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Use Arrow C Data Interface functions for Python interop #15904
Use Arrow C Data Interface functions for Python interop #15904
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…rrow_c_interface_py
…ge strings to strings if the compression is lossless
…rrow_c_interface_py
I think this PR is ready to go now (pending reviews). Thanks to everyone for the fruitful discussions! There is plenty of follow-up work to be done that came out of it. |
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I'm a little hazy on the cudf details, but I took a look for lifecycle, nanoarrow, and Cython, and those all look good to me!
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Python changes LGTM
Running
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/merge |
commit 1a4c2aa Author: Thomas Li <[email protected]> Date: Tue Jul 2 07:38:18 2024 -0700 Start migrating I/O writers to pylibcudf (starting with JSON) (rapidsai#15952) Switches the JSON writer to use pylibcudf. xref rapidsai#15162 Authors: - Thomas Li (https://github.com/lithomas1) - Vyas Ramasubramani (https://github.com/vyasr) Approvers: - Lawrence Mitchell (https://github.com/wence-) - Vyas Ramasubramani (https://github.com/vyasr) URL: rapidsai#15952 commit a1447c7 Author: Robert Maynard <[email protected]> Date: Tue Jul 2 09:34:29 2024 -0400 Promote has_nested_columns to cudf public API (rapidsai#16131) The `has_nested_columns` functionality is used in numerous tests. It looks like it should be part of our stable public API. Authors: - Robert Maynard (https://github.com/robertmaynard) Approvers: - Muhammad Haseeb (https://github.com/mhaseeb123) - Yunsong Wang (https://github.com/PointKernel) URL: rapidsai#16131 commit a4be7bd Author: Vyas Ramasubramani <[email protected]> Date: Tue Jul 2 00:50:42 2024 -0700 Use Arrow C Data Interface functions for Python interop (rapidsai#15904) This PR replaces the internals of `from_arrow` in pylibcudf with an implementation that uses the [Arrow C Data Interface](https://arrow.apache.org/docs/format/CDataInterface.html) using the [Python Capsule interface](https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html). This allows us to decouple our Python builds from using pyarrow Cython (partially, we haven't replaced the `to_arrow` conversion yet) and it will also allow us to support any other Python package that is a producer of the data interface. To support the above functionality, the following additional changes were needed in this PR: - Added the ability to produce cudf tables from `ArrowArrayStream` objects since that is what `pyarrow.Table` produces. This function is a simple wrapper around the existing `from_arrrow(ArrowArray)` API. - Added support for the large strings type, for which support has improved throughout cudf since the `from_arrow_host` API was added and for which we now require a basic overload for tests to pass. I did not add corresponding support for `from_arrow_device` to avoid ballooning the scope of this PR, so that work can be done in a follow-up. - Proper handling of `type_id::EMPTY` in concatenate because the most natural implementation of the ArrowArrayStream processing is to run `from_arrow` on each chunk and then concatenate the outputs, and from the Python side we can produce chunks of all null arrays from arrow. Contributes to rapidsai#14926 Authors: - Vyas Ramasubramani (https://github.com/vyasr) Approvers: - Matthew Roeschke (https://github.com/mroeschke) - Robert Maynard (https://github.com/robertmaynard) - David Wendt (https://github.com/davidwendt) URL: rapidsai#15904 commit 08552f8 Author: Lawrence Mitchell <[email protected]> Date: Tue Jul 2 03:12:50 2024 +0100 Update cudf-polars for v1 release of polars (rapidsai#16149) Minor changes to the IR, which we adapt to, and request `polars>=1.0` in dependencies. Authors: - Lawrence Mitchell (https://github.com/wence-) - Thomas Li (https://github.com/lithomas1) - Vyas Ramasubramani (https://github.com/vyasr) Approvers: - Vyas Ramasubramani (https://github.com/vyasr) URL: rapidsai#16149 commit 760c15c Author: Kyle Edwards <[email protected]> Date: Mon Jul 1 14:27:30 2024 -0400 Use verify-alpha-spec hook (rapidsai#16144) With the deployment of rapids-build-backend, we need to make sure our dependencies have alpha specs. Authors: - Kyle Edwards (https://github.com/KyleFromNVIDIA) Approvers: - Bradley Dice (https://github.com/bdice) URL: rapidsai#16144 commit b691b1c Author: David Wendt <[email protected]> Date: Mon Jul 1 14:25:11 2024 -0400 Add stream parameter to cudf::io::text::multibyte_split (rapidsai#16034) Adds stream support the `cudf::io::text::multibyte_split` API. Also adds a stream test and deprecates an overloaded API. Authors: - David Wendt (https://github.com/davidwendt) Approvers: - Mark Harris (https://github.com/harrism) - Karthikeyan (https://github.com/karthikeyann) URL: rapidsai#16034 commit 5efd72f Author: Matthew Roeschke <[email protected]> Date: Mon Jul 1 07:37:12 2024 -1000 Ensure cudf objects can astype to any type when empty (rapidsai#16106) pandas allows objects to `astype` to any other type if the object is empty. The PR mirrors that behavior for cudf. This PR also more consistently uses `astype` instead of `as_*_column` and fixes a bug in `IntervalDtype.__eq__` discovered when writing a unit test for this bug. Authors: - Matthew Roeschke (https://github.com/mroeschke) - GALI PREM SAGAR (https://github.com/galipremsagar) Approvers: - GALI PREM SAGAR (https://github.com/galipremsagar) URL: rapidsai#16106 commit 51fb873 Merge: 599ce95 e932fbd Author: gpuCI <[email protected]> Date: Mon Jul 1 12:17:38 2024 -0400 Merge pull request rapidsai#16145 from rapidsai/branch-24.06 Forward-merge branch-24.06 into branch-24.08 commit e932fbd Author: Vyas Ramasubramani <[email protected]> Date: Mon Jul 1 09:17:32 2024 -0700 Add patch for incorrect cuco noexcept clauses (rapidsai#16077) [cuco previously marked a number of methods as noexcept that can in fact throw exceptions](NVIDIA/cuCollections#510). This causes problems for cudf functions that call these methods. The issue [was fixed in cuco upstream](NVIDIA/cuCollections#511), but we cannot easily update to the latest commit of cuco, especially in a patch fix for 24.06. This PR instead adds a rapids-cmake patch for the cuco clone to address this issue. The patch may be removed once we update to a commit of cuco that contains the necessary fix. Resolves rapidsai#16059 commit 599ce95 Author: Lawrence Mitchell <[email protected]> Date: Mon Jul 1 09:35:35 2024 +0100 Implement handlers for series literal in cudf-polars (rapidsai#16113) A query plan can contain a "literal" polars Series. Often, for example, when calling a contains-like function. To translate these, introduce a new `LiteralColumn` node to capture the concept and add an evaluation rule (converting from arrow). Since list-dtype Series need the same casting treatment as in dataframe scan case, factor the casting out into a utility, and take the opportunity to handled casting of nested lists correctly. Authors: - Lawrence Mitchell (https://github.com/wence-) Approvers: - Thomas Li (https://github.com/lithomas1) - Vyas Ramasubramani (https://github.com/vyasr) URL: rapidsai#16113 commit 3c3edfe Author: Yunsong Wang <[email protected]> Date: Fri Jun 28 13:58:22 2024 -0700 Update implementations to build with the latest cuco (rapidsai#15938) This PR updates existing libcudf to accommodate a cuco breaking change introduced in NVIDIA/cuCollections#479. It helps avoid breaking cudf when bumping the cuco version in `rapids-cmake`. Redundant equal/hash overloads will be removed once the version bump is done on the `rapids-cmake` end. Authors: - Yunsong Wang (https://github.com/PointKernel) Approvers: - David Wendt (https://github.com/davidwendt) - Nghia Truong (https://github.com/ttnghia) URL: rapidsai#15938 commit df88cf5 Author: Bradley Dice <[email protected]> Date: Fri Jun 28 15:40:52 2024 -0500 Use size_t to allow large conditional joins (rapidsai#16127) The conditional join kernels were using `cudf::size_type` where `std::size_t` was needed. This PR fixes that bug, which caused `cudaErrorIllegalAddress` as shown in rapidsai#16115. This closes rapidsai#16115. I did not add tests because we typically do not test very large workloads. However, I committed the test and reverted it in this PR, so there is a record of my validation code. Authors: - Bradley Dice (https://github.com/bdice) Approvers: - Vyas Ramasubramani (https://github.com/vyasr) - https://github.com/nvdbaranec - Yunsong Wang (https://github.com/PointKernel) URL: rapidsai#16127 commit fb12d98 Author: Robert Maynard <[email protected]> Date: Fri Jun 28 12:14:58 2024 -0400 Installed cudf header use cudf::allocate_like (rapidsai#16087) Remove usage of non public cudf::allocate_like from implementations in headers we install Authors: - Robert Maynard (https://github.com/robertmaynard) Approvers: - Yunsong Wang (https://github.com/PointKernel) - Nghia Truong (https://github.com/ttnghia) URL: rapidsai#16087 commit 78f4a8a Author: Robert Maynard <[email protected]> Date: Fri Jun 28 11:26:27 2024 -0400 Move common string utilities to public api (rapidsai#16070) As part of rapidsai#15982 a subset of the strings utility functions have been identified as being worth expsosing as part of the cudf public API. The `create_string_vector_from_column`, `get_offset64_threshold`, and `is_large_strings_enabled` are now made part of the public `cudf::strings` api. Authors: - Robert Maynard (https://github.com/robertmaynard) Approvers: - MithunR (https://github.com/mythrocks) - David Wendt (https://github.com/davidwendt) - Jayjeet Chakraborty (https://github.com/JayjeetAtGithub) - Lawrence Mitchell (https://github.com/wence-) URL: rapidsai#16070 commit a4b951a Author: nvdbaranec <[email protected]> Date: Fri Jun 28 10:20:42 2024 -0500 Templatization of fixed-width parquet decoding kernels. (rapidsai#15911) This PR merges all of the fixed-width parquet decoding kernels into a single templatized kernel that can be selectively instantiated with desired features (dictionary/no-dictionary, nested/non-nested, etc). It also adds support for (non-list) nested columns in this path. So structs do not have to use the much slower general decode kernel any more. A new benchmark was added specific to structs containing only fixed width columns. I added this because the performance improvement is fairly high (+20%) but we don't see it in the normal struct benchmarks because they include (and are dominated by) string decode times. The new benchmark shows: Before this PR: ``` | data_type | io_type | cardinality | run_length | bytes_per_second | peak_memory_usage | encoded_file_size | |-----------|---------------|-------------|------------|------------------|-------------------|-------------------| | STRUCT | DEVICE_BUFFER | 0 | 1 | 21071216823 | 1.047 GiB | 511.675 MiB | | STRUCT | DEVICE_BUFFER | 1000 | 1 | 18974392387 | 821.312 MiB | 128.884 MiB | | STRUCT | DEVICE_BUFFER | 0 | 32 | 20429356824 | 621.787 MiB | 28.141 MiB | | STRUCT | DEVICE_BUFFER | 1000 | 32 | 20572327813 | 598.421 MiB | 16.475 MiB | ``` After this PR: ``` | data_type | io_type | cardinality | run_length | bytes_per_second | peak_memory_usage | encoded_file_size | |-----------|---------------|-------------|------------|------------------|-------------------|-------------------| | STRUCT | DEVICE_BUFFER | 0 | 1 | 25805996399 | 1.047 GiB | 511.675 MiB | | STRUCT | DEVICE_BUFFER | 1000 | 1 | 22422306660 | 821.312 MiB | 128.884 MiB | | STRUCT | DEVICE_BUFFER | 0 | 32 | 24460694014 | 621.787 MiB | 28.141 MiB | | STRUCT | DEVICE_BUFFER | 1000 | 32 | 24674861214 | 598.421 MiB | 16.475 MiB | ``` Split-page decoding for fixed-width types + structs are also going through this new path. New test added. This brings us closer to eliminating the "general" kernel. The only things left that run through it are lists and booleans. This is PR 1 of 2, with the followup moving a lot of code around. At this point, I think it makes sense to start consolidating our files a bit. I also left some breadcrumbs (a few small