* Enable scalars if compiled with WITH_SCALAR environment variable.
We are pretty close to enabling scalars (0-dimensional arrays); this allows turning them on
for development purposes and to be able to write code that works both with and without scalars enabled.
WITH_SCALARS is currently broken with distributions, but should work for test_torch, test_autograd, test_nn.
* Fix unsqueeze.
* Fix wrap dim, wrapping with Scalar.
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
Implements basic and advanced indexing using ATen tensors/variables.
Basic indexing is translated at the Python-binding level
(python_variable_indexing.cpp) to slice/squeeze/unsqueeze/select calls.
Advanced indexing is implemented in ATen in terms of take() and put()
calls.
* Use aten version of is_signed.
* Define is_cuda native function and use it for variable.
* Use ATen dim for Variable dim/ndimension.
* Get rid of dim, ndimension fallthroughs in variable.py.
* Move size/stride Variable methods to use ATen.
* Implement shape property on Variable via ATen.
* Remove the _getattr__ function from Variable.
* Get rid of dispatch functions and avoid cast.
* Add THPUtils_packInt64Array.
* Throw python errors.
* Use fallthrough and fix fallthrough generation for native functions.
* is_cuda is a property, not a method.
Add (fully opt-in) functionality to support setting pretty names for
nodes in the graph. In particular
- Variable now has a `name` parameter in the constructor
- export now has `input_names` and `export_names` parameters
Nodes that are not named via this mechanism continue to be named
internally with unique integers.
Names have a few rules.
- They must all be unique in the graph.
- They may not be integers (because of potential conflicts with
internally generated names).
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
Previously, we the Variable.data PyObject* in THPVariable_Wrap. For many
Variables, we don't access their data directly. Instead, they are passed
from one Variable compuatation to another.
This reduces the overhead of ATen-implemented Variable methods by
~200ns.
Also squash a warning about an implicit conversion that will never
occur (because the type being converted to is a superclass).
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Variable is now a subclass of at::Tensor backed by a VariableImpl* pImpl. The implementation of the ATen functions is defined in the auto-generated VariableType.h/cpp file.
Currently, only functions which fall through to the base type, such as sizes() and isCuda() are implemented. Differentiable ops like add() and mul() will be added in a subsequent PR.
Previously, our AST was a DAG, where shared Nodes indicated a computation
should be reused. This commit rewrites the IR into a new functional
representation which represents sharing explicitly using variable
bindings.
We offer a few justifications for this new style:
1. The new representation is not all that different from the
old one; it is about as easy to construct, and the lack of an
explicit graph doesn't negatively impact our ability to interpret
the graph, since we've chosen, as a matter of design, to NOT have
the IR participate in the actual execution of a graph.
2. The new let-binding representation has an implicit ordering,
which we can use to conveniently keep track of the original order
the trace showed up as. This automatically gives us a topsort,
and gives us an easier to read textual representation of our
IR:
%14 = Embedding %11, %0, -1, None, 2, False, False
%15 = Dropout %14, 0.2, True, False
%16 = Index %12, 0
%17 = Index %12, 1
%18 = Index %13, 0
%19 = Index %13, 1
%20 = Index %15, 0
%21 = Linear %20, %1, %3
%22 = Linear %16, %2, %4
3. It moves us closer to a Futhark style language
(http://futhark-lang.org/publications/pldi17.pdf).
Major aspects of the diff
- Node is replaced with Expr and Arg, a pair of mutually recursive
structures which represent our new language. In BNF, the language
looks like this:
a ::= c | %i
e ::= %i, ... = e
| PyOp e, ...
| Ret %i, ...
Technically, Ret is not actually a return (no control flow is involved),
it just tuples up a series of tensors (identified by variables).
One important invariant is that locals are always tensors; they
are never constants (this is asymmetric with Args.)
- Arguments support Python constants. This is an important piece because
many operators take extra Python literals like integers and tuples in
order to specify extra parameters about how an operator operates. Adding
this was essential to getting word_language_model to work.
- As both Expr and Arg have multiple variants, there is new infrastructure
for doing case on the variants using ExprVisitor and ArgVisitor. The
strategy here is adapted from WebAssembly's visitors, although we have
generalized to permit arbitrary argument forwarding, which is necessary
to support tail-recursive visitor calls. TCO is important because our
interpreter may recurse arbitrarily deep into a stack of nested lets.
If users wish, they can also manually case on the type tag.
- Tracing is now turned on and off using _tracer_enter/_tracer_exit in
torch._C. _tracer_enter accepts a list of variables which are to be
treated as arguments; _tracer_exit accepts the list of traced variables
which should be returned when you reexecute the trace, and returns
the trace expression which can be reexecuted. GlobalTracingState
is a global variable which tracks whether or not we are tracing or not.
- You use run_forward to execute a trace on some set of parameters.
- When under tracing, variables keep track, via trace_local, what the
name of their variables in the IR are.
Here is a simple runner which leaks memory but can be used to JIT models:
import torch.autograd.function as F
import torch._C
def jit(model):
import types
real_forward = model.forward
def forward(self, *args):
def flatten(x):
return tuple(F._iter_variables(x))
if not hasattr(self, "saved_trace"):
torch._C._tracer_enter(tuple(self.parameters()) + flatten(args))
out = real_forward(*args)
self.saved_trace = torch._C._tracer_exit(flatten(out))
self.saved_outs = out
return out
else:
flat_out = Variable._execution_engine.run_forward(self.saved_trace, tuple(self.parameters()) + flatten(args))
return F._unflatten(flat_out, self.saved_outs)
Major problems:
- Sanity checking is spotty at best, especially when users pass in variables.
- The interpreter leaks tensor memory from the store. When we add back def-use
we should be able to deallocate tensors as soon as we know they are no longer
necessary.
- The interpreter needs to reach feature parity with the old execution engine.
From there, we need to see if backwards can be subsumed as well.
- I still have no confidence in having memory managed everything correctly.
This requires a close look.
- Rather than return an *open* expression as a trace, we should return a
*lambda* instead, which knows about how many formal parameters it
requires.
- The IR is not introspectable from Python at the moment, but this is simply a
matter of implementing all the binding code.
- The tracer is NOT reentrant (you can't trace while you're inside a trace.)
Furthermore, no sanity checking is done if you try to incorrectly reuse
things from one trace in another.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Fix gc_refs assertion failure
Ensure that each THPVariable -> THPFunction reference contributes one
ref count to the THPFunction by creating a new shared_ptr for each ref.
Because multiple shared_ptrs can again manage a single THPFunction, it's
not safe to use std::weak_ptr where it may point to a PyFunction. It's
still safe to use weak_ptr for grad_accumulator since these are never
PyFunctions.
Fixes#1626
* Remove stale comment
Because of this Variables can no longer appear in the graph.
Every usage of a leaf Variable will leave an AccumulateGrad
function that has no outputs, but modifies var.grad as a side
effect.
The core autograd Variable, Function, and Engine no longer depend on the
Python API. This let's us implement functions in C++. In the future, we
can also multithread engine and release the GIL for most of the
non-Python backwards.
