Summary:
* Deletes all weak script decorators / associated data structures / methods
* In order to keep supporting the standard library in script, this enables recursive script on any function defined in `torch.nn`
* Most changes in `torch/nn` are the result of `ag -Q "weak" torch/nn/ -l | xargs sed -i '/weak/d'`, only `rnn.py` needed manual editing to use the `ignore` and `export` to continue supporting the overloaded `forward` methods
* `Sequential`/`ModuleList` no longer need to be added to constants since they are compiled on demand
This should also fix https://github.com/pytorch/pytorch/issues/22212
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22212
Differential Revision: D15988346
Pulled By: driazati
fbshipit-source-id: af223e3ad0580be895377312949997a70e988e4f
Summary:
Changelog:
- Append a condition that switches to the native CUDA implementation for affine_grid
Fixes#16365
Differential Revision: D13832192
Pulled By: soumith
fbshipit-source-id: 3f484e6673d71e3ba7627b170cb8f1611e12b9b2
Summary:
Port AffineGrid to C++, because script does not support compiling Function classes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14392
Differential Revision: D13219698
Pulled By: eellison
fbshipit-source-id: 3ddad8a84c72010b5a6c6f7f9712be614202faa6
Summary:
I've implemented affine grid generation for volumetric (5d) inputs. The implementation is based off of the spatial implementation, extended by one dimension. I have a few questions about my implementation vs. the existing one that I will add inline.
I have some extensive test cases for the forward pass here: https://gist.github.com/elistevens/6e3bfb20d8d0652b83bd16b3e911285b However, they use `pytest.fixture` extensively, so I'm not sure the best way to incorporate them into the pytorch test suite. Suggestions? I have not tested backwards at all.
Diff probably best viewed with whitespace changes ignored.
Thanks for considering!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/8322
Differential Revision: D9332335
Pulled By: SsnL
fbshipit-source-id: 1b3a91d078ef41a6d0a800514e49298fd817e4df
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.
* Comprehensive rewrite of Torch CuDNN bindings / a bit of ATen infra
The executive summary is that this moves the torch/csrc/cudnn
library into ATen, adding a number of new cudnn_ methods to ATen
for batchnorm, convolution, affine grid generator and grid sampler.
ATen infra changes:
- TensorGeometry was moved to ATen
- TensorGeometry was modified to make its interface resemble that of
Tensor; in particular, sizes is no longer a field, it's a method.
- AT_CUDA_ENABLED macro is set via ATen/Config.h header which is
generated at cmake configure time.
Fixes https://github.com/zdevito/ATen/issues/168
- Change AT_CUDA_ENABLED macro to be a function macro, so that we
error if it is not defined
- Introduce a new TensorArg class, which is a Tensor plus a little
metadata. This helps us give good error messages when checking
dimensions/shapes of tensors.
Fixes https://github.com/zdevito/ATen/issues/169
- Also introduce a TensorGeometryArg class, for when you don't
need the actual tensor data (which is most of the time.)
- Add ATen/Check.h, which contains a number of utility functions
for testing shapes, types and devices of input tensors. This
will be particulary useful for native methods, which don't get
code generated input testing code. These functions take a
'CheckedFrom' argument, at the moment just a string, which
specifies some extra information about what function was
doing the actual checking; this greatly improves error messages.
- Many check functions take initializer lists, which let you
test that all tensors have some property. This API is
peculiar, in that we IGNORE undefined tensors in this case.
This is handled by filterDefined.
- Add AT_CUDNN_ENABLED macro
- CuDNN linking from ATen was improved; for example, we now actually
add the CuDNN headers to our include path.
- Add some missing override specifiers to some methods
- We now actually build tests with CUDA functionality accessible
(previously, AT_CUDA_ENABLED was not defined, meaning that
the headers were missing all CUDA-only functionality.)
- Native functions now support giving explicit names to return
outputs in yaml. This makes it possible to hook into the NN
autogenerated derivatives codepath using native functions.
