* change the xla representation of JAX's unit
Previously the representation of JAX's unit value (a sentinel /
placeholder) was an empty tuple, but by changing the representation to
something else we can further reduce our dependence on runtime tuples.
This commit makes the representation fairly easy to change. There are
three functions in xla.py that define the representation. Here are
versions that would keep the old XLA representation as an empty tuple:
```
def _make_unit(c): return c.Tuple()
def _make_abstract_unit(_): return xc.Shape.tuple_shape(())
def _device_put_unit(_, device):
return xc.Buffer.make_tuple((), device, backend=xb.get_device_backend(device))
```
The new representation is as a trivial array. An alternative
representation would be nothing at all: we don't need to generate XLA
computations that have representations of JAX units. While that
alterntaive is probably the best choice, it seemed like it would require
a bit more refactoring/bookkeeping (e.g. to allow XLA computations to
have a smaller number of outputs than the corresponding JAX function),
and would also mean the XLA representation would be a step further
removed from the jaxpr representation. So I stuck with a trivial array
for now.
The mapping from JAX types to XLA types need not be invertible. However,
XLA translation rules currently don't take as arguments the
corresponding JAX types (abstract values), and there were a few cases
where we relied on checking whether an argument's XLA type was that of
an empty tuple so as to determine if we were effectively operating on a
JAX unit.
In particular, the AD-related primitive add_jaxvals_p could in principle
add two units, and get lowered to an XLA addition on the unit
representation. Previously, the translation rule for add_jaxvals_p
checked the XLA type so that adding two empty tuples didn't produce any
XLA operation; now it adds its inputs, and so if unit is represented as
a trivial array we could be inserting trivial scalar adds where we had
none before. However, if that case is ever possible, it doesn't come up
in our tests (which I checked by keeping the representation as an empty
tuple and then asserting an XLA tuple type is never seen by that
translation rule).
* add comment about JAX<->XLA array types assumption
* Adding `static_argnums` to `pmap` for similar behaviour to `static_argnums` of `jit`.
* Removed check for ShardedDeviceArray
* Final clean up and rename.
Freevars played a very small role, and they can be folded with
the invars. This simplifies the Jaxpr data structure.We remove
the `freevars` field from Jaxpr and from the bound_subjaxprs.
The only non-trivial change is for xla_pmap, where we need
to carry one extra parameter `mapped_invars` with a bitmap
to encode which invars are mapped and which are broadcast.
Previously, the freevars were broadcast.
* shapecheck of jit, device_put, broadcast_in_dim, better error for unsupported ops, parse multi-digit integer literals
* WIP shapecheck np.pad
* Implement shapecheck of gather, pad
* Fix shapecheck of pad
* Implement polymorphic shape rule for (strided/dilated) convolution, refactor
* Cleanup
* Fix
* Remove all polymorphic shape rules, reuse shape rules instead.
* Register shape_rule for all standard_primitives
* Remove ShapeExpr, canonicalize_poly, renames
* Complete shapecheck(binop) implementation, remove special cases for polymorphic shapes
* Allow Poly of form d*poly + k to be divided by d
* Fix bug, inline poly_without_zeros.
One issue with nested pmaps on multihost platforms is inferring the global
pmap axis size without communication. This commit sidesteps the issue by adding
an `axis_size` argument to manually provide this information.
This change only enables a single cross-host pmap; all inner pmaps must be
single-host.
Addressing: #1753
We want to allow users to control how reverse-mode autodiff saves values
from the forward pass. In particular, we want it to be easy to signal
that a function shouldn't have any of its intermediate residuals stored
for the backward pass, and instead those values should be recomputed
from the function's saved inputs. (This feature is especially handy for
accelerators on which memory access is much more expensive than FLOPs
are.) In JAX terms, since we implement reverse-mode as a composition of
forward-mode, partial evaluation, and transposition, we want users to
control how partial evaluation behaves.
See https://github.com/google/jax/pull/1749 for more.
Co-authored-by: Dougal Maclaurin <dougalm@google.com>
* A TypedJaxpr contains more useful information (consts, types)
* Also forced the instantiation of constants when producing the jaxpr.
Before:
>>>print(api.make_jaxpr(lambda x: 1.)(0.))
lambda ; ; a.
let
in [*]}
After this change:
>>>print(api.make_jaxpr(lambda x: 1.)(0.))
lambda ; ; a.
let
in [1.0]}
The appropriate Backend is instead inferred from the 'device' argument. This is a first step towards removing the 'backend' argument from more functions.
* Move internal type-related functions into a new (internal) jax.types module.
Avoid calling onp type functions in lieu of the wrappers in jax.types. Currently these do the same thing, but future changes will make the behavior of the jax type functions diverge from the classic NumPy versions in some cases.
Move xla_bridge.canonicalize_dtype into jax.types, since it fits there more naturally.
* Rename jax.types to jax.dtypes.
* s/types/dtypes/ in tests.
