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This version works with Pythons below 3.12 so far. Upstream changes since 0.55.2: Version 0.58.1 (17 October 2023) This is a maintenance release that adds support for NumPy 1.26 and fixes a bug. NumPy Support Support NumPy 1.26 Support for NumPy 1.26 is added. (PR-#9227) Bug Fixes Fixed handling of float default arguments in inline closures Float default arguments in inline closures would produce incorrect results since updates for Python 3.11 - these are now handled correctly again. (PR-#9222) Pull-Requests PR #9220: Support passing arbitrary flags to NVVM (gmarkall) PR #9227: Support NumPy 1.26 (PR aimed at review / merge) (Tialo gmarkall) PR #9228: Fix #9222 - Don’t replace . with _ in func arg names in inline closures (gmarkall) Authors gmarkall Tialo Version 0.58.0 (20 September 2023) Table of Contents Version 0.58.0 (20 September 2023) Highlights New Features Improvements NumPy Support CUDA Changes Bug Fixes Changes Deprecations Pull-Requests Authors This is a major Numba release. Numba now uses towncrier to create the release notes, so please find a summary of all noteworthy items below. Highlights Added towncrier This PR adds towncrier as a GitHub workflow for checking release notes. From this PR onwards every PR made in Numba will require a appropriate release note associated with it. The reviewer may decide to skip adding release notes in smaller PRs with minimal impact by addition of a skip_release_notes label to the PR. (PR-#8792) The minimum supported NumPy version is 1.22. Following NEP-0029, the minimum supported NumPy version is now 1.22. (PR-#9093) Add support for NumPy 1.25 Extend Numba to support new and changed features released in NumPy 1.25. (PR-#9011) Remove NVVM 3.4 and CTK 11.0 / 11.1 support Support for CUDA toolkits < 11.2 is removed. (PR-#9040) Removal of Windows 32-bit Support This release onwards, Numba has discontinued support for Windows 32-bit operating systems. (PR-#9083) The minimum llvmlite version is now 0.41.0. The minimum required version of llvmlite is now version 0.41.0. (PR-#8916) Added RVSDG-frontend This PR is a preliminary work on adding a RVSDG-frontend for processing bytecode. RVSDG (Regionalized Value-State Dependence Graph) allows us to have a dataflow-centric view instead of a traditional SSA-CFG view. This allows us to simplify the compiler in the future. (PR-#9012) New Features numba.experimental.jitclass gains support for __*matmul__ methods. numba.experimental.jitclass now has support for the following methods: __matmul__ __imatmul__ __rmatmul__ (PR-#8892) numba.experimental.jitclass gains support for reflected “dunder” methods. numba.experimental.jitclass now has support for the following methods: __radd__ __rand_ __rfloordiv__ __rlshift__ __ror_ __rmod_ __rmul_ __rpow_ __rrshift_ __rsub_ __rtruediv_ __rxor_ (PR-#8906) Add support for value max to NUMBA_OPT. The optimisation level that Numba applies when compiling can be set through the environment variable NUMBA_OPT. This has historically been a value between 0 and 3 (inclusive). Support for the value max has now been added, this is a Numba-specific optimisation level which indicates that the user would like Numba to try running the most optimisation possible, potentially trading a longer compilation time for better run-time performance. In practice, use of the max level of optimisation may or may not benefit the run-time or compile-time performance of user code, but it has been added to present an easy to access option for users to try if they so wish. (PR-#9094) Improvements Updates to numba.core.pythonapi. Support for Python C-API functions PyBytes_AsString and PyBytes_AsStringAndSize is added to numba.core.pythonapi.PythonAPI as bytes_as_string and bytes_as_string_and_size methods respectively. (PR-#8462) Support for isinstance is now non-experimental. Support for the isinstance built-in function has moved from being considered an experimental feature to a fully supported feature. (PR-#8911) NumPy Support All modes are supported in numpy.correlate and numpy.convolve. All values for the mode argument to numpy.correlate and numpy.convolve are now supported. (PR-#7543) @@vectorize accommodates arguments implementing __array_ufunc__. Universal functions (ufuncs) created with numba.vectorize will now respect arguments implementing __array_ufunc__ (NEP-13) to allow pre- and post-processing of arguments and return values when the ufunc is called from the interpreter. (PR-#8995) Added support for np.geomspace function. This PR improves on #4074 by adding support for np.geomspace. The current implementation only supports scalar start and stop parameters. (PR-#9068) Added support for np.vsplit, np.hsplit, np.dsplit. This PR improves on #4074 by adding support for np.vsplit, np.hsplit, and np.dsplit. (PR-#9082) Added support for np.row_stack function. Support is added for numpy.row_stack. (PR-#9085) Added support for functions np.polynomial.polyutils.trimseq, as well as functions polyadd, polysub, polymul from np.polynomial.polynomial. Support is added for np.polynomial.polyutils.trimseq, np.polynomial.polynomial.polyadd, np.polynomial.polynomial.polysub, np.polynomial.polynomial.polymul. (PR-#9087) Added support for np.diagflat function. Support is added for numpy.diagflat. (PR-#9113) Added support for np.resize function. Support is added for numpy.resize. (PR-#9118) Add np.trim_zeros Support for np.trim_zeros() is added. (PR-#9074) CUDA Changes Bitwise operation ufunc support for the CUDA target. Support is added for some ufuncs associated with bitwise operation on the CUDA target. Namely: numpy.bitwise_and numpy.bitwise_or numpy.bitwise_not numpy.bitwise_xor numpy.invert numpy.left_shift numpy.right_shift (PR-#8974) Add support for the latest CUDA driver codes. Support is added for the latest set of CUDA driver codes. (PR-#8988) Add NumPy comparison ufunc in CUDA this PR adds support for comparison ufuncs for the CUDA target (eg. numpy.greater, numpy.greater_equal, numpy.less_equal, etc.). (PR-#9007) Report absolute path of libcuda.so on Linux numba -s now reports the absolute path to libcuda.so on Linux, to aid troubleshooting driver issues, particularly on WSL2 where a Linux driver can incorrectly be installed in the environment. (PR-#9034) Add debuginfo support to nvdisasm output. Support is added for debuginfo (source line and inlining information) in functions that make calls through nvdisasm. For example the CUDA dispatcher .inspect_sass method output is now augmented with this information. (PR-#9035) Add CUDA SASS CFG Support This PR adds support for getting the SASS CFG in dot language format. It adds an inspect_sass_cfg() method to CUDADispatcher and the -cfg flag to the nvdisasm command line tool. (PR-#9051) Support NVRTC using the ctypes binding NVRTC can now be used when the ctypes binding is in use, enabling float16, and linking CUDA C / C++ sources without needing the NVIDIA CUDA Python bindings. (PR-#9086) Fix CUDA atomics tests with toolkit 12.2 CUDA 12.2 generates slightly different PTX for some atomics, so the relevant tests are updated to look for the correct instructions when 12.2 is used. (PR-#9088) Bug Fixes Handling of different sized unsigned integer indexes are fixed in numba.typed.List. An issue with the order of truncation/extension and casting of unsigned integer indexes in numba.typed.List has been fixed. (PR-#7262) Prevent invalid fusion This PR fixes an issue in which an array first read in a parfor and later written in the same parfor would only be classified as used in the parfor. When a subsequent parfor also used the same array then fusion of the parfors was happening which should have been forbidden given that that the first parfor was also writing to the array. This PR treats such arrays in a parfor as being both used and defined so that fusion will be prevented. (PR-#7582) The numpy.allclose implementation now correctly handles default arguments. The implementation of numpy.allclose is corrected to use TypingError to report typing errors. (PR-#8885) Add type validation to numpy.isclose. Type validation is added to the implementation of numpy.isclose. (PR-#8944) Fix support for overloading dispatcher with non-compatible first-class functions Fixes an error caused by not handling compilation error during casting of Dispatcher objects into first-class functions. With the fix, users can now overload a dispatcher with non-compatible first-class functions. Refer to https://github.com/numba/numba/issues/9071 for details. (PR-#9072) Support dtype keyword argument in numpy.arange with parallel=True Fixes parfors transformation to support the use of dtype keyword argument in numpy.arange(..., dtype=dtype). (PR-#9095) Fix all @@overloads to use parameter names that match public APIs. Some of the Numba @@overloads for functions in NumPy and Python’s built-ins were written using parameter names that did not match those used in API they were overloading. The result of this being that calling a function with such a mismatch using the parameter names as key-word arguments at the call site would result in a compilation error. This has now been universally fixed throughout the code base and a unit test is running with a best-effort attempt to prevent reintroduction of similar mistakes in the future. Fixed functions include: From Python built-ins: complex From the Python random module: random.seed random.gauss random.normalvariate random.randrange random.randint random.uniform random.shuffle From the numpy module: numpy.argmin numpy.argmax numpy.array_equal numpy.average numpy.count_nonzero numpy.flip numpy.fliplr numpy.flipud numpy.iinfo numpy.isscalar numpy.imag numpy.real numpy.reshape numpy.rot90 numpy.swapaxes numpy.union1d numpy.unique From the numpy.linalg module: numpy.linalg.norm numpy.linalg.cond numpy.linalg.matrix_rank From the numpy.random module: numpy.random.beta numpy.random.chisquare numpy.random.f numpy.random.gamma numpy.random.hypergeometric numpy.random.lognormal numpy.random.pareto numpy.random.randint numpy.random.random_sample numpy.random.ranf numpy.random.rayleigh numpy.random.sample numpy.random.shuffle numpy.random.standard_gamma numpy.random.triangular numpy.random.weibull (PR-#9099) Changes Support for @@numba.extending.intrinsic(prefer_literal=True) In the high level extension API, the prefer_literal option is added to the numba.extending.intrinsic decorator to prioritize the use of literal types when available. This has the same behavior as in the prefer_literal option in the numba.extending.overload decorator. (PR-#6647) Deprecations Deprecation of old-style NUMBA_CAPTURED_ERRORS Added deprecation schedule of NUMBA_CAPTURED_ERRORS=old_style. NUMBA_CAPTURED_ERRORS=new_style will become the default in future releases. Details are documented at https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-old-style-numba-captured-errors (PR-#9090) Pull-Requests PR #6647: Support prefer_literal option for intrinsic decorator (ashutoshvarma sklam) PR #7262: fix order of handling and casting (esc) PR #7543: Support for all modes in np.correlate and np.convolve (jeertmans) PR #7582: Use get_parfor_writes to detect illegal array access that prevents fusion. (DrTodd13) PR #8371: Added binomial distribution (esc kc611) PR #8462: Add PyBytes_AsString and PyBytes_AsStringAndSize (ianna) PR #8633: DOC: Convert vectorize and guvectorize examples to doctests (Matt711) PR #8730: Update dev-docs (sgbaird esc) PR #8792: Added towncrier as a github workflow (kc611) PR #8854: Updated mk_alloc to support Numba-Dpex compute follows data. (mingjie-intel) PR #8861: CUDA: Don’t add device kwarg for jit registry (gmarkall) PR #8871: Don’t return the function in CallConv.decorate_function() (gmarkall) PR #8885: Fix np.allclose not handling default args (guilhermeleobas) PR #8892: Add support for __*matmul__ methods in jitclass (louisamand) PR #8895: CUDA: Enable caching functions that use CG (gmarkall) PR #8906: Add support for reflected dunder methods in jitclass (louisamand) PR #8911: Remove isinstance experimental feature warning (guilhermeleobas) PR #8916: Bump llvmlite requirement to 0.41.0dev0 (sklam) PR #8925: Update release checklist template (sklam) PR #8937: Remove old Website development documentation (esc gmarkall) PR #8944: Add exceptions to np.isclose (guilhermeleobas) PR #8974: CUDA: Add binary ufunc support (Matt711) PR #8976: Fix index URL for ptxcompiler/cubinlinker packages. (bdice) PR #8978: Import MVC packages when using MVCLinker. (bdice) PR #8983: Fix typo in deprecation.rst (dsgibbons) PR #8988: support for latest CUDA driver codes #8363 (s1Sharp) PR #8995: Allow libraries that implement __array_ufunc__ to override DUFunc.