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.
This allows assert() to be used on Zynq, where abort() is not
currently implemented for kernels. Furthermore, this is arguably
the more natural implementation of assertions on all kernel targets
(i.e. where embedding into host Python is used), as it matches host
Python behavior, and the exception information actually makes it to
the user rather than leading to a ConnectionClosed error.
Since this does not implement printing of the subexpressions, I
left the old print+abort implementation as default for the time
being.
The lit/integration/instance.py diff isn't just a spurious change;
the exception-based assert implementation exposes a limitation in
the existing closure lifetime tracking algorithm (which is not
supposed to be what is tested there).
GitHub: Fixes#1539.
Jagged arrays are no longer silently inferred as dtype=object,
as per NEP-34.
The compiler ndarray (re)implementation is unchanged, so the
test still fails.
Added more tests and use normal rpc instead of async rpc.
Async RPC does not represent the real throughput which is limited by the
hardware and the network. Normal RPC which requires a response from the
remote is closer to real usecases.