commented out code blocks) in the core kernel `gpuDecodePageDataGeneric` for the next step of adding list support. They can be removed if people don't like them. Authors: - https://github.com/nvdbaranec Approvers: - Mike Wilson (https://github.com/hyperbolic2346) - Vukasin Milovanovic (https://github.com/vuule) - Muhammad Haseeb (https://github.com/mhaseeb123) URL: rapidsai#15911 commit e434fdb Author: David Wendt <[email protected]> Date: Fri Jun 28 10:57:01 2024 -0400 Update libcudf compiler requirements in contributing doc (rapidsai#16103) Updates the compiler requirements in the contributing document. Authors: - David Wendt (https://github.com/davidwendt) Approvers: - Bradley Dice (https://github.com/bdice) - Karthikeyan (https://github.com/karthikeyann) URL: rapidsai#16103 commit 565c0d1 Author: Matthew Murray <[email protected]> Date: Fri Jun 28 10:16:55 2024 -0400 Migrate lists/contains to pylibcudf (rapidsai#15981) Part of rapidsai#15162. Authors: - Matthew Murray (https://github.com/Matt711) Approvers: - Vyas Ramasubramani (https://github.com/vyasr) URL: rapidsai#15981 commit c40e0cc Author: Matthew Murray <[email protected]> Date: Fri Jun 28 10:10:31 2024 -0400 Add support for proxy `np.flatiter` objects (rapidsai#16107) Closes rapidsai#15388 Authors: - Matthew Murray (https://github.com/Matt711) Approvers: - Matthew Roeschke (https://github.com/mroeschke) URL: rapidsai#16107 commit 673d766 Author: Paul Mattione <[email protected]> Date: Fri Jun 28 09:38:57 2024 -0400 Make binary operators work between fixed-point and floating args (rapidsai#16116) Some of the binary operators in cuDF don't work between fixed_point and floating-point numbers after [this earlier PR](rapidsai#15438) removed the ability to construct and implicitly cast fixed_point numbers from floating point numbers. This PR restores that functionality by detecting and performing the necessary explicit casts, and adds tests for the supported operators. Note that the `binary_op_has_common_type` code is modeled after `has_common_type` found in traits.hpp. This closes [issue 16090](rapidsai#16090) Authors: - Paul Mattione (https://github.com/pmattione-nvidia) Approvers: - Jayjeet Chakraborty (https://github.com/JayjeetAtGithub) - Karthikeyan (https://github.com/karthikeyann) URL: rapidsai#16116 commit 224ac5b Author: David Wendt <[email protected]> Date: Fri Jun 28 09:26:37 2024 -0400 Add libcudf public/detail API pattern to developer guide (rapidsai#16086) Adds specific description for the public API to detail API function pattern to the libcudf developer guide. Also fixes some formatting issues and broken link. Authors: - David Wendt (https://github.com/davidwendt) Approvers: - Shruti Shivakumar (https://github.com/shrshi) - Karthikeyan (https://github.com/karthikeyann) URL: rapidsai#16086 commit 2b547dc Author: Matthew Roeschke <[email protected]> Date: Fri Jun 28 03:11:01 2024 -1000 Add ensure_index to not unnecessarily shallow copy cudf.Index (rapidsai#16117) The `cudf.Index` constructor will shallow copy a `cudf.Index` input. Sometimes, we just need to make sure an input is a `cudf.Index`, so created `ensure_index` (pandas has something similar) so we don't shallow copy these inputs unnecessarily Authors: - Matthew Roeschke (https://github.com/mroeschke) Approvers: - GALI PREM SAGAR (https://github.com/galipremsagar) URL: rapidsai#16117 commit 57862a3 Author: Robert Maynard <[email protected]> Date: Fri Jun 28 08:43:12 2024 -0400 stable_distinct public api now has a stream parameter (rapidsai#16068) As part of rapidsai#15982 we determined that the cudf `stable_distinct` public API needs to be updated so that a user provided stream can be provided. Authors: - Robert Maynard (https://github.com/robertmaynard) Approvers: - Nghia Truong (https://github.com/ttnghia) - Srinivas Yadav (https://github.com/srinivasyadav18) - Bradley Dice (https://github.com/bdice) URL: rapidsai#16068 commit 6b04fd3 Author: Mads R. B. Kristensen <[email protected]> Date: Fri Jun 28 12:31:18 2024 +0200 Memory Profiling (rapidsai#15866) Use [RMM's new memory profiler](rapidsai/rmm#1563) to profile all functions already decorated with `_cudf_nvtx_annotate`. Example ```python import cudf from cudf.utils.performance_tracking import print_memory_report cudf.set_option("memory_profiling", True) df1 = cudf.DataFrame({"a": [1, 2, 3]}) df2 = cudf.DataFrame({"a": [2, 2, 3]}) df3 = df1.merge(df2) print_memory_report() ``` Output: ``` Memory Profiling ================ Ordered by: memory_peak ncalls memory_peak memory_total filename:lineno(function) 1 272 688 /home/mkristensen/apps/miniforge3/envs/rmm-cudf-0527/lib/python3.11/site-packages/cudf/core/dataframe.py:4072(DataFrame.merge) 2 32 64 /home/mkristensen/apps/miniforge3/envs/rmm-cudf-0527/lib/python3.11/site-packages/cudf/core/dataframe.py:1043(DataFrame._init_from_dict_like) 2 32 64 /home/mkristensen/apps/miniforge3/envs/rmm-cudf-0527/lib/python3.11/site-packages/cudf/core/dataframe.py:690(DataFrame.__init__) 2 0 0 /home/mkristensen/apps/miniforge3/envs/rmm-cudf-0527/lib/python3.11/site-packages/cudf/core/dataframe.py:1131(DataFrame._align_input_series_indices) 7 0 0 /home/mkristensen/apps/miniforge3/envs/rmm-cudf-0527/lib/python3.11/site-packages/cudf/core/index.py:214(RangeIndex.__init__) 6 0 0 /home/mkristensen/apps/miniforge3/envs/rmm-cudf-0527/lib/python3.11/site-packages/cudf/core/index.py:424(RangeIndex.__len__) 4 0 0 /home/mkristensen/apps/miniforge3/envs/rmm-cudf-0527/lib/python3.11/site-packages/cudf/core/frame.py:271(Frame.__len__) 2 0 0 /home/mkristensen/apps/miniforge3/envs/rmm-cudf-0527/lib/python3.11/site-packages/cudf/core/dataframe.py:3195(DataFrame._insert) 2 0 0 /home/mkristensen/apps/miniforge3/envs/rmm-cudf-0527/lib/python3.11/site-packages/cudf/core/index.py:270(RangeIndex.name) 2 0 0 /home/mkristensen/apps/miniforge3/envs/rmm-cudf-0527/lib/python3.11/site-packages/cudf/core/index.py:369(RangeIndex.copy) 5 0 0 /home/mkristensen/apps/miniforge3/envs/rmm-cudf-0527/lib/python3.11/site-packages/cudf/core/frame.py:134(Frame._from_data) 2 0 0 /home/mkristensen/apps/miniforge3/envs/rmm-cudf-0527/lib/python3.11/site-packages/cudf/core/frame.py:1039(Frame._copy_type_metadata) 2 0 0 /home/mkristensen/apps/miniforge3/envs/rmm-cudf-0527/lib/python3.11/site-packages/cudf/core/indexed_frame.py:315(IndexedFrame._from_columns_like_self) ``` Authors: - Mads R. B. Kristensen (https://github.com/madsbk) Approvers: - Mark Harris (https://github.com/harrism) - Lawrence Mitchell (https://github.com/wence-) - Vyas Ramasubramani (https://github.com/vyasr) URL: rapidsai#15866 commit e35da6b Author: Lawrence Mitchell <[email protected]> Date: Fri Jun 28 09:54:03 2024 +0100 Implement Ternary copy_if_else (rapidsai#16114) A straightforward evaluation using `copy_if_else`. Authors: - Lawrence Mitchell (https://github.com/wence-) Approvers: - https://github.com/brandon-b-miller URL: rapidsai#16114 commit c847b98 Author: Lawrence Mitchell <[email protected]> Date: Thu Jun 27 21:33:29 2024 +0100 Finish implementation of cudf-polars boolean function handlers (rapidsai#16098) The missing nodes were `is_in`, `not` (both easy), `is_finite` and `is_infinite` (obtained by translating to `contains` calls). While here, remove the implementation of `IsBetween` and just translate to an expression with binary operations. This removes the need for special-casing scalar arguments to `IsBetween` and reproducing the code for binop evaluation. Authors: - Lawrence Mitchell (https://github.com/wence-) Approvers: - Vyas Ramasubramani (https://github.com/vyasr) URL: rapidsai#16098 commit 2ed69c9 Author: Matthew Roeschke <[email protected]> Date: Thu Jun 27 10:11:09 2024 -1000 Ensure MultiIndex.to_frame deep copies columns (rapidsai#16110) Additionally, this allows simplification in `MultiIndex.__repr__` which avoids a shallow copy and also caught a bug where `NaT` was not supposed to be quoted Authors: - Matthew Roeschke (https://github.com/mroeschke) Approvers: - Vyas Ramasubramani (https://github.com/vyasr) URL: rapidsai#16110 commit a71c249 Author: GALI PREM SAGAR <[email protected]> Date: Thu Jun 27 14:29:31 2024 -0500 Fix dtype errors in `StringArrays` (rapidsai#16111) This PR adds proxy classes for `ArrowStringArray` and `ArrowStringArrayNumpySemantics` that will increase the pandas test pass rate by 1%. Authors: - GALI PREM SAGAR (https://github.com/galipremsagar) Approvers: - Matthew Roeschke (https://github.com/mroeschke) URL: rapidsai#16111