__c… (jpivarski) PR #9007: CUDA: Add comparison ufunc support (Matt711) PR #9012: RVSDG-frontend (sklam) PR #9021: update the release checklist following 0.57.1rc1 (esc) PR #9022: fix: update the C++ ABI repo reference (emmanuel-ferdman) PR #9028: Replace use of imp module removed in 3.12 (hauntsaninja) PR #9034: CUDA libs test: Report the absolute path of the loaded libcuda.so on Linux, + other improvements (gmarkall) PR #9035: CUDA: Allow for debuginfo in nvdisasm output (Matt711) PR #9037: Recognize additional functions as being pure or not having side effects. (DrTodd13) PR #9039: Correct git clone link in installation instructions. (ellifteria) PR #9040: Remove NVVM 3.4 and CTK 11.0 / 11.1 support (gmarkall) PR #9046: copy the change log changes for 0.57.1 to main (esc) PR #9050: Update CODEOWNERS (sklam) PR #9051: Add CUDA CFG support (Matt711) PR #9056: adding weekly meeting notes script (esc) PR #9068: Adding np.geomspace (KrisMinchev) PR #9069: Fix towncrier error due to importlib_resources upgrade (sklam) PR #9072: Fix support for overloading dispatcher with non-compatible first-class functions (gmarkall sklam) PR #9074: Add np.trim_zeros (sungraek guilhermeleobas) PR #9082: Add np.vsplit, np.hsplit, and np.dsplit (KrisMinchev) PR #9083: Removed windows 32 references from code and documentation (kc611) PR #9085: Add tests for np.row_stack (KrisMinchev) PR #9086: Support NVRTC using ctypes binding (testhound gmarkall) PR #9087: Add trimseq from np.polynomial.polyutils and polyadd, polysub, polymul from np.polynomial.polynomial (KrisMinchev) PR #9088: Fix: Issue 9063 - CUDA atomics tests failing with CUDA 12.2 (gmarkall) PR #9090: Add deprecation notice for old_style error capturing. (esc sklam) PR #9094: Add support for a ‘max’ level to NUMBA_OPT environment variable. (stuartarchibald) PR #9095: Support dtype keyword in arange_parallel_impl (DrTodd13 sklam) PR #9105: NumPy 1.25 support (PR #9011) continued (gmarkall apmasell) PR #9111: Fixes ReST syntax error in PR#9099 (stuartarchibald gmarkall sklam apmasell) PR #9112: Fixups for PR#9100 (stuartarchibald sklam) PR #9113: Add support for np.diagflat (KrisMinchev) PR #9114: update np min to 122 (stuartarchibald esc) PR #9117: Fixed towncrier template rendering (kc611) PR #9118: Add support for np.resize() (KrisMinchev) PR #9120: Update conda-recipe for numba-rvsdg (sklam) PR #9127: Fix accidental cffi test deps, refactor cffi skipping (gmarkall) PR #9128: Merge rvsdg_frontend branch to main (esc sklam) PR #9152: Fix old_style error capturing deprecation warnings (sklam) PR #9159: Fix uncaught exception in find_file() (gmarkall) PR #9173: Towncrier fixups (Continue #9158 and retarget to main branch) (sklam) PR #9181: Remove extra decrefs in RNG (sklam) PR #9190: Fix issue with incompatible multiprocessing context in test. (stuartarchibald) Authors apmasell ashutoshvarma bdice DrTodd13 dsgibbons ellifteria emmanuel-ferdman esc gmarkall guilhermeleobas hauntsaninja ianna jeertmans jpivarski jtilly kc611 KrisMinchev louisamand Matt711 mingjie-intel s1Sharp sgbaird sklam stuartarchibald sungraek testhound Version 0.57.1 (21 June, 2023) Pull-Requests: PR #8964: fix missing nopython keyword in cuda random module (esc) PR #8965: fix return dtype for np.angle (guilhermeleobas esc) PR #8982: Don’t do the parfor diagnostics pass for the parfor gufunc. (DrTodd13) PR #8996: adding a test for 8940 (esc) PR #8958: resurrect the import, this time in the registry initialization (esc) PR #8947: Introduce internal _isinstance_no_warn (guilhermeleobas esc) PR #8998: Fix 8939 (second attempt) (esc) PR #8978: Import MVC packages when using MVCLinker. (bdice) PR #8895: CUDA: Enable caching functions that use CG (gmarkall) PR #8976: Fix index URL for ptxcompiler/cubinlinker packages. (bdice) PR #9004: Skip MVC test when libraries unavailable (gmarkall esc) PR #9006: link to version support table instead of using explicit versions (esc) PR #9005: Fix: Issue #8923 - avoid spurious device-to-host transfers in CUDA ufuncs (gmarkall) Authors: bdice DrTodd13 esc gmarkall Version 0.57.0 (1 May, 2023) This release continues to add new features, bug fixes and stability improvements to Numba. Please note that this release contains a significant number of both deprecation and pending-deprecation notices with view of making it easier to develop new technology for Numba in the future. Also note that this will be the last release to support Windows 32-bit packages produced by the Numba team. Highlights of core dependency upgrades: Support for Python 3.11 (minimum is moved to 3.8) Support for NumPy 1.24 (minimum is moved to 1.21) Python language support enhancements: Exception classes now support arguments that are not compile time constant. The built-in functions hasattr and getattr are supported for compile time constant attributes. The built-in functions str and repr are now implemented similarly to their Python implementations. Custom __str__ and __repr__ functions can be associated with types and work as expected. Numba’s unicode functionality in str.startswith now supports kwargs start and end. min and max now support boolean types. Support is added for the dict(iterable) constructor. NumPy features/enhancements: The largest set of new features is within the numpy.random.Generator support, the vast majority of commonly used distributions are now supported. Namely: Generator.beta Generator.chisquare Generator.exponential Generator.f Generator.gamma Generator.geometric Generator.integers Generator.laplace Generator.logistic Generator.lognormal Generator.logseries Generator.negative_binomial Generator.noncentral_chisquare Generator.noncentral_f Generator.normal Generator.pareto Generator.permutation Generator.poisson Generator.power Generator.random Generator.rayleigh Generator.shuffle Generator.standard_cauchy Generator.standard_exponential Generator.standard_gamma Generator.standard_normal Generator.standard_t Generator.triangular Generator.uniform Generator.wald Generator.weibull Generator.zipf The nbytes property on NumPy ndarray types is implemented. Nesting of nested-array types is now supported. datetime and timedelta types can be cast to int. F-order iteration is supported in ufunc generation for increased performance when using combinations of predominantly F-order arrays. The following functions are also now supported: np.argpartition np.isclose np.nan_to_num np.new_axis np.union1d Highlights of core changes: A large amount of refactoring has taken place to convert many of Numba’s internal implementations, of both Python and NumPy functions, from the low-level extension API to the high-level extension API (numba.extending). The __repr__ method is supported for Numba types. The default target for applicable functions in the extension API (numba.extending) is now "generic". This means that @@overload* and @@intrinsic functions will by default be accepted by both the CPU and CUDA targets. The use of __getitem__ on Numba types is now supported in compiled code. i.e. types.float64[:, ::1] is now compilable. Performance: The performance of str.find() and str.rfind() has been improved. Unicode support for __getitem__ now avoids allocation and returns a view. The numba.typed.Dict dictionary now accepts an n_keys option to enable allocating the dictionary instance to a predetermined initial size (useful to avoid resizes!). The Numba Run-time (NRT) has been improved in terms of performance and safety: The NRT internal statistics counters are now off by default (removes atomic lock contentions). Debug cache line filling is off by default. The NRT is only compiled once a compilation starts opposed to at function decoration time, this improves import speed. The NRT allocation calls are all made through a “checked” layer by default. CUDA: New NVIDIA hardware and software compatibility / support: Toolkits: CUDA 11.8 and 12, with Minor Version Compatibility for 11.x. Packaging: NVIDIA-packaged CUDA toolkit conda packages. Hardware: Hopper, Ada Lovelace, and AGX Orin. float16 support: Arithmetic operations are now fully supported. A new method, is_fp16_supported(), and device property, supports_float16, for checking the availability of float16 support. Functionality: The high-level extension API is now fully-supported in the CUDA target. Eager compilation of multiple signatures, multiple outputs from generalized ufuncs, and specifying the return type of ufuncs are now supported. A limited set of NumPy ufuncs (trigonometric functions) can now be called inside kernels. Lineinfo quality improvement: enabling lineinfo no longer results in any changes to generated code. Deprecations: The numba.pycc module and everything in it is now pending deprecation. The long awaited full deprecation of object mode fall-back is underway. This change means @@jit with no keyword arguments will eventually alias @@njit. The @@generated_jit decorator is deprecated as the Numba extension API provides a better supported superset of the same functionality, particularly through @@numba.extending.overload. Version support/dependency changes: The setuptools package is now an optional run-time dependency opposed to a required run-time dependency. The TBB threading-layer now requires version 2021.6 or later. LLVM 14 is now supported on all platforms via llvmlite. Pull-Requests: PR #5113: Fix error handling in the Interval extending example (esc eric-wieser) PR #5544: Add support for np.union1d (shangbol gmarkall) PR #7009: Add writable args (dmbelov) PR #7067: Implement np.isclose (guilhermeleobas) PR #7255: CUDA: Support CUDA Toolkit conda packages from NVIDIA (gmarkall) PR #7622: Support fortran loop ordering for ufunc generation (sklam) PR #7733: fix for /tmp/tmp access issues (ChiCheng45) PR #7884: Implement getattr builtin. (stuartarchibald) PR #7885: Adds CUDA FP16 arithmetic operators (testhound) PR #7920: Drop pre-3.7 code path (CPU only) (sklam) PR #8001: CUDA fp16 math functions (testhound gmarkall) PR #8010: Add support for fp16 comparison native operators (testhound) PR #8024: Allow converting NumPy datetimes to int (apmasell) PR #8038: Support for Numpy BitGenerators PR#2: Standard Distributions support (kc611) PR #8040: Support for Numpy BitGenerators PR#3: Advanced Distributions Support. (kc611) PR #8041: Support for Numpy BitGenerators PR#4: Generator().integers() Support. (kc611) PR #8042: Support for NumPy BitGenerators PR#5: Generator Shuffling Methods. (kc611) PR #8061: Migrate random glue_lowering to overload where easy (apmasell) PR #8106: Remove injection of atomic JIT functions into NRT memsys. (stuartarchibald) PR #8120: Support nesting of nested array types (gmarkall) PR #8134: Support non-constant exception values in JIT (guilhermeleobas sklam) PR #8147: Adds size variable at runtime for arrays that cannot be inferred (njriasan) PR #8154: Testhound/native cast 8138 (testhound) PR #8158: adding -pthread for linux-ppc64le in setup.py (esc) PR #8164: remove myself from automatic reviewer assignment (esc) PR #8167: CUDA: Facilitate and document passing arrays / pointers to foreign functions (gmarkall) PR #8180: CUDA: Initial support for Minor Version Compatibility (gmarkall) PR #8183: Add n_keys option to Dict.empty() (stefanfed gmarkall) PR #8198: Update the release template to include updating the version table. (stuartarchibald) PR #8200: Make the NRT use the “unsafe” allocation API by default. (stuartarchibald) PR #8201: Bump llvmlite dependency to 0.40.dev0 for Numba 0.57.0dev0 (stuartarchibald) PR #8207: development tag should be in monofont (esc) PR #8212: release checklist: include a note to ping @@RC_testers on discourse (esc) PR #8216: chore: Set permissions for GitHub actions (naveensrinivasan) PR #8217: Fix syntax in docs (jorgepiloto) PR #8220: Added the interval example as doctest (kc611) PR #8221: CUDA stubs docstring: Replace illegal escape sequence (gmarkall) PR #8228: Fix typo in @@vectorize docstring and a NumPy spelling. (stuartarchibald) PR #8229: Remove mk_unique_var in inline_closurecall.py (sklam) PR #8234: Replace @@overload_glue by @@overload for 20 NumPy functions (guilhermeleobas) PR #8235: Make the NRT stats counters optional. (stuartarchibald) PR #8238: Advanced Indexing Support #1 (kc611) PR #8240: Add get_shared_mem_per_block method to Dispatcher (testhound) PR #8241: Reorder typeof checks to avoid infinite loops on StructrefProxy __hash__ (DannyWeitekamp) PR #8243: Add a note to reference/numpysupported.rst () PR #8245: Fix links in CONTRIBUTING.md () PR #8247: Fix issue 8127 (bszollosinagy) PR #8250: Fix issue 8161 (bszollosinagy) PR #8253: CUDA: Verify NVVM IR prior to compilation (gmarkall) PR #8255: CUDA: Make numba.cuda.tests.doc_examples.ffi a module to fix #8252 (gmarkall) PR #8256: Migrate linear algebra functions from glue_lowering (apmasell) PR #8258: refactor np.where to use overload (guilhermeleobas) PR #8259: Add np.broadcast_to(scalar_array, ()) (guilhermeleobas) PR #8264: remove mk_unique_var from parfor_lowering_utils.py (guilhermeleobas) PR #8265: Remove mk_unique_var from array_analysis.py (guilhermeleobas) PR #8266: Remove mk_unique_var in untyped_passes.py (guilhermeleobas) PR #8267: Fix segfault for invalid axes in np.split (aseyboldt) PR #8271: Implement some CUDA intrinsics with @@overload, @@overload_attribute, and @@intrinsic (gmarkall) PR #8274: Update version support table doc for 0.56. (stuartarchibald) PR #8275: Update CHANGE_LOG for 0.56.0 final (stuartarchibald) PR #8283: Clean up / remove support for old NumPy versions (gmarkall) PR #8287: Drop CUDA 10.2 (gmarkall) PR #8289: Revert #8265. (stuartarchibald) PR #8290: CUDA: Replace use of deprecated NVVM IR features, questionable constructs (gmarkall) PR #8292: update checklist (esc) PR #8294: CUDA: Add trig ufunc support (gmarkall) PR #8295: Add get_const_mem_size method to Dispatcher (testhound gmarkall) PR #8297: Add __name__ attribute to CUDAUFuncDispatcher and test case (testhound) PR #8299: Fix build for mingw toolchain (Biswa96) PR #8302: CUDA: Revert numba_nvvm intrinsic name workaround (gmarkall) PR #8308: CUDA: Support for multiple signatures (gmarkall) PR #8315: Add get_local_mem_per_thread method to Dispatcher (testhound) PR #8319: Bump minimum supported Python version to 3.8 (esc stuartarchibald jamesobutler) PR #8320: Add __name__ support for GUFuncs (testhound) PR #8321: Fix literal_unroll pass erroneously exiting on non-conformant loop. (stuartarchibald) PR #8325: Remove use of mk_unique_var in stencil.py (bszollosinagy) PR #8326: Remove mk_unique_var from parfor_lowering.py (guilhermeleobas) PR #8331: Extend docs with info on how to call C functions from Numba (guilhermeleobas) PR #8334: Add dict(*iterable) constructor (guilhermeleobas) PR #8335: Remove deprecated pycc script and related source. (stuartarchibald) PR #8336: Fix typos of “Generalized” in GUFunc-related code (gmarkall) PR #8338: Calculate reductions before fusion so that use of reduction vars can stop fusion. (DrTodd13) PR #8339: Fix #8291 parfor leak of redtoset variable (sklam) PR #8341: CUDA: Support multiple outputs for Generalized Ufuncs (gmarkall) PR #8343: Eliminate references to type annotation in compile_ptx (testhound) PR #8348: Add get_max_threads_per_block method to Dispatcher (testhound) PR #8354: pin setuptools to < 65 and switch from mamba to conda on RTD (esc gmarkall) PR #8357: Clean up the buildscripts directory. (stuartarchibald) PR #8359: adding warnings about cache behaviour (luk-f-a) PR #8368: Remove glue_lowering in random math that requires IR (apmasell) PR #8376: Fix issue 8370 (bszollosinagy) PR #8387: Add support for compute capability in IR Lowering (testhound) PR #8388: Remove more references to the pycc binary. (stuartarchibald) PR #8389: Make C++ extensions compile with correct compiler (apmasell) PR #8390: Use NumPy logic for lessthan in sort to move NaNs to the back. (sklam) PR #8401: Remove Cuda toolkit version check (testhound) PR #8415: Refactor numba.np.arraymath methods from lower_builtins to overloads (kc611) PR #8418: Fixes ravel failure on 1d arrays (#5229) (cako) PR #8421: Update release checklist: add a task to check dependency pinnings on subsequent releases (e.g. PATCH) (esc) PR #8422: Switch public CI builds to use gdb from conda packages. (stuartarchibald) PR #8423: Remove public facing and CI references to 32 bit linux support. (stuartarchibald, in addition, we are grateful for the contribution of jamesobutler towards a similar goal in PR #8319) PR #8425: Post 0.56.2 cleanup (esc) PR #8427: Shorten the time to verify test discovery. (stuartarchibald) PR #8429: changelog generator script (esc) PR #8431: Replace @@overload_glue by @@overload for np.linspace and np.take (guilhermeleobas) PR #8432: Refactor carray/farray to use @@overload (guilhermeleobas) PR #8435: Migrate np.atleast_? functions from glue_lowering to overload (apmasell) PR #8438: Make the initialisation of the NRT more lazy for the njit decorator. (stuartarchibald) PR #8439: Update the contributing docs to include a policy on formatting changes. (stuartarchibald) PR #8440: [DOC]: Replaces icc_rt with intel-cmplr-lib-rt (oleksandr-pavlyk) PR #8442: Implement hasattr(), str() and repr(). (stuartarchibald) PR #8446: add version info in ImportError’s (raybellwaves) PR #8450: remove GitHub username from changelog generation script (esc) PR #8467: Convert implementations using generated_jit to overload (gmarkall) PR #8468: Reference test suite in installation documentation (apmasell) PR #8469: Correctly handle optional types in parfors lowering (apmasell) PR #8473: change the include style in _pymodule.h and remove unused or duplicate headers in two header files () PR #8476: Make setuptools optional at runtime. (stuartarchibald) PR #8490: Restore installing SciPy from defaults instead of conda-forge on public CI (esc) PR #8494: Remove context.compile_internal where easy on numba/cpython/cmathimpl.py (guilhermeleobas) PR #8495: Removes context.compile_internal where easy on numba/cpython/listobj.py (guilhermeleobas) PR #8496: Rewrite most of the set API to use overloads (guilhermeleobas) PR #8499: Deprecate numba.generated_jit (stuartarchibald) PR #8508: This updates the release checklists to capture some more checks. (stuartarchibald) PR #8513: Added support for numpy.newaxis (kc611) PR #8517: make some typedlist C-APIs public () PR #8518: Adjust stencil tests to use hardcoded python source opposed to AST. (stuartarchibald) PR #8520: Added noncentral-chisquared, noncentral-f and logseries distributions (kc611) PR #8522: Import jitclass from numba.experimental in jitclass documentation (armgabrielyan) PR #8524: Fix grammar in stencil.rst (armgabrielyan) PR #8525: Making CUDA specific datamodel manager (sklam) PR #8526: Fix broken url (Nimrod0901) PR #8527: Fix grammar in troubleshoot.rst (armgabrielyan) PR #8532: Vary NumPy version on gpuCI (gmarkall) PR #8535: LLVM14 (apmasell) PR #8536: Fix fusion bug. (DrTodd13) PR #8539: Fix #8534, np.broadcast_to should update array size attr. (stuartarchibald) PR #8541: Remove restoration of “free” channel in Azure CI windows builds. (stuartarchibald) PR #8542: CUDA: Make arg optional for Stream.add_callback() (gmarkall) PR #8544: Remove reliance on npy_ ufunc loops. (stuartarchibald) PR #8545: Py3.11 basic support (esc sklam) PR #8547: [Unicode] Add more string view usages for unicode operations () PR #8549: Fix rstcheck in Azure CI builds, update sphinx dep and docs to match (stuartarchibald) PR #8550: Changes how tests are split between test instances (apmasell) PR #8554: Make target for @@overload have ‘generic’ as default. (stuartarchibald gmarkall) PR #8557: [Unicode] support startswith with args, start and end. () PR #8566: Update workqueue abort message on concurrent access. (stuartarchibald) PR #8572: CUDA: Reduce memory pressure from local memory tests (gmarkall) PR #8579: CUDA: Add CUDA 11.8 / Hopper support and required fixes (gmarkall) PR #8580: adding note about doing a wheel test build prior to tagging (esc) PR #8583: Skip tests that contribute to M1 RuntimeDyLd Assertion error (sklam) PR #8587: Remove unused refcount removal code, clean core/cpu.py module. (stuartarchibald) PR #8588: Remove lowering extension hooks, replace with pass infrastructure. (stuartarchibald) PR #8590: Py3.11 support continues (sklam) PR #8592: fix failure of test_cache_invalidate due to read-only install (tpwrules) PR #8593: Adjusted ULP precesion for noncentral distribution test (kc611) PR #8594: Fix various CUDA lineinfo issues (gmarkall) PR #8597: Prevent use of NumPy’s MaskedArray. (stuartarchibald) PR #8598: Setup Azure CI to test py3.11 (sklam) PR #8600: Chrome trace timestamp should be in microseconds not seconds. (sklam) PR #8602: Throw error for unsupported dunder methods (apmasell) PR #8605: Support for CUDA fp16 math functions (part 1) (testhound) PR #8606: [Doc] Make the RewriteArrayExprs doc more precise () PR #8619: Added flat iteration logic for random distributions (kc611) PR #8623: Adds support for np.nan_to_num (thomasjpfan) PR #8624: DOC: Add guvectorize scalar return example (Matt711) PR #8625: Refactor test_ufuncs (gmarkall) PR #8626: [unicode-PERF]: use optmized BM algorithm to replace the brute-force finder (dlee992) PR #8630: Fix #8628: Don’t test math.trunc with non-float64 NumPy scalars (gmarkall) PR #8634: Add new method is_fp16_supported (testhound) PR #8636: CUDA: Skip test_ptds on Windows (gmarkall) PR #8639: Python 3.11 - fix majority of remaining test failures. (stuartarchibald) PR #8644: Fix bare reraise support (sklam) PR #8649: Remove numba.core.overload_glue module. (apmasell) PR #8659: Preserve module name of jitted class (neilflood) PR #8661: Make external compiler discovery lazy in the test suite. (stuartarchibald) PR #8662: Add support for .nbytes accessor for numpy arrays (alanhdu) PR #8666: Updates for Python 3.8 baseline/Python 3.11 migration (stuartarchibald) PR #8673: Enable the CUDA simulator tests on Windows builds in Azure CI. (stuartarchibald) PR #8675: Make always_run test decorator a tag and improve shard tests. (stuartarchibald) PR #8677: Add support for min and max on boolean types. (DrTodd13) PR #8680: Adjust flake8 config to be compatible with flake8=6.0.0 (thomasjpfan) PR #8685: Implement __repr__ for numba types (luk-f-a) PR #8691: NumPy 1.24 (gmarkall) PR #8697: Close stale issues after 7 days (gmarkall) PR #8701: Relaxed ULP testing precision for NumPy Generator tests across all systems (kc611) PR #8702: Supply concrete timeline for objmode fallback deprecation/removal. (stuartarchibald) PR #8706: Fix doctest for @@vectorize (sklam) PR #8711: Python 3.11 tracing support (continuation of #8670). (AndrewVallette sklam) PR #8716: CI: Use set -e in “Before Install” step and fix install (gmarkall) PR #8720: Enable coverage for subprocess testing (sklam) PR #8723: Check for void return type in cuda.compile_ptx (brandonwillard) PR #8726: Make Numba dependency check run ahead of Numba internal imports. (stuartarchibald) PR #8728: Fix flake8 checks since upgrade to flake8=6.x (stuartarchibald) PR #8729: Run flake8 CI step in multiple processes. (stuartarchibald) PR #8732: Add numpy argpartition function support () PR #8735: Update bot to close PRs waiting on authors for more than 3 months (guilhermeleobas) PR #8736: Implement np.lib.stride_tricks.sliding_window_view () PR #8744: Update CtypesLinker::add_cu error message to include fp16 usage (testhound gmarkall) PR #8746: Fix failing test_dispatcher test case (testhound) PR #8748: Suppress known test failures for py3.11 (sklam) PR #8751: Recycle test runners more aggressively (apmasell) PR #8752: Flake8 fixes for py311 branch (esc sklam) PR #8760: Bump llvmlite PR in py3.11 branch testing (sklam) PR #8764: CUDA tidy-up: remove some unneeded methods (gmarkall) PR #8765: BLD: remove distutils (fangchenli) PR #8766: Stale bot: Use abandoned - stale label for closed PRs (gmarkall) PR #8771: Update vendored Versioneer from 0.14 to 0.28 (oscargus gmarkall) PR #8775: Revert PR#8751 for buildfarm stability (sklam) PR #8780: Improved documentation for Atomic CAS (MiloniAtal) PR #8781: Ensure gc.collect() is called before checking refcount in tests. (sklam) PR #8782: Changed wording of the escape error (MiloniAtal) PR #8786: Upgrade stale GitHub action (apmasell) PR #8788: CUDA: Fix returned dtype of vectorized functions (Issue #8400) (gmarkall) PR #8790: CUDA compare and swap with index (ianthomas23) PR #8795: Add pending-deprecation warnings for numba.pycc (stuartarchibald) PR #8802: Move the minimum supported NumPy version to 1.21 (stuartarchibald) PR #8803: Attempted fix to #8789 by changing compile_ptx to accept a signature instead of argument tuple (KyanCheung) PR #8804: Split parfor pass into 3 parts (DrTodd13) PR #8809: Update LLVM versions for 0.57 release (apmasell) PR #8810: Fix llvmlite dependency in meta.yaml (sklam) PR #8816: Fix some buildfarm test failures (sklam) PR #8819: Support “static” __getitem__ on Numba types in @@njit code. (stuartarchibald) PR #8822: Merge py3.11 branch to main (esc AndrewVallette stuartarchibald sklam) PR #8826: CUDA CFFI test: conditionally require cffi module (gmarkall) PR #8831: Redo py3.11 sync branch with main (sklam) PR #8833: Fix typeguard import hook location. (stuartarchibald) PR #8836: Fix failing typeguard test. (stuartarchibald) PR #8837: Update AzureCI matrix for Python 3.11/NumPy 1.21..1.24 (stuartarchibald) PR #8839: Add Dynamic Shared Memory example. (k1m190r) PR #8842: Fix buildscripts, setup.py, docs for setuptools becoming optional. (stuartarchibald) PR #8843: Pin typeguard to 3.0.1 in AzureCI. (stuartarchibald) PR #8848: added lifted loops to glossary term (cherieliu) PR #8852: Disable SLP vectorisation due to miscompilations. (stuartarchibald) PR #8855: DOC: pip into double backticks in installing.rst (F3eQnxN3RriK) PR #8856: Update TBB to use >= 2021.6 by default. (kozlov-alexey stuartarchibald) PR #8858: Update deprecation notice for objmode fallback RE @@jit use. (stuartarchibald) PR #8864: Remove obsolete deprecation notices (gmarkall) PR #8866: Revise CUDA deprecation notices (gmarkall) PR #8869: Update CHANGE_LOG for 0.57.0rc1 (stuartarchibald esc gmarkall) PR #8870: Fix opcode “spelling” change since Python 3.11 in CUDA debug