We don't need to know whether there's a outer finally block
that's already implicit in the current break and continue
target.
Signed-off-by: Michael Birtwell <michael.birtwell@oxionics.com>
Note that because we changed exception representation from using string
names as exception identifier into using integer IDs, we need to
initialize the embedding map in order to allocate the integer IDs. Also,
we can no longer print the exception names and messages from the kernel,
we will need the host to map exception IDs to names, and may need the
host to map string IDs to actual strings (messages can be static strings
in the firmware, or strings stored in the host only).
We now check for exception IDs for lit tests, which are fixed because we
preallocated all builtin exceptions.
Ported from:
M-Labs/artiq-zynq#162
This includes new API for exception handling, some refactoring to avoid
code duplication for exception structures, and modified protocols to
send nested exceptions and avoid string allocation.
* Revert "Merge pull request #1544 from airwoodix/dataset-compression"
This reverts commit 311a818a49, reversing
changes made to 7ffe4dc2e3.
* fix accidental revert of f42bea06a8
Removed test cases that do not respect lifetime/scope constraint.
See discussion in artiq-zynq repo: M-Labs/artiq-zynq#119
Referred to the patch from @dnadlinger. 5faa30a837
In the origin implementation, the `nowrite` flag literally means not writing memory at all.
Due to the usage of flags on certain functions, it results in the same issues found in artiq-zynq after optimization passes. (M-Labs/artiq-zynq#119)
A fix wrote by @dnadlinger can resolve this issue. (c1e46cc7c8)
This breaks the internal dataset representation used by applets
and when saving to disk (``dataset_db.pyon``).
See ``test/test_dataset_db.py`` and ``test/test_datasets.py``
for examples.
Signed-off-by: Etienne Wodey <wodey@iqo.uni-hannover.de>
This broke after b8cd163978, but
is invalid code to start with; this would have previously
crashed the code generator had the code actually been compiled.
(Allowing implicit conversion to bool would be a separate debate.)
The previous code could have never worked as-is, as the result slot
went unused, and it tried to append the load instruction to the
block just terminated with the invoke.
GitHub: Fixes#1506, #1531.
Since we don't implement any integer-like operations for TBool
(addition, bitwise not, etc.), TBool is currently neither
strictly equivalent to builtin bool nor numpy.bool_, but through
very obvious compiler errors (operation not supported) rather than
silently different runtime behaviour.
Just mapping both to TBool thus is a huge improvement over the
current behaviour (where numpy.False_ is a true-like object). In
the future, we could still implement more operations for TBool,
presumably following numpy.bool_ rather than the builtin type,
just like builtin integers get translated to the numpy-like
TInt{32,64}.
GitHub: Fixes#1275.
Previously, any type would be accepted for the test expression,
leading to internal errors in the code generator if the passed
value wasn't in fact a bool.
array([...]), the constructor for NumPy arrays, currently has the
status of some weird kind of macro in ARTIQ Python, as it needs
to determine the number of dimensions in the resulting array
type, which is a fixed type parameter on which inference cannot
be performed.
This leads to an ambiguity for empty lists, which could contain
elements of arbitrary type, including other lists (which would
add to the number of dimensions).
Previously, I had chosen to make array([]) to be of completely
indeterminate type for this reason. However, this is different
to how the call behaves in host NumPy, where this is a well-formed
call creating an empty 1D array (or 2D for array([[], []]), etc.).
This commit adds special matching for (recursive lists of) empty
ListT AST nodes to treat them as scalar dimensions, with the
element type still unknown.
This also happens to fix type inference for embedding empty 1D
NumPy arrays from host object attributes, although multi-dimensional
arrays will still require work (see GitHub #1633).
GitHub: Fixes#1626.
Strided slicing of one-dimensional arrays (i.e. with non-trivial
steps) might have previously been working, but would have had
different semantics, as all slices were copies rather than a view
into the original data.
Fixing this in the future will require adding support for an index
stride field/tuple to our array representation (and all the
associated indexing logic).
GitHub: Fixes#1627.
This was a long-standing issue affecting both lists and
the new NumPy array implementation, just caused by the
generic inference passes not being run on the slice
subexpressions (and thus e.g. ints not being monomorphized).
GitHub: Fixes#1632.
kk1050
Sebastien Bourdeauducq
David Nadlinger
spaqin
Michael Birtwell
Steve Fan
pca006132
hartytp
Etienne Wodey
occheung
Etienne Wodey
Peter Drmota
Leon Riesebos