…pp (#16168) Fixes a debug build error reporting an unused return value in `from_arrow_stream_test.cpp` ``` g++ -DFMT_HEADER_ONLY=1 -DLIBCUDACXX_ENABLE_EXPERIMENTAL_MEMORY_RESOURCE -DNANOARROW_DEBUG -DSPDLOG_ACTIVE_LEVEL=SPDLOG_LEVEL_INFO -DSPDLOG_FMT_EXTERNAL -DTHRUST_DEVICE_SYSTEM=THRUST_DEVICE_SYSTEM_CUDA -DTHRUST_DISABLE_ABI_NAMESPACE -DTHRUST_HOST_SYSTEM=THRUST_HOST_SYSTEM_CPP -DTHRUST_IGNORE_ABI_NAMESPACE_ERROR -I/cudf/cpp -I/cudf/cpp/src -I/cudf/cpp/build/_deps/dlpack-src/include -I/cudf/cpp/build/_deps/jitify-src -I/cudf/cpp/include -I/cudf/cpp/build/include -I/cudf/cpp/build/_deps/cccl-src/thrust/thrust/cmake/../.. -I/cudf/cpp/build/_deps/cccl-src/libcudacxx/lib/cmake/libcudacxx/../../../include -I/cudf/cpp/build/_deps/cccl-src/cub/cub/cmake/../.. -I/cudf/cpp/build/_deps/nanoarrow-src/src -I/cudf/cpp/build/_deps/nanoarrow-build/generated -isystem /cudf/cpp/build/_deps/gtest-src/googlemock/include -isystem /cudf/cpp/build/_deps/gtest-src/googlemock -isystem /cudf/cpp/build/_deps/gtest-src/googletest/include -isystem /cudf/cpp/build/_deps/gtest-src/googletest -fvisibility-inlines-hidden -fmessage-length=0 -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /conda/envs/rapids/include -fdiagnostics-color=always -I/conda/envs/rapids/targets/x86_64-linux/include -L/conda/envs/rapids/targets/x86_64-linux/lib -L/conda/envs/rapids/targets/x86_64-linux/lib/stubs -g -std=gnu++17 -fPIE -Wall -Werror -Wno-unknown-pragmas -Wno-error=deprecated-declarations -pthread -MD -MT tests/CMakeFiles/INTEROP_TEST.dir/interop/from_arrow_stream_test.cpp.o -MF tests/CMakeFiles/INTEROP_TEST.dir/interop/from_arrow_stream_test.cpp.o.d -o tests/CMakeFiles/INTEROP_TEST.dir/interop/from_arrow_stream_test.cpp.o -c /cudf/cpp/tests/interop/from_arrow_stream_test.cpp /cudf/cpp/tests/interop/from_arrow_stream_test.cpp: In static member function 'static int VectorOfArrays::get_schema(ArrowArrayStream*, ArrowSchema*)': /cudf/cpp/tests/interop/from_arrow_stream_test.cpp:49:24: error: ignoring return value of 'ArrowErrorCode cudfArrowSchemaDeepCopy(const ArrowSchema*, ArrowSchema*)' declared with attribute 'warn_unused_result' [-Werror=unused-result] 49 | ArrowSchemaDeepCopy(private_data->schema.get(), out_schema); cc1plus: all warnings being treated as errors ``` Adding a variable decorated with `[[maybe_unused]]` clears the error. Error introduced in #15904 Authors: - David Wendt (https://github.com/davidwendt) Approvers: - Vyas Ramasubramani (https://github.com/vyasr) - Shruti Shivakumar (https://github.com/shrshi) URL: #16168
Description
This PR replaces the internals of
from_arrow
in pylibcudf with an implementation that uses the Arrow C Data Interface using the Python Capsule interface. This allows us to decouple our Python builds from using pyarrow Cython (partially, we haven't replaced theto_arrow
conversion yet) and it will also allow us to support any other Python package that is a producer of the data interface.To support the above functionality, the following additional changes were needed in this PR:
ArrowArrayStream
objects since that is whatpyarrow.Table
produces. This function is a simple wrapper around the existingfrom_arrrow(ArrowArray)
API.from_arrow_host
API was added and for which we now require a basic overload for tests to pass. I did not add corresponding support forfrom_arrow_device
to avoid ballooning the scope of this PR, so that work can be done in a follow-up.type_id::EMPTY
in concatenate because the most natural implementation of the ArrowArrayStream processing is to runfrom_arrow
on each chunk and then concatenate the outputs, and from the Python side we can produce chunks of all null arrays from arrow.Contributes to #14926
Checklist