test. (stuartarchibald) PR #8879: Remove use of compile_isolated from generator tests. (stuartarchibald) PR #8880: Fix missing dependency guard on pyyaml in test_azure_config. (stuartarchibald) PR #8881: Replace use of compile_isolated in test_obj_lifetime (sklam) PR #8884: Pin llvmlite and NumPy on release branch (sklam) PR #8887: Update PyPI supported version tags (bryant1410) PR #8896: Remove codecov install (now deleted from PyPI) (gmarkall) PR #8902: Enable CALL_FUNCTION_EX fix for py3.11 (sklam) PR #8907: Work around issue #8898. Defer exp2 (and log2) calls to Numba internal symbols. (stuartarchibald) PR #8909: Fix #8903. NumbaDeprecationWarning``s raised from ``@@{gu,}vectorize. (stuartarchibald) PR #8929: Update CHANGE_LOG for 0.57.0 final. (stuartarchibald) PR #8930: Fix year in change log (jtilly) PR #8932: Fix 0.57 release changelog (sklam) Authors: alanhdu AndrewVallette apmasell armgabrielyan aseyboldt Biswa96 brandonwillard bryant1410 bszollosinagy cako cherieliu ChiCheng45 DannyWeitekamp dlee992 dmbelov DrTodd13 eric-wieser esc F3eQnxN3RriK fangchenli gmarkall guilhermeleobas ianthomas23 jamesobutler jorgepiloto jtilly k1m190r kc611 kozlov-alexey KyanCheung luk-f-a Matt711 MiloniAtal naveensrinivasan neilflood Nimrod0901 njriasan oleksandr-pavlyk oscargus raybellwaves shangbol sklam stefanfed stuartarchibald testhound thomasjpfan tpwrules Version 0.56.4 (3 November, 2022) This is a bugfix release to fix a regression in the CUDA target in relation to the .view() method on CUDA device arrays that is present when using NumPy version 1.23.0 or later. Pull-Requests: PR #8537: Make ol_compatible_view accessible on all targets (gmarkall) PR #8552: Update version support table for 0.56.4. (stuartarchibald) PR #8553: Update CHANGE_LOG for 0.56.4 (stuartarchibald) PR #8570: Release 0.56 branch: Fix overloads with target="generic" for CUDA (gmarkall) PR #8571: Additional update to CHANGE_LOG for 0.56.4 (stuartarchibald) Authors: gmarkall stuartarchibald Version 0.56.3 (13 October, 2022) This is a bugfix release to remove the version restriction applied to the setuptools package and to fix a bug in the CUDA target in relation to copying zero length device arrays to zero length host arrays. Pull-Requests: PR #8475: Remove setuptools version pin (gmarkall) PR #8482: Fix #8477: Allow copies with different strides for 0-length data (gmarkall) PR #8486: Restrict the TBB development package to supported version in Azure. (stuartarchibald) PR #8503: Update version support table for 0.56.3 (stuartarchibald) PR #8504: Update CHANGE_LOG for 0.56.3 (stuartarchibald) Authors: gmarkall stuartarchibald Version 0.56.2 (1 September, 2022) This is a bugfix release that supports NumPy 1.23 and fixes CUDA function caching. Pull-Requests: PR #8239: Add decorator to run a test in a subprocess (stuartarchibald) PR #8276: Move Azure to use macos-11 (stuartarchibald) PR #8310: CUDA: Fix Issue #8309 - atomics don’t work on complex components (Graham Markall) PR #8342: Upgrade to ubuntu-20.04 for azure pipeline CI (jamesobutler) PR #8356: Update setup.py, buildscripts, CI and docs to require setuptools<60 (stuartarchibald) PR #8374: Don’t pickle LLVM IR for CUDA code libraries (Graham Markall) PR #8377: Add support for NumPy 1.23 (stuartarchibald) PR #8384: Move strace() check into tests that actually need it (stuartarchibald) PR #8386: Fix the docs for numba.get_thread_id (stuartarchibald) PR #8407: Pin NumPy version to 1.18-1.24 (Andre Masella) PR #8411: update version support table for 0.56.1 (esc) PR #8412: Create changelog for 0.56.1 (Andre Masella) PR #8413: Fix Azure CI for NumPy 1.23 and use conda-forge scipy (Siu Kwan Lam) PR #8414: Hotfix for 0.56.2 (Siu Kwan Lam) Authors: Andre Masella esc Graham Markall jamesobutler Siu Kwan Lam stuartarchibald Version 0.56.1 (NO RELEASE) The release was skipped due to issues during the release process. Version 0.56.0 (25 July, 2022) This release continues to add new features, bug fixes and stability improvements to Numba. Please note that this will be the last release that has support for Python 3.7 as the next release series (Numba 0.57) will support Python 3.11! Also note that, this will be the last release to support linux-32 packages produced by the Numba team. Python language support enhancements: Previously missing support for large, in-line dictionaries and internal calls to functions with large numbers of keyword arguments in Python 3.10 has been added. operator.mul now works for list s. Literal slices, e.g. slice(1, 10, 2) can be returned from nopython mode functions. The len function now works on dict_keys, dict_values and dict_items . Numba’s set implementation now supports reference counted items e.g. strings. Numba specific feature enhancements: The experimental jitclass feature gains support for a large number of builtin methods e.g. declaring __hash__ or __getitem__ for a jitclass type. It’s now possible to use @@vectorize on an already @@jit family decorated function. Name mangling has been updated to emit compiled function names that exactly match the function name in Python. This means debuggers, like GDB, can be set to break directly on Python function names. A GDB “pretty printing” support module has been added, when loaded into GDB Numba’s internal representations of Python/NumPy types are rendered inside GDB as they would be in Python. An experimental option is added to the @@jit family decorators to entirely turn off LLVM’s optimisation passes for a given function (see _dbg_optnone kwarg in the @@jit decorator family). A new environment variable is added NUMBA_EXTEND_VARIABLE_LIFETIMES, which if set will extend the lifetime of variables to the end of their basic block, this to permit a debugging experience in GDB similar to that found in compiled C/C++/Fortran code. NumPy features/enhancements: Initial support for passing, using and returning numpy.random.Generator instances has been added, this currently includes support for the random distribution. The broadcasting functions np.broadcast_shapes and np.broadcast_arrays are now supported. The min and max functions now work with np.timedelta64 and np.datetime64 types. Sorting multi-dimensional arrays along the last axis is now supported in np.sort(). The np.clip function is updated to accept NumPy arrays for the a_min and a_max arguments. The NumPy allocation routines (np.empty , np.ones etc.) support shape arguments specified using members of enum.IntEnum s. The function np.random.noncentral_chisquare is now supported. The performance of functions np.full and np.ones has been improved. Parallel Accelerator enhancements: The parallel=True functionality is enhanced through the addition of the functions numba.set_parallel_chunksize and numba.get_parallel_chunksize to permit a more fine grained scheduling of work defined in a parallel region. There is also support for adjusting the chunksize via a context manager. The ID of a thread is now defined to be predictable and within a known range, it is available through calling the function numba.get_thread_id. The performance of @@stencil s has been improved in both serial and parallel execution. CUDA enhancements: New functionality: Self-recursive device functions. Vector type support (float4, int2, etc.). Shared / local arrays of extension types can now be created. Support for linking CUDA C / C++ device functions into Python kernels. PTX generation for Compute Capabilities 8.6 and 8.7 - e.g. RTX A series, GTX 3000 series. Comparison operations for float16 types. Performance improvements: Context queries are no longer made during launch configuration. Launch configurations are now LRU cached. On-disk caching of CUDA kernels is now supported. Documentation: many new examples added. Docs: Numba now has an official “mission statement”. There’s now a “version support table” in the documentation to act as an easy to use, single reference point, for looking up information about Numba releases and their required/supported dependencies. General Enhancements: Numba imports more quickly in environments with large numbers of packages as it now uses importlib-metadata for querying other packages. Emission of chrome tracing output is now supported for the internal compilation event handling system. This release is tested and known to work when using the Pyston Python interpreter. Pull-Requests: PR #5209: Use importlib to load numba extensions (Stepan Rakitin Graham Markall stuartarchibald) PR #5877: Jitclass builtin methods (Ethan Pronovost Graham Markall) PR #6490: Stencil output allocated with np.empty now and new code to initialize the borders. (Todd A. Anderson) PR #7005: Make numpy.searchsorted match NumPy when first argument is unsorted (Brandon T. Willard) PR #7363: Update cuda.local.array to clarify “simple constant expression” (e.g. no NumPy ints) (Sterling Baird) PR #7364: Removes an instance of signed integer overflow undefined behaviour. (Tobias Sargeant) PR #7537: Add chrome tracing (Hadia Ahmed Siu Kwan Lam) PR #7556: Testhound/fp16 comparison (Michael Collison Graham Markall) PR #7586: Support for len on dict.keys, dict.values, and dict.items (Nick Riasanovsky) PR #7617: Numba gdb-python extension for printing (stuartarchibald) PR #7619: CUDA: Fix linking with PTX when compiling lazily (Graham Markall) PR #7621: Add support for linking CUDA C / C++ with @@cuda.jit kernels (Graham Markall) PR #7625: Combined parfor chunking and caching PRs. (stuartarchibald Todd A. Anderson Siu Kwan Lam) PR #7651: DOC: pypi and conda-forge badges (Ray Bell) PR #7660: Add support for np.broadcast_arrays (Guilherme Leobas) PR #7664: Flatten mangling dicts into a single dict (Graham Markall) PR #7680: CUDA Docs: include example calling slow matmul (Graham Markall) PR #7682: performance improvements to np.full and np.ones (Rishi Kulkarni) PR #7684: DOC: remove incorrect warning in np.random reference (Rishi Kulkarni) PR #7685: Don’t convert setitems that have dimension mismatches to parfors. (Todd A. Anderson) PR #7690: Implemented np.random.noncentral_chisquare for all size arguments (Rishi Kulkarni) PR #7695: IntEnumMember support for np.empty, np.zeros, and np.ones (Benjamin Graham) PR #7699: CUDA: Provide helpful error if the return type is missing for declare_device (Graham Markall) PR #7700: Support for scalar arguments in Np.ascontiguousarray (Dhruv Patel) PR #7703: Ignore unsupported types in ShapeEquivSet._getnames() (Benjamin Graham) PR #7704: Move the type annotation pass to post legalization. (stuartarchibald) PR #7709: CUDA: Fixes missing type annotation pass following #7704 (stuartarchibald) PR #7712: Fixing issue 7693 (stuartarchibald Graham Markall luk-f-a) PR #7714: Support for boxing SliceLiteral type (Nick Riasanovsky) PR #7718: Bump llvmlite dependency to 0.39.0dev0 for Numba 0.56.0dev0 (stuartarchibald) PR #7724: Update URLs in error messages to refer to RTD docs. (stuartarchibald) PR #7728: Document that AOT-compiled functions do not check arg types (Graham Markall) PR #7729: Handle Omitted/OmittedArgDataModel in DI generation. (stuartarchibald) PR #7732: update release checklist following 0.55.0 RC1 (esc) PR #7736: Update CHANGE_LOG for 0.55.0 final. (stuartarchibald) PR #7740: CUDA Python 11.6 support (Graham Markall) PR #7744: Fix issues with locating/parsing source during DebugInfo emission. (stuartarchibald) PR #7745: Fix the release year for Numba 0.55 change log entry. (stuartarchibald) PR #7748: Fix #7713: Ensure _prng_random_hash return has correct bitwidth (Graham Markall) PR #7749: Refactor threading layer priority tests to not use stdout/stderr (stuartarchibald) PR #7752: Fix #7751: Use original filename for array exprs (Graham Markall) PR #7755: CUDA: Deprecate support for CC < 5.3 and CTK < 10.2 (Graham Markall) PR #7763: Update Read the Docs configuration (automatic) (readthedocs-assistant) PR #7764: Add dbg_optnone and dbg_extend_lifetimes flags (Siu Kwan Lam) PR #7771: Move function unique ID to abi-tags (stuartarchibald Siu Kwan Lam) PR #7772: CUDA: Add Support to Creating StructModel Array (Michael Wang) PR #7776: Updates coverage.py config (stuartarchibald) PR #7777: Remove reference existing issue from GH template. (stuartarchibald) PR #7778: Remove long deprecated flags from the CLI. (stuartarchibald) PR #7780: Fix sets with reference counted items (Benjamin Graham) PR #7782: adding reminder to check on deprecations (esc) PR #7783: remove upper limit on Python version (esc) PR #7786: Remove dependency on intel-openmp for OSX (stuartarchibald) PR #7788: Avoid issue with DI gen for arrayexprs. (stuartarchibald) PR #7796: update change-log for 0.55.1 (esc) PR #7797: prune README (esc) PR #7799: update the release checklist post 0.55.1 (esc) PR #7801: add sdist command and umask reminder (esc) PR #7804: update local references from master -> main (esc) PR #7805: Enhance source line finding logic for debuginfo (Siu Kwan Lam) PR #7809: Updates the gdb configuration to accept a binary name or a path. (stuartarchibald) PR #7813: Extend parfors test timeout for aarch64. (stuartarchibald) PR #7814: CUDA Dispatcher refactor (Graham Markall) PR #7815: CUDA Dispatcher refactor 2: inherit from dispatcher.Dispatcher (Graham Markall) PR #7817: Update intersphinx URLs for NumPy and llvmlite. (stuartarchibald) PR #7823: Add renamed vars to callee scope such that it is self consistent. (stuartarchibald) PR #7829: CUDA: Support Enum/IntEnum in Kernel (Michael Wang) PR #7833: Add version support information table to docs. (stuartarchibald) PR #7835: Fix pickling error when module cannot be imported (idorrington) PR #7836: min() and max() support for np.datetime and np.timedelta (Benjamin Graham) PR #7837: Initial refactoring of parfor reduction lowering (Siu Kwan Lam) PR #7845: change time.time() to time.perf_counter() in docs (Nopileos2) PR #7846: Fix CUDA enum vectorize test on Windows (Graham Markall) PR #7848: Support for int * list (Nick Riasanovsky) PR #7850: CUDA: Pass fastmath compiler flag down to compile_ptx and compile_device; Improve fastmath tests (Michael Wang) PR #7855: Ensure np.argmin/no.argmax return type is intp (stuartarchibald) PR #7858: CUDA: Deprecate ptx Attribute and Update Tests (Graham Markall Michael Wang) PR #7861: Fix a spelling mistake in README (Zizheng Guo) PR #7864: Fix cross_iter_dep check. (Todd A. Anderson) PR #7865: Remove add_user_function (Graham Markall) PR #7866: Support for large numbers of args/kws with Python 3.10 (Nick Riasanovsky) PR #7878: CUDA: Remove some deprecated support, add CC 8.6 and 8.7 (Graham Markall) PR #7893: Use uuid.uuid4() as the key in serialization. (stuartarchibald) PR #7895: Remove use of llvmlite.llvmpy (Andre Masella) PR #7898: Skip test_ptds under cuda-memcheck (Graham Markall) PR #7901: Pyston compatibility for the test suite (Kevin Modzelewski) PR #7904: Support m1 (esc) PR #7911: added sys import (Nightfurex) PR #7915: CUDA: Fix test checking debug info rendering. (stuartarchibald) PR #7918: Add JIT examples to CUDA docs (brandon-b-miller Graham Markall) PR #7919: Disallow //= reductions in pranges. (Todd A. Anderson) PR #7924: Retain non-modified index tuple components. (Todd A. Anderson) PR #7939: Fix rendering in feature request template. (stuartarchibald) PR #7940: Implemented np.allclose in numba/np/arraymath.py (Gagandeep Singh) PR #7941: Remove debug dump output from closure inlining pass. (stuartarchibald) PR #7946: instructions for creating a build environment were outdated (esc) PR #7949: Add Cuda Vector Types (Michael Wang) PR #7950: mission statement (esc) PR #7956: Stop using pip for 3.10 on public ci (Revert “start testing Python 3.10 on public CI”) (esc) PR #7957: Use cloudpickle for disk caches (Siu Kwan Lam) PR #7958: numpy.clip accept numpy.array for a_min, a_max (Gagandeep Singh) PR #7959: Permit a new array model to have a super set of array model fields. (stuartarchibald) PR #7961: numba.typed.typeddict.Dict.get uses castedkey to avoid returning default value even if the key is present (Gagandeep Singh) PR #7963: remove the roadmap from the sphinx based docs (esc) PR #7964: Support for large constant dictionaries in Python 3.10 (Nick Riasanovsky) PR #7965: Use uuid4 instead of PID in cache temp name to prevent collisions. (stuartarchibald) PR #7971: lru cache for configure call (Tingkai Liu) PR #7972: Fix fp16 support for cuda shared array (Michael Collison Graham Markall) PR #7986: Small caching refactor to support target cache implementations (Graham Markall) PR #7994: Supporting multidimensional arrays in quick sort (Gagandeep Singh Siu Kwan Lam) PR #7996: Fix binding logic in @@overload_glue. (stuartarchibald) PR #7999: Remove @@overload_glue for NumPy allocators. (stuartarchibald) PR #8003: Add np.broadcast_shapes (Guilherme Leobas) PR #8004: CUDA fixes for Windows (Graham Markall) PR #8014: Fix support for {real,imag} array attrs in Parfors. (stuartarchibald) PR #8016: [Docs] [Very Minor] Make numba.jit boundscheck doc line consistent (Kyle Martin) PR #8017: Update FAQ to include details about using debug-only option (Guilherme Leobas) PR #8027: Support for NumPy 1.22 (stuartarchibald) PR #8031: Support for Numpy BitGenerators PR#1 - Core Generator Support (Kaustubh) PR #8035: Fix a couple of typos RE implementation (stuartarchibald) PR #8037: CUDA self-recursion tests (Graham Markall) PR #8044: Make Python 3.10 kwarg peephole less restrictive (Nick Riasanovsky) PR #8046: Fix caching test failures (Siu Kwan Lam) PR #8049: support str(bool) syntax (LI Da) PR #8052: Ensure pthread is linked in when building for ppc64le. (Siu Kwan Lam) PR #8056: Move caching tests from test_dispatcher to test_caching (Graham Markall) PR #8057: Fix coverage checking (Graham Markall) PR #8064: Rename “nb:run_pass” to “numba:run_pass” and document it. (Siu Kwan Lam) PR #8065: Fix PyLowering mishandling starargs (Siu Kwan Lam) PR #8068: update changelog for 0.55.2 (esc) PR #8077: change return type of np.broadcast_shapes to a tuple (Guilherme Leobas) PR #8080: Fix windows test failure due to timeout when the machine is slow poss… (Siu Kwan Lam) PR #8081: Fix erroneous array count in parallel gufunc kernel generation. (stuartarchibald) PR #8089: Support on-disk caching in the CUDA target (Graham Markall) PR #8097: Exclude libopenblas 0.3.20 on osx-arm64 (esc) PR #8099: Fix Py_DECREF use in case of error state (for devicearray). (stuartarchibald) PR #8102: Combine numpy run_constrained in meta.yaml to the run requirements (Siu Kwan Lam) PR #8109: Pin TBB support with respect to incompatible 2021.6 API. (stuartarchibald) PR #8118: Update release checklists post 0.55.2 (esc) PR #8123: Fix CUDA print tests on Windows (Graham Markall) PR #8124: Add explicit checks to all allocators in the NRT. (stuartarchibald) PR #8126: Mark gufuncs as having mutable outputs (Andre Masella) PR #8133: Fix #8132. Regression in Record.make_c_struct for handling nestedarray (Siu Kwan Lam) PR #8137: CUDA: Fix #7806, Division by zero stops the kernel (Graham Markall) PR #8142: CUDA: Fix some missed changes from dropping 9.2 (Graham Markall) PR #8144: Fix NumPy capitalisation in docs. (stuartarchibald) PR #8145: Allow ufunc builder to use previously JITed function (Andre Masella) PR #8151: pin NumPy to build 0 of 1.19.2 on public CI (esc) PR #8163: CUDA: Remove context query in launch config (Graham Markall) PR #8165: Restrict strace based tests to be linux only via support feature. (stuartarchibald) PR #8170: CUDA: Fix missing space in low occupancy warning (Graham Markall) PR #8175: make build and upload order consistent (esc) PR #8181: Fix various typos (luzpaz) PR #8187: Update CHANGE_LOG for 0.55.2 (stuartarchibald esc) PR #8189: updated version support information for 0.55.2/0.57 (esc) PR #8191: CUDA: Update deprecation notes for 0.56. (Graham Markall) PR #8192: Update CHANGE_LOG for 0.56.0 (stuartarchibald esc Siu Kwan Lam) PR #8195: Make the workqueue threading backend once again fork safe. (stuartarchibald) PR #8196: Fix numerical tolerance in parfors caching test. (stuartarchibald) PR #8197: Fix isinstance warning check test. (stuartarchibald) PR #8203: pin llvmlite 0.39 for public CI builds (esc) PR #8255: CUDA: Make numba.cuda.tests.doc_examples.ffi a module to fix #8252 (Graham Markall) PR #8274: Update version support table doc for 0.56. (stuartarchibald) PR #8275: Update CHANGE_LOG for 0.56.0 final (stuartarchibald) Authors: Andre Masella Benjamin Graham brandon-b-miller Brandon T. Willard Gagandeep Singh Dhruv Patel LI Da Todd A. Anderson Ethan Pronovost esc Tobias Sargeant Graham Markall Guilherme Leobas Zizheng Guo Hadia Ahmed idorrington Michael Wang Kaustubh Kevin Modzelewski luk-f-a luzpaz Kyle Martin Nightfurex Nick Riasanovsky Nopileos2 Ray Bell readthedocs-assistant Rishi Kulkarni Sterling Baird Siu Kwan Lam stuartarchibald Stepan Rakitin Michael Collison Tingkai Liu @ text @@@comment $NetBSD$ bin/numba-${PYVERSSUFFIX} ${PYSITELIB}/${EGG_INFODIR}/PKG-INFO ${PYSITELIB}/${EGG_INFODIR}/SOURCES.txt ${PYSITELIB}/${EGG_INFODIR}/dependency_links.txt ${PYSITELIB}/${EGG_INFODIR}/requires.txt ${PYSITELIB}/${EGG_INFODIR}/top_level.txt ${PYSITELIB}/numba/__init__.py ${PYSITELIB}/numba/__init__.pyc ${PYSITELIB}/numba/__init__.pyo ${PYSITELIB}/numba/__main__.py ${PYSITELIB}/numba/__main__.pyc ${PYSITELIB}/numba/__main__.pyo ${PYSITELIB}/numba/_arraystruct.h ${PYSITELIB}/numba/_devicearray.h ${PYSITELIB}/numba/_devicearray.so ${PYSITELIB}/numba/_dispatcher.so ${PYSITELIB}/numba/_dynfunc.c 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Version 0.54.1 This is a bugfix release for 0.54.0. It fixes a regression in structured array type handling, a potential leak on initialization failure in the CUDA target, a regression caused by Numba’s vendored cloudpickle module resetting dynamic classes and a few minor testing/infrastructure related problems. Version 0.53.1 This is a bugfix release for 0.53.0. It contains the following four pull-requests which fix two critical regressions and two build failures reported by the openSuSe team: * Fix regression on gufunc serialization * Fix regression in CUDA: Set stream in mapped and managed array device_setup * Ignore warnings from packaging module when testing import behaviour. * set non-reported llvm timing values to 0.0 Version 0.53.0 This release continues to add new features, bug fixes and stability improvements to Numba. Highlights of core changes: Support for Python 3.9 Function sub-typing Initial support for dynamic gufuncs Parallel Accelerator (@@njit(parallel=True) now supports Fortran ordered arrays Version 0.52.0 This release focuses on performance improvements, but also adds some new features and contains numerous bug fixes and stability improvements. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.16 2021/01/01 13:29:16 mef Exp $ a2 1 bin/pycc-${PYVERSSUFFIX} a20 1 ${PYSITELIB}/numba/_hashtable.c a25 1 ${PYSITELIB}/numba/_npymath_exports.c a28 1 ${PYSITELIB}/numba/_typeof.c a125 3 ${PYSITELIB}/numba/core/dataflow.py ${PYSITELIB}/numba/core/dataflow.pyc ${PYSITELIB}/numba/core/dataflow.pyo d204 3 a218 3 ${PYSITELIB}/numba/core/overload_glue.py ${PYSITELIB}/numba/core/overload_glue.pyc ${PYSITELIB}/numba/core/overload_glue.pyo d264 1 a264 1 ${PYSITELIB}/numba/core/runtime/nrt.c d276 18 a411 3 ${PYSITELIB}/numba/core/typing/randomdecl.py ${PYSITELIB}/numba/core/typing/randomdecl.pyc 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improvements. Highlights of core feature changes include: The compilation chain is now based on LLVM 10 (Valentin Haenel). Numba has internally switched to prefer non-literal types over literal ones so as to reduce function over-specialisation, this with view of speeding up compile times (Siu Kwan Lam). On the CUDA target: Support for CUDA Toolkit 11, Ampere, and Compute Capability 8.0; Printing of SASS code for kernels; Callbacks to Python functions can be inserted into CUDA streams, and streams are async awaitable; Atomic nanmin and nanmax functions are added; Fixes for various miscompilations and segfaults. (mostly Graham Markall; call backs on streams by Peter Würtz). Intel also kindly sponsored research and development that lead to some exciting new features: Support for heterogeneous immutable lists and heterogeneous immutable string key dictionaries. Also optional initial/construction value capturing for all lists and dictionaries containing literal values (Stuart Archibald). A new pass-by-reference mutable structure extension type StructRef (Siu Kwan Lam). Object mode blocks are now cacheable, with the side effect of numerous bug fixes and performance improvements in caching. This also permits caching of functions defined in closures (Siu Kwan Lam). Deprecations to note: To align with other targets, the argtypes and restypes kwargs to @@cuda.jit are now deprecated, the bind kwarg is also deprecated. Further the target kwarg to the numba.jit decorator family is deprecated. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.14 2020/06/16 17:07:47 adam Exp $ d1079 1 @ 1.14 log @py-numba: updated to 0.50.0 Version 0.50.0: This is a more usual release in comparison to the others that have been made in the last six months. It comprises the result of a number of maintenance tasks along with some new features and a lot of bug fixes. Highlights of core feature changes include: The compilation chain is now based on LLVM 9. The error handling and reporting system has been improved to reduce the size of error messages, and also improve quality and specificity. The CUDA target has more stream constructors available and a new function for compiling to PTX without linking and loading the code to a device. Further, the macro-based system for describing CUDA threads and blocks has been replaced with standard typing and lowering implementations, for improved debugging and extensibility. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.13 2020/05/12 08:11:36 adam Exp $ d154 3 d528 6 d615 3 d712 3 d718 3 d781 3 d862 3 d968 3 d1378 3 d1387 3 d1930 3 @ 1.13 log @py-numba: updated to 0.49.1 Version 0.49.1: This is a bugfix release for 0.49.0, it fixes some residual issues with SSA form, a critical bug in the branch pruning logic and a number of other smaller issues: * Fixed Threading Implementation Typos * Fixes Remove references to cffi_support from docs and examples * Fix invalid type in resolve for comparison expr in parfors. * Fix erroneous rewrite of predicate to bit const on prune. * Fixes SSA local def scan based on invalid equality assumption. * Fixes naming error in array_exprs * Fix. Incorrect race variable detection due to SSA naming. * Make literal_unroll function work as a freevar. * Unset the memory manager after EMM Plugin tests * Fix some SSA issues * Pin to sphinx=2.4.4 to avoid problem with C declaration * Fix unifying undefined first class function types issue * Update example in 5m guide WRT SSA type stability. * Restore numba.types as public API @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.12 2020/04/18 08:14:09 adam Exp $ a37 24 ${PYSITELIB}/numba/analysis.py ${PYSITELIB}/numba/analysis.pyc ${PYSITELIB}/numba/analysis.pyo ${PYSITELIB}/numba/annotations.py ${PYSITELIB}/numba/annotations.pyc ${PYSITELIB}/numba/annotations.pyo ${PYSITELIB}/numba/appdirs.py ${PYSITELIB}/numba/appdirs.pyc ${PYSITELIB}/numba/appdirs.pyo ${PYSITELIB}/numba/array_analysis.py ${PYSITELIB}/numba/array_analysis.pyc ${PYSITELIB}/numba/array_analysis.pyo ${PYSITELIB}/numba/bytecode.py ${PYSITELIB}/numba/bytecode.pyc ${PYSITELIB}/numba/bytecode.pyo ${PYSITELIB}/numba/byteflow.py 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${PYSITELIB}/numba/untyped_passes.pyc ${PYSITELIB}/numba/untyped_passes.pyo ${PYSITELIB}/numba/utils.py ${PYSITELIB}/numba/utils.pyc ${PYSITELIB}/numba/utils.pyo ${PYSITELIB}/numba/withcontexts.py ${PYSITELIB}/numba/withcontexts.pyc ${PYSITELIB}/numba/withcontexts.pyo @ 1.12 log @py-numba: updated to 0.49.0 Version 0.49.0: This release is very large in terms of code changes. Large scale removal of unsupported Python and NumPy versions has taken place along with a significant amount of refactoring to simplify the Numba code base to make it easier for contributors. Numba’s intermediate representation has also undergone some important changes to solve a number of long standing issues. In addition some new features have been added and a large number of bugs have been fixed! IMPORTANT: In this release Numba’s internals have moved about a lot. A backwards compatibility “shim” is provided for this release so as to not immediately break projects using Numba’s internals. If a module is imported from a moved location the shim will issue a deprecation warning and suggest how to update the import statement for the new location. The shim will be removed in 0.50.0! Highlights of core feature changes include: Removal of all Python 2 related code and also updating the minimum supported Python version to 3.6, the minimum supported NumPy version to 1.15 and the minimum supported SciPy version to 1.0. Refactoring of the Numba code base. The code is now organised into submodules by functionality. This cleans up Numba’s top level namespace. Introduction of an ir.Del free static single assignment form for Numba’s intermediate representation An OpenMP-like thread masking API has been added for use with code using the parallel CPU backends For the CUDA target, all kernel launches now require a configuration, this preventing accidental launches of kernels with the old default of a single thread in a single block. The hard-coded autotuner is also now removed, such tuning is deferred to CUDA API calls that provide the same functionality The CUDA target also gained an External Memory Management plugin interface to allow Numba to use another CUDA-aware library for all memory allocations and deallocations The Numba Typed List container gained support for construction from iterables Experimental support was added for first-class function types @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.11 2020/02/01 21:03:58 adam Exp $ d1912 3 @ 1.11 log @py-numba: updated to 0.48.0 Version 0.48.0 This release is particularly small as it was present to catch anything that missed the 0.47.0 deadline (the deadline deliberately coincided with the end of support for Python 2.7). The next release will be considerably larger. The core changes in this release are dominated by the start of the clean up needed for the end of Python 2.7 support, improvements to the CUDA target and support for numerous additional unicode string methods. Enhancements from user contributed PRs (with thanks!): Brian Wignall fixed more spelling typos in. Denis Smirnov added support for string methods capitalize, casefold, swapcase , rsplit , partition and splitlines . Elena Totmenina extended support for string methods startswith and added endswith . Eric Wieser made type_callable return the decorated function itself in Ethan Pronovost added support for np.argwhere in Graham Markall contributed a large number of CUDA enhancements and fixes, namely: * Remove Python 3.4 backports from utils * Make device_array_like create contiguous arrays * Don’t launch ForAll kernels with 0 elements * Fix various issues in CUDA library search * Enable use of records and bools for shared memory, remove ddt, add additional transpose tests * Fix: Add more appropriate typing for CUDA device arrays * test_consuming_strides: Keep dev array alive * State that CUDA Toolkit 8.0 required in docs James Bourbeau added the Python 3.8 classifier to setup.py in. John Kirkham added a clarification to the __cuda_array_interface__ documentation in. Leo Fang Fixed an indexing problem in dummyarray in. Marcel Bargull fixed a build and test issue for Python 3.8 in. Maria Rubtsov added support for string methods isdecimal , isdigit , isnumeric and replace . General Enhancements: * Make type_callable return the decorated function * merge string prs This merge PR included the following: * Implement str.capitalize() based on CPython * Implement str.casefold() based on CPython * Implement str.swapcase() based on CPython * Implement str.rsplit() based on CPython * Implement str.isdecimal * Implement str.isdigit * Implement str.isnumeric * Implement str.partition() based on CPython * Implement str.splitlines() based on CPython * Implement str.replace * Functionality extension str.startswith() based on CPython * Add functionality for str.endswith() * Disable help messages. * Add coverage for np.argwhere Fixes: * Only use lives (and not aliases) to create post parfor live set. * Fix more spelling typos * Propagate semantic constants ahead of static rewrites. * Add Python 3.8 classifier to setup.py * Update setup.py and buildscripts for dependency requirements * Convert from arrays to names in define() and don’t invalidate for multiple consistent defines. * Permit mixed int types in wrap_index * Catch the use of global typed-list in JITed functions * Fix, bug in bytecode analysis. CUDA Enhancements/Fixes: * Fix: Add more appropriate typing for CUDA device arrays * Make device_array_like create contiguous arrays * State that CUDA Toolkit 8.0 required in docs * test_consuming_strides: Keep dev array alive * Fix IndexError when accessing the “-1” element of dummyarray * Enable use of records and bools for shared memory, remove ddt, add additional transpose tests * Fix various issues in CUDA library search * Don’t launch ForAll kernels with 0 elements * Remove Python 3.4 backports from utils Documentation Updates: * Clarify what dictionary means * Update docs for updated version requirements * Update deprecation notices for 0.48.0 CI updates: * Install optional dependencies for Python 3.8 tests * Drop Py2.7 and Py3.5 from public CI * Fix CI py38 @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.10 2020/01/14 16:25:34 adam Exp $ a27 2 ${PYSITELIB}/numba/_math_c99.c ${PYSITELIB}/numba/_math_c99.h a31 3 ${PYSITELIB}/numba/_runtests.py ${PYSITELIB}/numba/_runtests.pyc 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${PYSITELIB}/numba/unittest_support.pyc ${PYSITELIB}/numba/unittest_support.pyo a2211 6 ${PYSITELIB}/numba/unsafe/bytes.py ${PYSITELIB}/numba/unsafe/bytes.pyc ${PYSITELIB}/numba/unsafe/bytes.pyo ${PYSITELIB}/numba/unsafe/eh.py ${PYSITELIB}/numba/unsafe/eh.pyc ${PYSITELIB}/numba/unsafe/eh.pyo a2214 12 ${PYSITELIB}/numba/unsafe/nrt.py ${PYSITELIB}/numba/unsafe/nrt.pyc ${PYSITELIB}/numba/unsafe/nrt.pyo ${PYSITELIB}/numba/unsafe/numbers.py ${PYSITELIB}/numba/unsafe/numbers.pyc ${PYSITELIB}/numba/unsafe/numbers.pyo ${PYSITELIB}/numba/unsafe/refcount.py ${PYSITELIB}/numba/unsafe/refcount.pyc ${PYSITELIB}/numba/unsafe/refcount.pyo ${PYSITELIB}/numba/unsafe/tuple.py ${PYSITELIB}/numba/unsafe/tuple.pyc ${PYSITELIB}/numba/unsafe/tuple.pyo @ 1.10 log @py-numba: updated to 0.47.0 Version 0.47.0 -------------- This release expands the capability of Numba in a number of important areas and is also significant as it is the last major point release with support for Python 2 and Python 3.5 included. The next release (0.48.0) will be for Python 3.6+ only! (This follows NumPy's deprecation schedule as specified in `NEP 29 `_.) Highlights of core feature changes include: * Full support for Python 3.8 (Siu Kwan Lam) * Opt-in bounds checking (Aaron Meurer) * Support for ``map``, ``filter`` and ``reduce`` (Stuart Archibald) Intel also kindly sponsored research and development that lead to some exciting new features: * Initial support for basic ``try``/``except`` use (Siu Kwan Lam) * The ability to pass functions created from closures/lambdas as arguments (Stuart Archibald) * ``sorted`` and ``list.sort()`` now accept the ``key`` argument (Stuart Archibald and Siu Kwan Lam) * A new compiler pass triggered through the use of the function ``numba.literal_unroll`` which permits iteration over heterogeneous tuples and constant lists of constants. (Stuart Archibald) Enhancements from user contributed PRs (with thanks!): * Ankit Mahato added a reference to a new talk on Numba at PyCon India 2019 * Brian Wignall kindly fixed some spelling mistakes and typos * Denis Smirnov wrote numerous methods to considerable enhance string support including: * ``str.rindex()`` * ``str.isprintable()`` * ``str.index()`` * ``start/end`` parameters for ``str.find()`` * ``str.isspace()`` * ``str.isidentifier()`` * ``str.rpartition()`` * ``str.lower()`` and ``str.islower()`` * Elena Totmenina implemented both ``str.isalnum()``, ``str.isalpha()`` and ``str.isascii`` * Eric Larson fixed a bug in literal comparison * Ethan Pronovost updated the ``np.arange`` implementation to allow the use of the ``dtype`` key word argument and also added ``bool`` implementations for several types. * Graham Markall fixed some issues with the CUDA target, namely: * Added physical limits for CC 7.0 / 7.5 to CUDA autotune * Fixed bugs in TestCudaWarpOperations * Improved errors / warnings for the CUDA vectorize decorator * Guilherme Leobas fixed a typo in the ``urem`` implementation * Isaac Virshup contributed a number of patches that fixed bugs, added support for more NumPy functions and enhanced Python feature support. These contributions included: * Allow array construction with mixed type shape tuples * Implementing ``np.lcm`` * Implement np.gcd and math.gcd * Make slice constructor more similar to python. * Added support for slice.indices * Clarify numba ufunc supported features * James Bourbeau fixed some issues with tooling, add ``setuptools`` as a dependency and add pre-commit hooks for ``flake8`` compliance. * Leo Fang made ``numba.dummyarray.Array`` iterable * Marc Garcia fixed the ``numba.jit`` parameter name signature_or_function * Marcelo Duarte Trevisani patched the llvmlite requirement to ``>=0.30.0`` * Matt Cooper fixed a long standing CI problem by remove maxParallel * Matti Picus fixed an issue with ``collections.abc`` from Azure Pipelines. * Rob Ennis patched a bug in ``np.interp`` ``float32`` handling * VDimir fixed a bug in array transposition layouts and re-enabled and fixed some idle tests. * Vyacheslav Smirnov Enable support for `str.istitle()`` General Enhancements: * Bounds checking * Add pre-commit hooks * Handle kw args in inliner when callee is a function * Permits closures to become functions, enables map(), filter() * Implement method title() for unicode based on Cpython * Enable support for istitle() method for unicode string * Implement str.lower() and str.islower() * Implement str.rfind() * Refactor `overload*` and support `jit_options` and `inline` * Added support for slice.indices * Add `bool` overload for several types * Allow array construction with mixed type shape tuples * Python3.8 support * Add parfor support for ndarray.fill. * Update typeconv error message to ask for sys.executable. * Update `np.arange` implementation with `@@overload` * Make slice constructor more similar to python. * Implement np.gcd and math.gcd * Add setuptools as a dependency * put git hash into build string * Better compiler error messages for improperly used reduction variables. * Typed list implement