* Fix some minor errors in existing docs.
* Fix Convolution and Pooling docs in torch.nn.functional
* Cleaned up torch.nn.functional docs
* Address @SsnL 's comments
* Add multiplication sign missing in docs
* Fix more typos, and clear some warnings
* Change infinity symbol in LPPool2d
* Revert some changes in torch.nn.functional
* Few more minor changes
* implement CosineEmbeddingLoss as a native function and add reduce=True arg to it
* fix flake8
* address comments
* add reference function to tests
* fix flake8
The nn.* counterpart of #5443 . Mostly removed Variable wrapper. Also added doc for nn.RReLU.
Notice that torch.randn(*, requires_grad=True) isn't documented until #5462 is done.
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
* at::maybe_data_ptr and Check.h => TensorUtils.h
* THNN support for optional BN running_*
* ATen support for optional BN running_*
* Python nn.* support for optional BN running_*; Improve IN and BN doc
* Add tests for IN and BN new option
* Layer Norm
* Fix LRN doc
* functional interface for LN and IN
* Layer norm tests
* fix BN double backward returning undefined tensors
* fix jit test using wrong dim inputs for BN
* add/improve BN, IN and LN GPU tests with half type
* Udpate docs to be consistent with Conv notation
Fix onnx
Clarified onnx symbokic wrapper
* fix typo
* Address comments
* add reduce=True arg to HingeEmbeddingLoss
* pass arg to super constructor in HingeEmbeddingLoss
* make HingeEmbeddingLoss reference fn work on legacy
* Add criterion scalar tests.
This exposed an issue in MarginRankingLoss with scalars, but the cleanest way to fix is to wait
until forward runs on Variables (so we don't have to wait for the backward to check if something
is a scalar).
* Fix flake8.
* Add error message for margin_ranking_loss with scalars.
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.
Implements nn.Embedding (lookup table) in ATen.
Breaking change: new optional argument padding_idx in F.embedding to
match nn.Embedding.
Note that there are a few bugs in Embedding that are inherited from the
previous code:
- CUDA renorm has race conditions if index contains duplicate entries
- sparse gradient doesn't work with scale_grad_by_freq
This is a step towards removing the special casing of NN functions in gen_variable_type.py. It fixes the signature of in-place NN functions so that they return Tensor & instead of Tensor.
- Rename THNN convolution to have thnn_ prefix.
- Propagate CuDNN benchmark and deterministic to at::Context
- Add 'convolution', 'convNd' and 'conv_transposeNd' native wrappers, with defaults
The conv_transposeNd wrappers are updated to have the same argument
order as Python.
- torch.nn.functional directly dispatches to the native wrappers
- Make it possible to turn off tracing for some native wrappers, so I don't
have to write symbolics for all the functions above
- Spectral ops can now make use of CuDNN convolution if possible
- Better commentary on cudnn_batch_norm
- Turn on DCE for all JIT tests.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Batchnorm in ATen
This commit moves BatchNorm derivatives into ATen, eliminating
torch/csrc/autograd/functions/batch_normalization.cpp
Some refactoring along the way:
- Functions got renamed to remove _forward from their names
- CuDNN batchnorm forward was modified to return save_mean/save_std instead of
take it as parameters. To avoid returning undefined Variables, these return
(small) uninitialized tensors when they are not used.
- THNN batch normalization takes care of resizing save_mean and save_std on
forward.
- There are some shenanigans re batchnorm backwards in eval mode. I'm tracking
that in #4284
- I decided not to introduce buffers as a proper concept in ATen, which means
that tensors like running_mean/running_var are variables in ATen. This meant
there needed to be some adjustments to how we *trace* such variables; the
new strategy is if we can't find a Value for a variable, we look and see
if we have a Value for the buffer pointed to by the variable, before
finally falling back on constant.
- This PR finally reliably triggered OOM on Travis builds; I fixed this by reducing
the number of parallel jobs.
- Stop using std::string when it's not necessary.
- Remove training parameter from cudnn_batch_norm_backward, because it
doesn't make sense; cuDNN doesn't implement the math for evaluation mode
batchnorm backwards.
- batchnorm_double_backward is now in an anonymous namespace, as it
no longer needs to be called from torch/csrc
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Convolution derivatives in ATen
This PR introduces ATen implementation of convolution, which dispatches to
THNN/CuDNN/nnpack based on input parameters. The general strategy is to compose
this function out of the various forward-backward pairs of specific
implementations, rather than write a monolithic function with backwards (which
is what we did before because the boilerplate of doing it otherwise would have
been very high.) The new API provides the following functions:
- _convolution, which is a fully generic, native convolution implementation
that dispatches to various other convolution implementations depending on
input characteristics. This is prefixed with an underscore because it
explicitly takes benchmark, deterministic and cudnn_enabled which are
implementation details for CuDNN. The intent is to eventually provide a
convolution that reads these parameters out of the context using #4104.
- _convolution_nogroup is a convolution implementation for non-CuDNN
algorithms which don't support group convolution natively.
- _convolution_double_backward is the generic double-backwards implementation
for convolution.
In more detail:
- Most functionality from torch/csrc/autograd/functions/convolution.cpp has been
moved into aten/src/ATen/native/Convolution.cpp
- We continue to make use of ConvParams, but we now construct the parameters
upon entry to a function from the function signature (which does not use
ConvParams; having convolution take ConvParams directly would require teaching
the code generator how to accept these as parameters, complicating ATen's API
model) and destruct them when making subprocedure calls.
- I introduce a new idiom, input_r, which represents a const Tensor& reference,
which will subsequently be assigned to a local Tensor input. This is helpful
because a lot of the existing algorithms relied on being able to assign to
locals, which is not permitted with a const reference.
- The native argument parser now supports std::array<bool,2> inputs (NB: there
MUST NOT be a space; this is the same hack as is applied to derivatives.yaml)
- Native parser now supports Tensor? arguments, which indicates a nullable
tensor. Previously this function was only used by NN methods.
- Documentation updates on THNN library
- I added an extra fgradInput argument to VolumetricConvolutionMM_updateOutput
and VolumetricConvolutionMM_accGradParameters so that its buffer list lines up
with the backward argument list. This makes it possible to write derivative
for conv3d which previously was not supported (commented out in
derivatives.yaml)
- Extra double_backward declarations for all convolution backwards functions was
added.
- You can now use the syntax Tensor? in native_functions.yaml to indicate that a
tensor argument is nullable. There are adjustments to propagate this to the
Python argument parser.
- NNPACK was ported to ATen, and ATen now builds and links against ATen if
possible. New AT_NNPACK_ENABLED macro. The nnpack functions are
nnpack_spatial_convolution.
- Some modest CuDNN convolution refactoring to remove _forward from names.
- There's a new cudnn_convolution_backward function to deal with the fact that
CuDNN convolution double backward requires you to have computed all gradients
in one go.
- Variable set_flags now checks if the tensor is undefined, fixing a silent memory
corruption.
- checkSameType updated to not raise an exception if called with Variable arguments
- "no ATen declaration found for" error message is improved to say what available declarations are
- make_variable now accepts undefined tensors, and returns an undefined tensor in this case.
* add reduce arg to PoissonNLLLoss
* fixed comments except reference function
* fixed unit test
* small indentation fix
* fixing last comments by richard
* lint check
* another linting issue
