This is the first of three PRs that #5537 will be split into.
This PR adds mkl headers to included files, and provides helper functions for MKL fft and cuFFT.
In particular, on POSIX, headers are using mkl-include from conda, and on Windows, it is from a new file @yf225 and I made and uploaded to s3.
* add mkl-include to required packages
* include MKL headers; add AT_MKL_ENABLED flag; add a method to query MKL availability
* Add MKL and CUFFT helpers
* Support native namespace functions with type dispatch.
Use 'ones' as an example. Note this is a "halfway" solution; i.e. the call chain is:
at::ones(shape, dtype) -> dtype.ones(shape, dtype) -> CPUFloatType.ones(shape, dtype) -> at::native::ones(shape, dtype)
The "nicer" solution would probably be something like:
at::ones(shape, dtype) -> dtype.ones(shape) -> CPUFloatType.ones(shape) -> at::native::ones(shape, this)
* Fix type inference.
* Fix test install.
* Fix extensions.
* Put dtype argument at the beginning.
* Fix extension.cpp.
* Fix rnn.
* Move zeros in the same manner.
* Fix cuda.
* Change randn.
* Change rand.
* Change randperm.
* Fix aten contrib.
* Resize in randperm_out.
* Implement eye.
* Fix sparse zeros.
* linspace, logspace.
* arange.
* range.
* Remove type dispatch from gen_python_functions.
* Properly generate maybe_init_cuda for type dispatch functions not named type.
* Don't duplicate dtype, this parameters for native type dispatched functions.
* Call VariableType factory methods from the base type so it gets version number 0.
* Address review comments.
* Port cuDNN RNN dropout state initialization to ATen and make Python code use it.
Fixes#5138.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Variable/Tensor bugfix
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
The Tensor and Variable classes are being merged.
autograd.Function.forward is now called on Variables, but with "no-grad"
mode (torch.no_grad()) enabled.
One benefit is that we no longer have to explicitly track shared
storages.
* Add transpose() to TensorGeometry.
This code is dead; I briefly used it in my RNN patchset but
eventually rewrote it to not be necessary. However, it seemed
like a useful gadget so I kept it. In general, it seems that it
would be useful for TensorGeometry to support all operations that
Tensor does, but it only computes the changes to sizes/strides
instead of actually doing the computation.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Turn on wrap_dim behavior for TensorGeometry
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Support for hard-coded differentiable outputs.
Some outputs of functions are nondifferentiable, and should always
be returned with requires_grad=False. Traditionally, we have used
the presence of 'grad' to signal that only the first output is
differentiable, and the rest are not, but cudnn_rnn (to be
implemented) breaks this pattern; its first three outputs are differentiable,
but its last output is a buffer that is just consumed by backwards.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* TensorGeometry constructor from just sizes
The sizes are assumed to form a contiguous tensor, and we compute
the strides we would get in that case.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Support saving TensorList for backwards.
There is some back story here. Saved TensorList in backwards will
be used by cudnn_rnn, and it is worth asking, why is it necessary to
save a list of tensors? Indeed, *technically* speaking a list of
tensors is not necessary, we only need to save the sizes of each
of the weight tensors. (We need the sizes because cuDNN is only
going to blast the derivative of weights into a flat buffer, but
we need to match the sizes of the views into the buffer when we
eventually return the derivatives.)
However, it was surprisingly awful trying to implement passing just
sizes, because as non-Tensor arguments, the JIT interpreter generation
code is expected to handle all non-Tensor arguments as attributes in the
trace, and our attributes struct doesn't actually know how to do
arrays of arrays. Saved TensorList code was much easier to get working,
so that's what this patch does.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* MatrixRef - an ArrayRef with a stride, making it a 2D ArrayRef.
Like ArrayRef, this class does not own the underlying data, it is expected
to be used in situations where the data resides in some other buffer.
This is intended to be trivially copyable, so it should be passed by
value.
For now, 2D only (so the copies are actually cheap, without having
to write a SmallVector class) and contiguous only (so we can
return non-strided ArrayRef on index).
The intended use-case (not in this commit) is to make it easier to
work with RNN weights, which are num_weights x num_layers matrix of
parameters.
P.S. dimension 0 indexes rows, dimension 1 indexes columns
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Generalize getDataType in Descriptors.h
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Change copy_range to take Tensor, and change cat_tensors_backward accordingly
Should a backward function return a Variable or a Tensor? For the most
part, all of our backward functions return Tensor, except cat_tensors_backward,
which returns a variable_list (which is really the only thing that matters,
because Tensor and Variable are interconvertible). But this is kind of weird,
because it means that you can't implement a backwards in ATen that returns
a std::vector<Tensor>, and then hook it up transparently with the derivatives
code. So I switched it over.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Support 5-ary return Tensor tuple.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Support code generation with mixed Tensor/TensorList in output.
I don't think I ended up using this in cudnn_rnn, but this seems
it might be useful for someone else later.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Support 4-ary boolean array
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Add support for retain_variables in tools/autograd/derivatives.yaml
'retain_variables', a bool which is true if a user has specified
that saved variables should be retained in case the backwards is
run again later. This allows an optimization where we can
destroy saved buffers if we know variables are not going to be retained,
e.g., it is (will be) used by _cudnn_rnn
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Lazily initialize cuDNN descriptors
Previously, cuDNN descriptors were eagerly allocated as soon
as a FooDescriptor object was created. However, in some uses
of TensorDescriptor, this is problematic: some tensors are optional
and cuDNN's API expects to be given a nullptr TensorDescriptor
in this case, not an uninitialized (but allocated) descriptor.