and expose allocation * Typed list faster copy * Implement str.isspace() based on CPython * Implement str.isprintable() based on CPython * Implement str.isidentifier() based on CPython * Implement str.isalnum() based on CPython * Implement str.isalpha() based on CPython * Implement str.rpartition() based on CPython * Implement str.isascii() based on CPython * Add graphviz output for FunctionIR * Python3.8 looplifting * Implement str.expandtabs() based on CPython * Implement str.index() based on CPython * Implement str.rindex() based on CPython * Support params start/end for str.find() * Bump to llvmlite 0.31 * Specialise arange dtype on arch + python version. * basic support for try except * Implement np.lcm * loop canonicalisation and type aware tuple unroller/loop body versioning passes * Update hash(tuple) for Python 3.8. * Implement sort/sorted with key. * Add `is_internal` property to all Type classes. Fixes: * Update to LLVM8 memset/memcpy intrinsic * Convert sub to add and div to mul when doing the reduction across the per-thread reduction array. * Handle 0 correctly as slice parameter. * Remove multiply defined variables from all blocks' equivalence sets. * Fix pickling of dufunc * BUG: Comparison for literal * Change get_call_table to support intermediate Vars. * Requires llvmlite >=0.30.0 * prefer to import from collections.abc * fix flake8 errors * Fix and enable idle tests from test_array_manipulation * Fix transpose output array layout * Fix issue with SVML (and knock-on function resolution effects). * Treat 0d arrays like scalars. * fix missing incref on flags * fix typos in numba/targets/base.py * fix typos * fix spelling in now-failing tests * windowing test should check equality only up to double precision errors * fix refining list by using extend on an iterator * Fix return type in arange and zero step size handling. * suppress spurious RuntimeWarning about ufunc sizes * skip the xfail test for now. Py3.8 CFG refactor seems to have changed the test case * regex needs to accept singular form of "argument" * fix typed list equals * Fix some spelling typos * np.interp bugfix for float32 handling * fix creating list with JIT disabled * fix creating dict with JIT disabled * Better handling of prange with multiple reductions on the same variable. * Improve the error message for `raise `. * Move overload of literal_unroll to avoid circular dependency that breaks Python 2.7 * Fix test error on windows * Fixes a bug in the relabelling logic in literal_unroll. * Fix overload_method problem with stararg * Add ind_to_const to enable fewer equivalence classes. * Remove xfail for test which has since had underlying issue fixed. * skip pycc test on Python 3.8 + macOS because of distutils issue @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.9 2019/10/19 14:17:02 adam Exp $ a1131 3 ${PYSITELIB}/numba/testing/ddt.py ${PYSITELIB}/numba/testing/ddt.pyc ${PYSITELIB}/numba/testing/ddt.pyo d1303 3 a1737 3 ${PYSITELIB}/numba/tests/test_utils.py ${PYSITELIB}/numba/tests/test_utils.pyc ${PYSITELIB}/numba/tests/test_utils.pyo @ 1.9 log @py-numba: updated to 0.46.0 Version 0.46.0 This release significantly reworked one of the main parts of Numba, the compiler pipeline, to make it more extensible and easier to use. The purpose of this was to continue enhancing Numba's ability for use as a compiler toolkit. In a similar vein, Numba now has an extension registration mechanism to allow other Numba-using projects to automatically have their Numba JIT compilable functions discovered. There were also a number of other related compiler toolkit enhancement added along with some more NumPy features and a lot of bug fixes. This release has updated the CUDA Array Interface specification to version 2, which clarifies the `strides` attribute for C-contiguous arrays and specifies the treatment for zero-size arrays. The implementation in Numba has been changed and may affect downstream packages relying on the old behavior General Enhancements: * Add rewrite for semantic constants. * Add np.cross support * Make IR comparable and legalize it. * R&D inlining, jitted and overloaded. * Automatic JIT of called functions * Inspection tool to check what numba supports * Implement np.count_nonzero * Unicode array support * Entrypoints for numba extensions * Literal dispatch * Allow dtype input argument in np.sum * New compiler. * add support for np.append * Refactor NRT C-API * 0.46 scheduled deprecations * Add env var to disable performance warnings. * add np.array_equal support * Implement numba.cross2d * Add triangular indices functions * Enable support for count() method for unicode string Fixes: * Fix inplace operator error for arrays * Detect and raise unsupported on generator expressions * Don't allow the allocation of mutable objects written into a container to be hoisted. * Avoid deprecated use of inspect.getargspec * Replace GC macro with function call * Loosen up typed container casting checks * Fix some coding lines at the top of some files (utf8 -> utf-8) * Replace "import \*" with explicit imports in numba/types * Fix incorrect alg in isupper for ascii strings. * test using jitclass in typed-list * Add allocation hoisting info to LICM section at diagnostic L4 * Offset search box to avoid wrapping on some pages with Safari. * Replace all "except BaseException" with "except Exception". * Restore the "free" conda channel for NumPy 1.10 support. * Add lowering for constant bytes. * Add exception chaining for better error context * Name of type should not contain user facing description for debug. * Limit the number of return types for recursive functions * Fixed two module teardown races in py2. * Fix and test numpy.random.random_sample(n) for np117 * NamedTuple - Raises an error on non-iterable elements * Add a newline in patched errors * Fix liveness for remove dead of parfors (and other IR extensions) * Make List.__getitem__ accept unsigned parameters * Raise specific error at typing time for iteration on >1D array. * Fix static_getitem with Literal type as index * Update to inliner cost model information. * Use specific random number seed when generating arbitrary test data * Adjust test timeouts * Skip unicode array tests on ppc64le that trigger an LLVM bug * Fix packaging issue due to missing numba/cext * Fix issue 4520 due to storage model mismatch * Updates for llvmlite 0.30.0 CUDA Enhancements/Fixes: * cudasim mishandling recarray * Replace use of `np.prod` with `functools.reduce` for computing size from shape * Prevent taking the GIL in ForAll * Just pass NULL for b2d_func for constant dynamic sharedmem * Update CUDA Array Interface & Enforce Numba compliance * Implement math.{degrees, radians} for the CUDA target. * Bump cuda array interface to version 2 Documentation Updates: * Add docs for ARMv8/AArch64 * Add supported platforms to the docs. * Add docstrings to inspect methods * Update Python 2.7 EOL statement * Add note about np.sum * Minor parallel performance tips edits * Clarify docs for typed dict with regard to arrays * Fix example in guvectorize docstring. * fix two typos in architecture.rst * Document numba.extending.intrinsic and inlining. * Fix typo in jit-compilation docs * add dependency list to docs * Add documentation for implementing new compiler passes. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.8 2019/06/21 08:07:47 adam Exp $ d62 3 d1066 3 a1068 3 ${PYSITELIB}/numba/targets/literal_dispatch.py ${PYSITELIB}/numba/targets/literal_dispatch.pyc ${PYSITELIB}/numba/targets/literal_dispatch.pyo d1297 3 d1519 3 d1528 3 d1537 3 d1684 3 d1901 3 @ 1.8 log @py-numba: updated to 0.44.1 Version 0.44.1 This patch release addresses some regressions reported in the 0.44.0 release: - Fix 4164 issue with NUMBAPRO_NVVM. - Abandon branch pruning if an arg name is redefined. - Fix 4156. Problem with defining in-loop variables. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.7 2019/06/02 09:04:33 adam Exp $ a15 2 ${PYSITELIB}/numba/_dictobject.c ${PYSITELIB}/numba/_dictobject.h d39 1 d72 9 d87 3 d96 3 a511 3 ${PYSITELIB}/numba/cuda/tests/cudapy/test_smart_array.py ${PYSITELIB}/numba/cuda/tests/cudapy/test_smart_array.pyc ${PYSITELIB}/numba/cuda/tests/cudapy/test_smart_array.pyo d596 3 d614 6 d651 3 d701 3 d707 3 d947 1 a977 3 ${PYSITELIB}/numba/smartarray.py ${PYSITELIB}/numba/smartarray.pyc ${PYSITELIB}/numba/smartarray.pyo d1021 3 d1063 3 a1116 3 ${PYSITELIB}/numba/targets/smartarray.py ${PYSITELIB}/numba/targets/smartarray.pyc ${PYSITELIB}/numba/targets/smartarray.pyo d1171 6 d1219 3 d1225 3 d1300 3 d1390 3 d1447 3 d1468 3 d1480 3 d1492 6 d1501 3 d1585 3 a1641 3 ${PYSITELIB}/numba/tests/test_smart_array.py ${PYSITELIB}/numba/tests/test_smart_array.pyc ${PYSITELIB}/numba/tests/test_smart_array.pyo d1675 6 d1702 3 d1772 9 d1871 3 d1886 3 d1898 3 @ 1.7 log @py-numba: updated to 0.44.0 Version 0.44.0 IMPORTANT: In this release a few significant deprecations (and some less significant ones) are being made, users are encouraged to read the related documentation. General enhancements in this release include: - Numba is backed by LLVM 8 on all platforms apart from ppc64le, which, due to bugs, remains on the LLVM 7.x series. - Numba's dictionary support now includes type inference for keys and values. - The .view() method now works for NumPy scalar types. - Newly supported NumPy functions added: np.delete, np.nanquantile, np.quantile, np.repeat, np.shape. In addition considerable effort has been made to fix some long standing bugs and a large number of other bugs, the "Fixes" section is very large this time! Enhancements from user contributed PRs (with thanks!): - Max Bolingbroke added support for the selective use of fastmath flags in 3847. - Rob Ennis made min() and max() work on iterables in 3820 and added np.quantile and np.nanquantile in 3899. - Sergey Shalnov added numerous unicode string related features, zfill in 3978, ljust in 4001, rjust and center in 4044 and strip, lstrip and rstrip in 4048. - Guilherme Leobas added support for np.delete in 3890 - Christoph Deil exposed the Numba CLI via python -m numba in 4066 and made numerous documentation fixes. - Leo Schwarz wrote the bulk of the code for jitclass default constructor arguments in 3852. - Nick White enhanced the CUDA backend to use min/max PTX instructions where possible in 4054. - Lucio Fernandez-Arjona implemented the unicode string __mul__ function in 3952. - Dimitri Vorona wrote the bulk of the code to implement getitem and setitem for jitclass in 3861. General Enhancements: * Min max on iterables * Unicode type iteration * Allow fine-grained control of fastmath flags to partially address 2923 * Add support for np.delete * Support for np.quantile and np.nanquantile * Fix 3457 :: Implements np.repeat * Add .view() method for NumPy scalars * Update icc_rt clone recipe. * __mul__ for strings, initial implementation and tests * Type-inferred dictionary * Create a view for string slicing to avoid extra allocations * zfill operation implementation * ljust operation implementation * Support dict() and {} * Support for llvm 8 * Make type.Optional str more representative * Deprecation warnings * rjust and center operations implementation * strip, lstrip and rstrip operations implementation * Expose numba CLI via python -m numba * Impl np.shape and support function for asarray. * Deprecate the use of iternext_impl without RefType CUDA Enhancements/Fixes: * Adds .nbytes property to CUDA device array objects. * Add .inspect_ptx() to cuda device function * CUDA: Use min/max PTX Instructions * Update env-vars for CUDA libraries lookup Documentation Updates: * Code repository map * adding Joris' Fosdem 2019 presentation * order talks on applications of Numba by date * fix two small typos in vectorize docs * Fixup jitclass docs * mention preprint repo in FAQ. Fixes 3981 * Correct runtests command in contributing.rst * fix typo * Ambiguous Documentation fix for guvectorize. * Remove remaining mentions of autojit in docs * Fix annotate example in docstring * Add FAQ entry explaining Numba project name * Add Documentation for atomicity of typed.Dict * Remove info about CUDA ENVVAR potential replacement Fixes: * Resolves issue 3528. Adds support for slices when not using parallel=True. * Remove dels for known dead vars. * Fix mutable flag transmission in .astype * Fix some minor issues in the C source. * Correct boolean reinterpretation of data * Comments out the appveyor badge * fixes flake8 after merge * Add assert to ir.py to help enforce correct structuring * fix preparfor dtype transform for datetime64 * Prevent mutation of objmode fallback IR. * Updates for llvmlite 0.29 * Use safe_load from pyyaml. * Add tolerance to network errors by permitting conda to retry * Fix casting in namedtuple ctor. * Fix array inliner for multiple array definition. * Cherrypick 3903 to main * Raise better error if unsupported jump opcode found. * Apply flake8 to the numpy related files * Silence DeprecationWarning * Better error message for unknown