CuDNN rewrite changes:
- torch/csrc/cudnn now uses ATen (rather than passing around
THVoidTensor) and lives in ATen. This lets us remove tensorPointer
shenanigans. The functions are exposed to ATen as native functions
described in aten/src/ATen/cudnn/cuDNN.yaml
- ATen now builds and links against CuDNN when enabled. The cmake
package script was taken from Caffe2.
- Some header reorganization was done to help reduce dependencies
on headers (this reorg is no longer used but I've kept it)
- Rename CHECK to CUDNN_CHECK
- Rip out old shape/type testing code in favor of modern ATen/Check.h
interface using TensorArg. In many cases, increase the robustness of
the checking code.
- Change the inputs of the public facing functions, so that they can
be bound by ATen
- Delete THCState*; this is retrieved from the global ATen context
- Delete cudnnHandle_t, this is retrieved from the global Handles.h
- Delete cudnnDataType_t, this is retrieved from the Tensor type
- Delete Convolution class, instead its constituent arguments are
passed individually
- Change functions to return tensors, rather than take an appropriately
sized output tensor as an input.
- Redo how transposed convolution / backward convolution is implemented
(knock on effect of returning tensors). Previously it was assumed
that you would always pass an appropriately sized output tensor, but
we don't want to do this anymore. For backwards, we instead give
the desired output tensor (input, really) size, because that is
readily available. For *transposed* convolution, however, we take
output_padding, and otherwise do the shape calculation.
- Redo how legacy group convolution is implemented (knock on effect from
porting cudnn to ATen.) Previously, group convolution was implemented
by manually constructing sizes and strides and then outputting
appropriate, with macros switching between individual groups and
all-at-once based on CuDNN version. Now, the code looks exactly what
you'd expect: there's a top-level wrapping function that supports
group convolution no matter the version of CuDNN, and a low-level
wrapper which supports only what CuDNN supports. The top-level
function conditions on CuDNN version, and invokes the low-level
interface 1 or n times.
- There is now a debugging printer for tensor descriptors.
- Convolution struct is replaced with ConvolutionArgs, which is not
part of the public API but is used internally to conveniently
pass around all of the arguments needed for Convolution.
- Add some constexprs for well-known dimensions, reduce amount of
magic numbers in code.
- Put 'deterministic' in to ConvParams. Fixes#3659
- Lots more comments.
- Some pessimizations, in the name of code clarity:
- The descriptors are initialized on every invocation of convolution
forward/backward. Previously, the descriptors were cached, so that
you didn't have to initialize them again on backwards. This is
difficult to support in the ATen interface so I didn't support it.
- Legacy group convolution initializes its workspace for *every* group
it performs. I did not feel motivated to fix this because the
legacy codepath is already quite slow.
- Affine grid generator and grid sampler automatically call contiguous
on their arguments as necessary.
- Batchnorm input checking is greatly beefed up, it now checks for
the following input characteristics:
- Definedness
- GPU location
- Type
- Contiguity
- Size
PyTorch binding code changes
- batchnorm now uses consistent var/data naming
- batchnorm and convolution make use of new ATen bindings
- Affine grid generator and grid sampler make use of ATen CuDNN
bindings via derivatives.yaml. This means I had to restructure
the code a little, since the THNN bindings still go through
a legacy Python class.
- I fixed some warnings:
- s/friend class/friend struct/ on InterpreterStateImpl
- Removed pessimizing move 'detached' in torch/csrc/autograd/variable.cpp
- Removed unused pack_list on Scalar
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
GCC 4.8 buildfix
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Add TensorGeometry to ATen.h
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
CUDNN_CHECK
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Update TODO comment
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Delete return in cudnn_grid_sampler
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
s/cudnnSetStreamToCurrent/setCuDNNStreamToCurrent/g
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Don't allocate a new vector when filtering defined.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Remove Check overloads, convert to pass references.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Some more microbenchmarking.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