Lazily initializing the descriptors makes it less likely for
us to use uninitialized memory and matches the usual semantics of
unique_ptr. It's good sense!
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Port cuDNN RNNs to ATen.
This brings three new functions:
- _cudnn_rnn_flatten_weight: flatten a matrix of weight tensors into
a single contiguous weight buffer as required by cuDNN
- _cudnn_rnn: run RNN forwards
- _cudnn_rnn_backward: run RNN backwards
RNNs have a lot of parameters, so we restructured what was previously
a single 'fn' object that recorded all the parameters into three
objects: RNNDescriptorParams, TensorDescriptorListParams and
DropoutDescriptorParams.
We make use of MatrixRef to organize the weight tensors (which are
weight/bias x number of layers), but I did not teach the codegen
how to pass these as arguments/return values natively, so instead
a MatrixRef is passed as its constituent ArrayRef and int64_t stride0.
cudnn_rnn has three differentiable outputs and one nondifferentiable
one, so it makes use of the support for hard-coded differentiable outputs.
I haven't deleted all of the descriptor code from Python, because dropout
initialization still goes through this codepath, that should be fixed soon
but I don't see it as essential for this PR.
This commit also removes the last use of NestedIOFunction from PyTorch.
There are some shenanigans with cuDNN dropout descriptor initialization,
see below:
Note [cuDNN dropout descriptor initialization]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In most cases, setting descriptors in cuDNN is cheap (e.g.,
cudnnSetTensorNdDescriptor). However, this is not the case for
cudnnSetDropoutDescriptor: in cuDNN 6/7 (and possibly others) it does an
expensive precomputation to initialize the random number generator states. In
cuDNN 6, this is the ONLY official mechanism to initialize a dropout descriptor,
which means that law-abiding clients were expected to generate a dropout
descriptor once and cache it. However, our ATen interface is (1) stateless (so
we can't cache the descriptors) and (2) does not accept arbitrary user types in
its interface (so we can't pass the descriptor in). This puts us in a pickle.
In cuDNN 7, a new function, cudnnRestoreDropoutDescriptor was added, which
forgoes the expensive initialization process, and can initialize the
descriptor with a pre-initialized state CUDA tensor. This is great, because
it means we can simply pass in the state tensor and then initialize the
descriptor internally. Unfortunately, this function is not available in
cuDNN 6.
To work around this, we break the cuDNN abstraction barrier, and have
the struct layout of the underlaying dropout descriptor. With this struct,
we can reimplement cudnnRestoreDropoutDescriptor from scratch. Great!
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Fix cuDNN 7 behavior.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Delete some unused, controversial methods from MatrixRef.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Add missing filter_dim_a slice
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Replace nested for-loop with itertools.chain.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* CR comment on mut_desc()
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Refactor DropoutDescriptor API.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Use cached CurrentDeviceProperties from Context.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Document _cudnn_rnn outputs.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Improve fmap docs, convert some functions to use it.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Move IndexRange to autograd/function.h
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Elaborate on CUDNN_STATUS_INVALID_VALUE return some more.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Add an all-in-one setter for RNNDescriptorParams.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Print what the unrecognized RNN mode was
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* RNN TensorDescriptor improvements
- Have an explicit size/stride overload for set TensorDescriptor,
so you don't have to create a goofy view to feed in.
- Change the padding to 3D rather than 5D, which is all you actually
need (it's just 2D that is not supported by cuDNN API.)
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Fix implementation of cudnnRestoreDropoutDescriptor, plus test.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Better comments about input layout.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Add comment about no-DropoutDescriptor argument RNNDescriptor function.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Rename vocab_size back to input_size.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Don't use backslash in comment.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Bugfix for contiguous TensorGeometry calculation.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Don't allocate a dummy tensor when setting TensorDescriptor for flatten_weight.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Make contiguity errors more user-friendly.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* s/fn.dropout.train/fn_train/
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* s/_cudnn_rnn_backward_grad/_cudnn_rnn_backward_input/
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Make dcx properly undefined when not required.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Remove old TODO.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Add state size check in cudnnRestoreDropoutDescriptor
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Explicitly narrow int64_t to size_t
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Restore copyParams comment.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Update benchmark numbers, and slight engineering improvements.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Typofix.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Three stage plan to no more stupidly weird "why isn't cuDNN enabled"
bugs:
- Add torch.backends.cudnn.disable_global_flags(), which as its name suggests,
disables global flag setting in cuDNN, so that you are not allowed to
make changes to this state. However, the flags() context
manager continues to work (since they are non-global changes).
- Call disable_global_flags() in test/common.py
- Switch all of the manual flag setting/unsetting in test/test_nn.py
to use the context manager.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
- 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>
This is not currently used by anything, but eventually ATen
will need to make decisions about whether or not to use
CuDNN functions or not, which means we need to propagate
this variable to ATen.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Check cuDNN version at runtime
This checks that the version from cudnn.h matches the version from
libcudnn.so.
Fixes#1476
* Only check major and minor version numbers
This ensures that we use the same library at the C++ level and with
Python ctypes. It moves the searching for the correct library from
run-time to compile-time.
Here's the command I used to invoke autopep8 (in parallel!):
git ls-files | grep '\.py$' | xargs -n1 -P`nproc` autopep8 -i
Several rules are ignored in setup.cfg. The goal is to let autopep8
handle everything which it can handle safely, and to disable any rules
which are tricky or controversial to address. We may want to come back
and re-enable some of these rules later, but I'm trying to make this
patch as safe as possible.
Also configures flake8 to match pep8's behavior.
Also configures TravisCI to check the whole project for lint.
