* Add a JIT interpreter
The separate interpreter is used to graphs with a lower overhead than
converting them to autograd graphs. Some notes:
* does not support Handles/PythonOp/CppOp, these will be in a future commit
* jit_closure.cpp still exists and we fall back to it for now when
cannot handle something because of PythonOp/CppOp
* In order to support retain_graph=True, the interpreter can be cloned,
creating a copy that can be run with different arguments. This is
assumed to be the non-standard case so cloning is not particularly optimized.
No tensor _data_ is copied, but the at::Tensor list in the interpreter is.
If we hit problems, there is a lot we could do (such as register allocation)
to minimize the stuff that needs to be copied.
* Uses a pImpl pattern to keep implementation details out of its header file.
* Modifies the way getTensorOp works so that it reads/writes to already-existing
vectors, this prevents needing to realloc these buffers each time.
* Timings are here: https://gist.github.com/zdevito/5a20ac29fb1b9e449e693b67dc478127
This reduces overhead to about the same as running it in python.
It is about 10us faster to run the same thing using ATen directly.
* Code Mod
Interpreter -> InterpreterState
Function -> Code
Add other requested comments.
* RegList -> ListHandle<T>
Change the RegList functions to be safer by identifying the type of
each argument list, and checking that list insert does not try
to add to two different lists at once.
* Use exactly equal for interp tests
Allow in-place operations on views
Adds VariableViewImpl, a subclass of VariableImpl which has a pointer to
the base Variable on which it is a view. In-place operations on views
change the grad_fn of the base.
Note that in-place operations only work on views that are the first output of the function that created them. All C++/ATen implemented functions have this behavior, but it's possible to write Python-implemented autograd functions that do not. In-place operations on these view will raise an exception.
Fixes#3313
* enable size from ATen type
* temp commit aten thd
* port copy, math
* port random
* changes after rebase
* lapack bind
* thd and csrc compile
* fix min/max reductions in DataChannelTCP
* clean up changes
* re-enable tensor constructors
* port MPI to at::Tensor
* fix storage methods to not cast to thpp storage ptrs
This breaks a lot of the onnx-pytorch tests because the abstraction
barriers are not respected. I'll spin up a patch for that separately.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
The pieces:
- I improved the lint / asserts to catch some bugs which I
committed while working on my export. There are two new
properties which the linter checks now:
(1) "Anticipated uses". If a node says that is used by
M, M better appear later in the topsort. Previously,
we only checked if it was in all_nodes.
(2) If you are a select node, you better be a multi-type node;
if you're not a select node, you better not be! And you
should never have an input that is multi-type.
- There is a new peephole optimization pass, for simple, local
transformations to graphs. Right now, it implements a simple
optimization: remove 'expand' invocations that are no-ops
(the size before matches the size after), but we can add other
things to it later. I needed this for ONNX because no-op expands
show up in the left-hand argument, which we don't support.
- There is now a broadcast fuser, which fuses ATen expand ops
into broadcastable ONNX ops (Add, Div, Mul, Pow, Sub, Gemm.)
It only fuses when the original size is a suffix of the new
size, as per the ONNX spec.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
- Cleaned up THNN and THCUNN code and kernels
- Improved THCUNN kernel performance 5x, making it match cuDNN performance
- Added support for computing softmax over arbitrary dims
NOTE: The default dim for 3D inputs is now 1 (used to be 0)
- Both functions now accept inputs with arbitrarily many dimensions
- Autograd functions no longer save the input (it's unnecessary)
- Added cuDNN bindings for softmax, but they are unused as THCUNN
matches or even exceeds cuDNN performance
* skeleton commit for building and linking nnpack library in PyTorch
* first stab at conv forward binding + integration
* bind NNPACK gradient kernels
* move nnpack forward, input gradient calls deeper
* nnpack conv api mimics nn
* fix symbol error; use memory across calls
* clean up warnings, add shape checking, thread safety, configurable thread specification
* add batch size threshold, also bind for single-element batch for the future
This adds some generated autograd functions implemented in C++, which
are generated from derivatives.yaml. It also generates Python bindings
for the Variable methods. The generated files are:
Functions.cpp/h: subclasses of torch::autograd::Function
VariableType.cpp/h: The at::Type for autograd Variables
python_variable_methods.cpp: Python bindings to torch::autograd::Variable
python_variable_methods_dispatch.h: wrapper which releases GIL and sets the
CUDA device
python_functions.cpp/h: exposes generated autograd functions as Python
objects
The generated functions are mostly shadowed by the definitions in
variable.py. We'll remove the Python implementations in favor of the
generated C++ implementations in a subsequent commit.
