What’s new in PyPy 5.0¶
_PyLong_FromByteArray(), which was buggy.
Fixed a crash with stacklets (or greenlets) on non-Linux machines which showed up if you forget stacklets without resuming them.
Fix tests to run cleanly with -A and start to fix micronumpy for upstream numpy which is now 1.10.2
Fix the cpyext tests on OSX by linking with -flat_namespace
Refactor and improve exception analysis in the annotator.
Fix issue #2193.
isinstance(..., int) =>
to allow for alternate
int-like implementations (e.g.,
Improve the performace of struct.unpack, which now directly reads inside the string buffer and directly casts the bytes to the appropriate type, when allowed. Unpacking of floats and doubles is about 15 times faster now, while for integer types it’s up to ~50% faster for 64bit integers.
Remove unnecessary special handling of space.wrap().
Improve the memory signature of numbering instances in the JIT. This should massively decrease the amount of memory consumed by the JIT, which is significant for most programs.
Improve the heuristic when disable trace-too-long
Make rlist’s ll_listsetslice() able to resize the target list to help simplify objspace/std/listobject.py. Was issue #2196.
A somewhat random bunch of changes and fixes following up on branch ‘anntype’. Highlights:
- Implement @doubledispatch decorator and use it for intersection() and difference().
- Turn isinstance into a SpaceOperation
- Create a few direct tests of the fundamental annotation invariant in test_model.py
- Remove bookkeeper attribute from DictDef and ListDef.
- Enhancement. Removed vector fields from AbstractValue.
Simplification. Backends implement too many loading instructions, only having a slightly different interface. Four new operations (gc_load/gc_load_indexed, gc_store/gc_store_indexed) replace all the commonly known loading operations
Move wrappers for OS functions from rpython/rtyper to rpython/rlib and turn them into regular RPython functions. Most RPython-compatible os.* functions are now directly accessible as rpython.rposix.*.
Simplify a bit the GIL handling in non-jitted code. Fixes issue #2205.
Trivial cleanups in flowspace.operation : fix comment & duplicated method
Add a test for pre-existing AF_NETLINK support. Was part of issue #1942.
Trivial misc cleanups: typo, whitespace, obsolete comments
Fix the cryptic exception message when attempting to use extended slicing in rpython. Was issue #2211.
Optimize the case where, in a new C-created thread, we keep invoking short-running Python callbacks. (CFFI on CPython has a hack to achieve the same result.) This can also be seen as a bug fix: previously, thread-local objects would be reset between two such calls.
Optimize global lookups.
Updated to CFFI 1.5, which supports a new way to do embedding. Deprecates https://pypy.readthedocs.org/en/latest/embedding.html.
Fix SSL tests by importing cpython’s patch
Remove pure variants of
getfield_gc_* operations from the JIT. Relevant
optimizations instead consult the field descriptor to determine the purity of
the operation. Additionally, pure
getfield operations are now handled
entirely by rpython/jit/metainterp/optimizeopt/heap.py rather than
rpython/jit/metainterp/optimizeopt/pure.py, which can result in better codegen
for traces containing a large number of pure getfield operations.
Try to ensure that no new functions get annotated during the ‘source_c’ phase. Refactor sandboxing to operate at a higher level.
Refactor vmprof to work cross-operating-system.
Seperate structmember.h from Python.h Also enhance creating api functions to specify which header file they appear in (previously only pypy_decl.h)
Refactor register_external(), remove running_on_llinterp mechanism and apply sandbox transform on externals at the end of annotation.
vmprof should work on Windows.
When creating instances and adding attributes in several different orders depending on some condition, the JIT would create too much code. This is now fixed.
Improve CPython C API support, which means lxml now runs unmodified (after removing pypy hacks, pending pull request)
Look inside tuple hash, improving mdp benchmark
Compress resume data, saving 10-20% of memory consumed by the JIT
Fix boolean-array indexing in micronumpy
Support ndarray.partition() as an app-level function numpy.core._partition_use, provided as a cffi wrapper to upstream’s implementation in the pypy/numpy repo