.
An example of a generated PYI is at [this gist](https://gist.github.com/ezyang/bf9b6a5fa8827c52152858169bcb61b1).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12500
Differential Revision: D13695553
Pulled By: ezyang
fbshipit-source-id: 4566c71913ede4e4c23ebc4a72c17151f94e8e21
Summary:
It's the best coding practice to always include dynamically declared module level methods in the "__all__" field. Otherwise, IDEs (such as PyCharm) with referenced module inspectors will complain "Cannot find reference ..." .
This PR adds 'rand' and 'randn' in __init.py__' .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12762
Differential Revision: D10427541
Pulled By: ezyang
fbshipit-source-id: ec0704dfd91e78d7ad098b42cfd4bd1ad0e119df
Summary:
This PR splits the CPU and CUDA fusion compilers, putting them into a new jit/fusers/ directory with jit/fusers/common for common components. In particular:
- A fusion interface is created that allows "fusion handles" to be requested
- The CPU and CUDA fusers implement this interface, with dispatch determined by device
- The fusion compilers, fusion function specializations and resource strings are split
- CPU-specific classes like TempFile and DynamicLibrary are in the CPU fuser
- Common classes likes TensorDesc and the base fusion function class are in jit/fusers/common
- There is still some specialization in jit/fusers/common, but these specializations are small(-ish)
- Updates the build system to remove the dummy interface on Windows and minimize the use of macros
This structure should allow in-flight PRs to easily rebase while providing a clear interface to the fusers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10981
Reviewed By: soumith
Differential Revision: D9701999
Pulled By: apaszke
fbshipit-source-id: 3b6bec7b97e0444b2a93caa38d9b897f2e68c1b3
Summary:
Fixes#9818.
It seems original Python doesn't add `[PYTHONPATH]\Library\bin` into `PATH`. We try to add it before dll loading process.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9920
Differential Revision: D9040825
Pulled By: soumith
fbshipit-source-id: c07fff71b2aea254a396042ab677696f6829aac7
* cache cufft plans
* use an LRU cache
* suffix CuFFTParams members with _
* import print_function for py2
* lint
* fix potential race; add dummy impl for CPU only builds
* cpp formatting; remove nccl makefile change
* Use CUDA hooks instead
* comments and doc
* update the error message
* move LRU cachae to a separate file and native::detail namespace
* update comment
* specify NOTE location in CuFFTPlanCache.h
* update disabled_features.yaml to make amd ci work
* another fix for AMD CI in disabled_features.yaml
* Wrap cufft_plan_cache_* methods in __HIP_PLATFORM_HCC__
* improve the notes
* lint
* revert onnx change
* put back inlining for CUFFT_CHECK
* Update docs for torch.zeros factory method
If this looks good, I'll submit another PR rewriting the other factory
methods in this fashion.
* Address comments
* Better explanation for device default
* Add variable argument back
* s/set/sequence/g
* Remove class from torch.strided
* Split set_default_tensor_type(dtype) into set_default_dtype(dtype).
* Fix flake8.
The difference between this one and set_default_tensor_type is that it only sets scalar type what determines the type + device of a tensor returned from a factory function with defaults is the default tensor type + the current device (if the default tensor type is cuda). This just changes the scalar type of the default tensor type.
We do eventually want to deprecate set_default_tensor_type; it is not clear how to do that in a sensible and backwards compatible way.
This changes type(tensor) to return `torch.Tensor` instead of
`torch.autograd.Variable`.
This requires a few implementation changes:
- torch.Tensor is now a regular Python class instead of a
pseudo-factory like torch.FloatTensor/torch.DoubleTensor
- torch.autograd.Variable is just a shell with a __new__ function.
Since no instanes are constructed it doesn't have any methods.
- Adds torch.get_default_dtype() since torch.Tensor.dtype returns
<attribute 'dtype' of 'torch._C._TensorBase' objects>
previously, it was being implicitly imported via the import of
torch.onnx
this is no longer the case, and is a hacky thing to depend on anyway,
so import it explicitly
* Improvize documentation
1. Add formula for erf, erfinv
2. Make exp, expm1 similar to log, log1p
3. Symbol change in ge, le, ne, isnan
* Fix minor nit in the docstring
* More doc improvements
1. Added some formulae
2. Complete scanning till "Other Operations" in Tensor docs
* Add more changes
1. Modify all torch.Tensor wherever required
* Fix Conv docs
1. Fix minor nits in the references for LAPACK routines
* Improve Pooling docs
1. Fix lint error
* Improve docs for RNN, Normalization and Padding
1. Fix flake8 error for pooling
* Final fixes for torch.nn.* docs.
1. Improve Loss Function documentation
2. Improve Vision Layers documentation
* Fix lint error
* Improve docstrings in torch.nn.init
* Fix lint error
* Fix minor error in torch.nn.init.sparse
* Fix Activation and Utils Docs
1. Fix Math Errors
2. Add explicit clean to Makefile in docs to prevent running graph generation script
while cleaning
3. Fix utils docs
* Make PYCMD a Makefile argument, clear up prints in the build_activation_images.py
* Fix batch norm doc error
* Add dtype to torch.Tensor, torch.FloatTensor, etc.