opcode * Fix typing of ufuncs in parfor conversion * Return variable renaming dict from inline_closurecall * Fix bug in alignment computation of Record.make_c_struct * Fix error with pickling unicode * Unicode split algo versioning * Add handler for unknown locale to numba -s * Permit Optionals in ufunc machinery * Remove assert in type inference causing poor error message. * add is_ascii flag to UnicodeType * Prevent zero division error in np.linalg.cond * Resolves 4007. * Add a more specific error message for invalid write to a global. * Fix handling of titles in record dtype * Do a check if a call is const before saying that an object is multiply defined. * Fix issue 4020. Turn off no_cpython_wrapper flag when compiling for… * [WIP] Fixing wrong dtype of array inside reflected list 4028 * Change IPython cache dir name to numba_cache * Delete examples/notebooks/LinearRegr.py * Catch writes to global typed.Dict and raise. * Check tuple length * Fix missing incref on optional return None * Make the warnings fixer flush work for warning comparing on type. * Fix function definition finding logic for commented def * Fix alignment check on 32-bit. * Use PEP 508 compliant env markers for install deps @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.6 2019/03/14 13:04:17 adam Exp $ d1525 3 @ 1.6 log @py-numba: updated to 0.43.0 Version 0.43.0 In this release, the major new features are: * Initial support for statically typed dictionaries * Improvements to hash() to match Python 3 behavior * Support for the heapq module * Ability to pass C structs to Numba * More NumPy functions: asarray, trapz, roll, ptp, extract @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.5 2019/01/02 15:43:10 adam Exp $ d12 3 d112 3 d170 3 d447 3 d540 3 d997 3 d1141 3 d1273 3 d1483 3 d1751 3 d1796 3 @ 1.5 log @py-numba: updated to 0.42.0 Version 0.42.0 In this release the major features are: * The capability to launch and attach the GDB debugger from within a jitted function. * The upgrading of LLVM to version 7.0.0. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.4 2018/12/09 20:25:12 adam Exp $ d13 2 d270 3 d561 3 d994 6 d1162 3 d1189 3 d1300 6 d1372 3 d1661 6 d1760 3 d1766 3 @ 1.4 log @py-numba: updated to 0.41.0 Version 0.41.0 This release adds the following major features: * Diagnostics showing the optimizations done by ParallelAccelerator * Support for profiling Numba-compiled functions in Intel VTune * Additional NumPy functions: partition, nancumsum, nancumprod, ediff1d, cov, conj, conjugate, tri, tril, triu * Initial support for Python 3 Unicode strings General Enhancements: * armv7 support * invert mapping b/w binop operators and the operator module * First attempt at parallel diagnostics * Adding NUMBA_ENABLE_PROFILING envvar, enabling jit event * Support for np.partition * Support for np.nancumsum and np.nancumprod * Add location information to exceptions. * Support for np.ediff1d * Support for np.cov * Support user pipeline class in with lifting * string support * Improve error message for empty imprecise lists. * Enable overload(operator.getitem) * Support negative indexing in tuple. * Refactor Const type * Optimized usage of alloca out of the loop * Updates for llvmlite 0.26 * Add support for `np.conj/np.conjugate`. * np.tri, np.tril, np.triu - default optional args * Permit dtype argument as sole kwarg in np.eye CUDA Enhancements: * Add max_registers Option to cuda.jit Continuous Integration / Testing: * CI with Azure Pipelines * Workaround race condition with apt * Fix issues with Azure Pipelines * Fix `RuntimeWarning: 'numba.runtests' found in sys.modules` * Disable openmp in wheel building * Azure Pipelines templates * Fix cuda tests and error reporting in test discovery * Prevent faulthandler installation on armv7l * Fix CUDA test that used negative indexing behaviour that's fixed. * Start Flake8 checking of Numba source Fixes: * Fix dispatcher to only consider contiguous-ness. * Fix 3119, raise for 0d arrays in reductions * Reduce redundant module linking * Fix AOT on windows. * Fix memory management of __cuda_array_interface__ views. * Fix typo in error name. * Fix the default unboxing logic * Allow non-global reference to objmode() context-manager * Fix global reference in objmode for dynamically created function * CUDA_ERROR_MISALIGNED_ADDRESS Using Multiple Const Arrays * Correctly handle very old versions of colorama * Add 32bit package guard for non-32bit installs * Fix with-objmode warning * Fix label offset in call inline after parfor pass * Fixes raising of user defined exceptions for exec(). * Fix error due to function naming in CI in py2.7 * Fixed TBB's single thread execution and test added for * Allow matching non-array objects in find_callname() * Change getiter and iternext to not be pure. * Make ir.UndefinedType singleton class. * Fix np.random.shuffle sideeffect * Raise unsupported for kwargs given to `print()` * Remove dead script. * Fix stencil support for boolean as return type * Fix handling make_function literals * Add missing unicode != unicode * Fix complex math sqrt implementation for large -ve values * This adds arg an check for the pattern supplied to Parfors. * Sets list dtor linkage to `linkonce_odr` to fix visibility in AOT Documentation Updates: * Update 0.40 changelog with additional PRs * Tweak spacing to avoid search box wrapping onto second line * Add note about memory leaks with exceptions to docs. * Add FAQ on CUDA + fork issue. * Update docs for argsort, kind kwarg partially supported. * Added mention of njit in 5minguide.rst * Fix parallel reduction example in docs. * Fix broken link and mark up problem. * Size Numba logo in docs in em units. * just two typos * Document string support * Documentation for parallel diagnostics. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.3 2018/09/03 23:47:44 minskim Exp $ d964 1 d983 3 d1331 3 @ 1.3 log @math/py-numba: Add ALTERNATIVES @ text @d1 3 a3 3 @@comment $NetBSD: PLIST,v 1.2 2018/08/28 12:06:42 adam Exp $ bin/numba${PYVERSSUFFIX} bin/pycc${PYVERSSUFFIX} d31 3 d77 3 d358 3 d367 3 a576 139 ${PYSITELIB}/numba/hsa/__init__.py ${PYSITELIB}/numba/hsa/__init__.pyc ${PYSITELIB}/numba/hsa/__init__.pyo ${PYSITELIB}/numba/hsa/api.py ${PYSITELIB}/numba/hsa/api.pyc ${PYSITELIB}/numba/hsa/api.pyo ${PYSITELIB}/numba/hsa/codegen.py ${PYSITELIB}/numba/hsa/codegen.pyc ${PYSITELIB}/numba/hsa/codegen.pyo ${PYSITELIB}/numba/hsa/compiler.py ${PYSITELIB}/numba/hsa/compiler.pyc ${PYSITELIB}/numba/hsa/compiler.pyo ${PYSITELIB}/numba/hsa/decorators.py ${PYSITELIB}/numba/hsa/decorators.pyc ${PYSITELIB}/numba/hsa/decorators.pyo ${PYSITELIB}/numba/hsa/descriptor.py ${PYSITELIB}/numba/hsa/descriptor.pyc 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${PYSITELIB}/numba/hsa/mathimpl.py ${PYSITELIB}/numba/hsa/mathimpl.pyc ${PYSITELIB}/numba/hsa/mathimpl.pyo ${PYSITELIB}/numba/hsa/stubs.py ${PYSITELIB}/numba/hsa/stubs.pyc ${PYSITELIB}/numba/hsa/stubs.pyo ${PYSITELIB}/numba/hsa/target.py ${PYSITELIB}/numba/hsa/target.pyc ${PYSITELIB}/numba/hsa/target.pyo ${PYSITELIB}/numba/hsa/tests/__init__.py ${PYSITELIB}/numba/hsa/tests/__init__.pyc ${PYSITELIB}/numba/hsa/tests/__init__.pyo ${PYSITELIB}/numba/hsa/tests/hsadrv/__init__.py ${PYSITELIB}/numba/hsa/tests/hsadrv/__init__.pyc ${PYSITELIB}/numba/hsa/tests/hsadrv/__init__.pyo ${PYSITELIB}/numba/hsa/tests/hsadrv/test_driver.py ${PYSITELIB}/numba/hsa/tests/hsadrv/test_driver.pyc ${PYSITELIB}/numba/hsa/tests/hsadrv/test_driver.pyo ${PYSITELIB}/numba/hsa/tests/hsadrv/test_hlc.py ${PYSITELIB}/numba/hsa/tests/hsadrv/test_hlc.pyc ${PYSITELIB}/numba/hsa/tests/hsadrv/test_hlc.pyo ${PYSITELIB}/numba/hsa/tests/hsadrv/vector_copy.brig ${PYSITELIB}/numba/hsa/tests/hsapy/__init__.py ${PYSITELIB}/numba/hsa/tests/hsapy/__init__.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/__init__.pyo ${PYSITELIB}/numba/hsa/tests/hsapy/test_atomics.py ${PYSITELIB}/numba/hsa/tests/hsapy/test_atomics.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/test_atomics.pyo ${PYSITELIB}/numba/hsa/tests/hsapy/test_autojit.py ${PYSITELIB}/numba/hsa/tests/hsapy/test_autojit.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/test_autojit.pyo ${PYSITELIB}/numba/hsa/tests/hsapy/test_barrier.py ${PYSITELIB}/numba/hsa/tests/hsapy/test_barrier.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/test_barrier.pyo ${PYSITELIB}/numba/hsa/tests/hsapy/test_compiler.py ${PYSITELIB}/numba/hsa/tests/hsapy/test_compiler.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/test_compiler.pyo ${PYSITELIB}/numba/hsa/tests/hsapy/test_decorator.py ${PYSITELIB}/numba/hsa/tests/hsapy/test_decorator.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/test_decorator.pyo ${PYSITELIB}/numba/hsa/tests/hsapy/test_gufuncbuilding.py ${PYSITELIB}/numba/hsa/tests/hsapy/test_gufuncbuilding.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/test_gufuncbuilding.pyo ${PYSITELIB}/numba/hsa/tests/hsapy/test_linkage.py ${PYSITELIB}/numba/hsa/tests/hsapy/test_linkage.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/test_linkage.pyo ${PYSITELIB}/numba/hsa/tests/hsapy/test_math.py ${PYSITELIB}/numba/hsa/tests/hsapy/test_math.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/test_math.pyo ${PYSITELIB}/numba/hsa/tests/hsapy/test_matmul.py ${PYSITELIB}/numba/hsa/tests/hsapy/test_matmul.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/test_matmul.pyo ${PYSITELIB}/numba/hsa/tests/hsapy/test_positioning.py ${PYSITELIB}/numba/hsa/tests/hsapy/test_positioning.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/test_positioning.pyo ${PYSITELIB}/numba/hsa/tests/hsapy/test_reduction.py ${PYSITELIB}/numba/hsa/tests/hsapy/test_reduction.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/test_reduction.pyo ${PYSITELIB}/numba/hsa/tests/hsapy/test_scan.py ${PYSITELIB}/numba/hsa/tests/hsapy/test_scan.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/test_scan.pyo ${PYSITELIB}/numba/hsa/tests/hsapy/test_simple.py ${PYSITELIB}/numba/hsa/tests/hsapy/test_simple.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/test_simple.pyo ${PYSITELIB}/numba/hsa/tests/hsapy/test_ufuncbuilding.py ${PYSITELIB}/numba/hsa/tests/hsapy/test_ufuncbuilding.pyc ${PYSITELIB}/numba/hsa/tests/hsapy/test_ufuncbuilding.pyo ${PYSITELIB}/numba/hsa/vectorizers.py ${PYSITELIB}/numba/hsa/vectorizers.pyc ${PYSITELIB}/numba/hsa/vectorizers.pyo a657 3 ${PYSITELIB}/numba/objmode.py ${PYSITELIB}/numba/objmode.pyc ${PYSITELIB}/numba/objmode.pyo d686 3 d713 165 a1014 3 ${PYSITELIB}/numba/targets/operatorimpl.py ${PYSITELIB}/numba/targets/operatorimpl.pyc ${PYSITELIB}/numba/targets/operatorimpl.pyo d1237 3 d1294 3 d1357 3 d1450 3 d1525 3 d1561 3 d1588 3 d1594 3 a1696 3 ${PYSITELIB}/numba/typing/operatordecl.py ${PYSITELIB}/numba/typing/operatordecl.pyc ${PYSITELIB}/numba/typing/operatordecl.pyo d1709 3 d1727 3 @ 1.2 log @py-numba: updated to 0.39.0 Version 0.39.0 Here are the highlights for the Numba 0.39.0 release. This is the first version that supports Python 3.7. With help from Intel, we have fixed the issues with SVML support. List has gained support for containing reference-counted types like NumPy arrays and list. Note, list still cannot hold heterogeneous types. We have made a significant change to the internal calling-convention, which should be transparent to most users, to allow for a future feature that will permitting jumping back into python-mode from a nopython-mode function. This also fixes a limitation to print that disabled its use from nopython functions that were deep in the call-stack. For CUDA GPU support, we added a __cuda_array_interface__ following the NumPy array interface specification to allow Numba to consume externally defined device arrays. We have opened a corresponding pull request to CuPy to test out the concept and be able to use a CuPy GPU array. The Numba dispatcher inspect_types() method now supports the kwarg pretty which if set to True will produce ANSI/HTML output, showing the annotated types, when invoked from ipython/jupyter-notebook respectively. The NumPy functions ndarray.dot, np.percentile and np.nanpercentile, and np.unique are now supported. Numba now supports the use of a per-project configuration file to permanently set behaviours typically set via NUMBA_* family environment variables. Support for the ppc64le architecture has been added. @ text @d1 3 a3 3 @@comment $NetBSD: PLIST,v 1.1 2018/05/18 16:08:49 minskim Exp $ bin/numba bin/pycc @ 1.1 log @math/py-numba: Import version 0.37.0 Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Continuum Analytics, Inc. It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls effectively removing the "interpreter" but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy. Packaged by Kamil Rytarowski for pkgsrc-wip and updated by me. @ text @d1 1 a1 1 @@comment $NetBSD$ d92 3 d162 3 a191 3 ${PYSITELIB}/numba/cuda/simulator/array.py ${PYSITELIB}/numba/cuda/simulator/array.pyc ${PYSITELIB}/numba/cuda/simulator/array.pyo d346 3 d457 3 d496 3 d794 3 d962 3 d1208 3 d1409 3 d1475 3 @