* Support passing dtypes to set_default_tensor_type.
* Check dtype exception.
* Correctly handle new type initialization order.
* Move handling of torch.Storage alias to C++.
* Delete function that erroneously reappeared.
This deletes most of the dead Tensor code paths, including the TensorMethods cwrap and generic/Tensor.cpp.
This also moves the THNN.cwrap/.cpp generation to generate_code which can use ninja if installed.
This replaces the torch.Tensor constructors with factories that produce
Variables. Similarly, functions on the torch module (e.g. torch.randn)
now return Variables.
To keep the PR to a reasonable size, I've left most of the unused tensor
code. Subsequent PRs will remove the dead code, clean-up calls to
torch.autograd.Variable, and rename Variable to Tensor everywhere.
There are some breaking changes because Variable and Tensors had
slightly different semantics. There's a list of those changes here:
https://github.com/pytorch/pytorch/wiki/Breaking-Changes-from-Variable-and-Tensor-merge
* Add numpy-style dtypes to Variable factories.
1) Add numpy-style dtypes corresponding to torch tensor types. These are:
torch.float16, torch.float32, torch.float64, torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64
as well as torch.cuda, torch.sparse, and torch.cuda.sparse equivalents.
2) Adds "legacy" names for the above dtypes that correspond more closely to existing tensor names. These are:
torch.half, torch.float, torch.double, torch.short, torch.int, torch.long.
torch.byte and torch.char don't exist because they either don't match numpy semantics or differ on different architectures.
3) Adds a "dtype" parameter to Variable factories (e.g. zeros, ones) that allows the user to specify the type without changing the default tensor type.
4) Adds a "dtype" getter to Variables that return the canonical dtype from 1)
This PR is missing the following useful features that should be added in the future:
A) We only add the "dtype" parameter to auto-generated factories; hand-written factories like in tensor_new.cpp don't support this yet.
B) We don't allow type conversions to use dtypes; that should be added to type(param) or a new function.
C) We don't yet have a "device" parameter for these factories; right now, they will only create Variables on the default device.
* backend_to_string can be private.
* Define python binding argument indexes in a more simple way.
* add all_declared_types, still need to hook it up to THPDType.
* Fix all_declared_types for missing types (it's Sparse + Half).
* Ensure cuda dtypes are created even if compiled with NO_CUDA=1.
* Fix case where dtype is provided but dispatch is via namespace.
This happens in ones_like, empty_like, randn_like.
There is some question if we should do:
1) at::ones_like(tensor).toType(dtype)
2) at::ones_like(tensor.toType(dtype))
I did the former because this matches with the numpy documentation, i.e.:
"Overrides the data type of the result." and it's easier to implement.
Note that the above causes an extra copy, either of the input or output.
Here's a better implementation:
1) Make zeros_like, ones_like native functions that take an optional type (named dtype?).
2) Match the type argument with the dtype, so we don't have two different parameters.
3) Call at::zeros_like(input, type) -> at::native::zeros_like(input, type) -> type.zeros(input.sizes())
* Don't return from maybe_initialize_cuda.
* Don't leak DType name.
* Address cpp review comments.
* Share code between sparse and non-sparse test_dtypes.
* Rewrite _like functions as native function with explicit type parameter.
* Use type 'Type' instead of 'dtype' for consistency.
* Address review comments.
* Handle arg_idx when there is requires_grad but no dtype in python_binding_arguments.
* Various testing and utility improvements including torch.testing module.
1) Remove method definition for randn_like since ones_like, zeros_like do not have methods.
2) Add an empty_like native function for creating a tensor with uninitialized values.
3) Add an is_floating_point() native function, similar to is_signed().
4) Add a torch.testing module loosely modeled after numpy.testing; currently it contains
make_non_contiguous (moved from test_autograd) and randn_like (wrapper around the VariableFunction).
5) Remove code from test_autograd and test_nn that is responsible for generating grad_outputs to use
with gradgradcheck. These now use gradgradcheck's own generating code. This fixes
test_nn.py with scalars because gradgradcheck does the right thing here already.
* Rename parameter.
* Fix parameter usages.
This adds overrides in VariableType for the xxx_out ATen functions and
implements Python bindings. There is no support for automatic
differentiation. If any of the inputs (or outputs) requires grad, then the
function will throw an exception unless it's running in "no-grad" mode.
The bindings for calling torch.xxx functions on Variables are moved to a
different object. Previously, they were static method on VariableBase.
This change prevents users from accidentally calling static methods as if
they were instance methods.
This removes volatile from Variable. The functionality is mostly
replaced by a global (thread-local) flag, which is controlled by
torch.set_grad_enabled() and the context manager torch.no_grad().
In C++, the flag is exposed through GradMode::is_enabled() and GradMode::set_enabled()
Fixes#3627
