From 5d5b35d4e17c5370c5e3ad4addada60f0c54506f Mon Sep 17 00:00:00 2001 From: "Mads R. B. Kristensen" Date: Thu, 14 Nov 2024 13:39:09 +0100 Subject: [PATCH 1/9] Polars: DataFrame Serialization (#17062) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Use pylibcudf’s pack and unpack to implement Dask compatible serialization. Authors: - Mads R. B. Kristensen (https://github.com/madsbk) - Lawrence Mitchell (https://github.com/wence-) - Richard (Rick) Zamora (https://github.com/rjzamora) - Vyas Ramasubramani (https://github.com/vyasr) - Peter Andreas Entschev (https://github.com/pentschev) Approvers: - Matthew Murray (https://github.com/Matt711) - Lawrence Mitchell (https://github.com/wence-) - GALI PREM SAGAR (https://github.com/galipremsagar) - Bradley Dice (https://github.com/bdice) URL: https://github.com/rapidsai/cudf/pull/17062 --- ci/test_wheel_cudf_polars.sh | 4 +- dependencies.yaml | 30 ++++++++ .../cudf_polars/containers/dataframe.py | 72 ++++++++++++++++++- .../cudf_polars/experimental/__init__.py | 8 +++ .../experimental/dask_serialize.py | 59 +++++++++++++++ python/cudf_polars/pyproject.toml | 4 ++ .../tests/containers/test_dataframe.py | 23 ++++++ .../tests/experimental/__init__.py | 8 +++ .../tests/experimental/test_dask_serialize.py | 40 +++++++++++ 9 files changed, 245 insertions(+), 3 deletions(-) create mode 100644 python/cudf_polars/cudf_polars/experimental/__init__.py create mode 100644 python/cudf_polars/cudf_polars/experimental/dask_serialize.py create mode 100644 python/cudf_polars/tests/experimental/__init__.py create mode 100644 python/cudf_polars/tests/experimental/test_dask_serialize.py diff --git a/ci/test_wheel_cudf_polars.sh b/ci/test_wheel_cudf_polars.sh index 6c827406f78..3f818867d49 100755 --- a/ci/test_wheel_cudf_polars.sh +++ b/ci/test_wheel_cudf_polars.sh @@ -17,11 +17,11 @@ rapids-logger "Installing cudf_polars and its dependencies" # generate constraints (possibly pinning to oldest support versions of dependencies) rapids-generate-pip-constraints py_test_cudf_polars ./constraints.txt -# echo to expand wildcard before adding `[test]` requires for pip +# echo to expand wildcard before adding `[test,experimental]` requires for pip python -m pip install \ -v \ --constraint ./constraints.txt \ - "$(echo ./dist/cudf_polars_${RAPIDS_PY_CUDA_SUFFIX}*.whl)[test]" \ + "$(echo ./dist/cudf_polars_${RAPIDS_PY_CUDA_SUFFIX}*.whl)[test,experimental]" \ "$(echo ./dist/libcudf_${RAPIDS_PY_CUDA_SUFFIX}*.whl)" \ "$(echo ./dist/pylibcudf_${RAPIDS_PY_CUDA_SUFFIX}*.whl)" diff --git a/dependencies.yaml b/dependencies.yaml index 85368be0877..a4a4113d1e4 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -41,9 +41,11 @@ files: - test_cpp - test_python_common - test_python_cudf + - test_python_cudf_common - test_python_dask_cudf - test_python_pylibcudf - test_python_cudf_pandas + - test_python_cudf_polars test_static_build: output: none includes: @@ -59,6 +61,7 @@ files: - cuda_version - py_version - test_python_common + - test_python_cudf_common - test_python_cudf - test_python_cudf_pandas test_python_cudf: @@ -67,6 +70,7 @@ files: - cuda_version - py_version - test_python_common + - test_python_cudf_common - test_python_cudf test_python_other: output: none @@ -74,6 +78,7 @@ files: - cuda_version - py_version - test_python_common + - test_python_cudf_common - test_python_dask_cudf test_java: output: none @@ -152,6 +157,7 @@ files: key: test includes: - test_python_common + - test_python_cudf_common - test_python_cudf py_build_libcudf: output: pyproject @@ -216,6 +222,7 @@ files: key: test includes: - test_python_common + - test_python_cudf_common - test_python_pylibcudf py_test_pandas_cudf: output: pyproject @@ -248,6 +255,14 @@ files: includes: - run_cudf_polars - depends_on_pylibcudf + py_run_cudf_polars_experimental: + output: pyproject + pyproject_dir: python/cudf_polars + extras: + table: project.optional-dependencies + key: experimental + includes: + - run_cudf_polars_experimental py_test_cudf_polars: output: pyproject pyproject_dir: python/cudf_polars @@ -256,6 +271,7 @@ files: key: test includes: - test_python_common + - test_python_cudf_polars py_build_dask_cudf: output: pyproject pyproject_dir: python/dask_cudf @@ -281,6 +297,7 @@ files: key: test includes: - test_python_common + - test_python_cudf_common - test_python_dask_cudf py_build_cudf_kafka: output: pyproject @@ -313,6 +330,7 @@ files: key: test includes: - test_python_common + - test_python_cudf_common py_build_custreamz: output: pyproject pyproject_dir: python/custreamz @@ -337,6 +355,7 @@ files: key: test includes: - test_python_common + - test_python_cudf_common channels: - rapidsai - rapidsai-nightly @@ -730,6 +749,11 @@ dependencies: - output_types: [conda, requirements, pyproject] packages: - polars>=1.11,<1.14 + run_cudf_polars_experimental: + common: + - output_types: [conda, requirements, pyproject] + packages: + - rapids-dask-dependency==24.12.*,>=0.0.0a0 run_dask_cudf: common: - output_types: [conda, requirements, pyproject] @@ -779,6 +803,7 @@ dependencies: - pytest<8 - pytest-cov - pytest-xdist + test_python_cudf_common: specific: # Define additional constraints for testing with oldest dependencies. - output_types: [conda, requirements] @@ -884,6 +909,11 @@ dependencies: - pyarrow==14.0.1 - matrix: packages: + test_python_cudf_polars: + common: + - output_types: [conda, requirements, pyproject] + packages: + - *numpy depends_on_libcudf: common: - output_types: conda diff --git a/python/cudf_polars/cudf_polars/containers/dataframe.py b/python/cudf_polars/cudf_polars/containers/dataframe.py index 7560a0f5a64..36e0fbe370e 100644 --- a/python/cudf_polars/cudf_polars/containers/dataframe.py +++ b/python/cudf_polars/cudf_polars/containers/dataframe.py @@ -5,8 +5,9 @@ from __future__ import annotations +import pickle from functools import cached_property -from typing import TYPE_CHECKING, cast +from typing import TYPE_CHECKING, Any, cast import pyarrow as pa @@ -147,6 +148,75 @@ def from_table(cls, table: plc.Table, names: Sequence[str]) -> Self: Column(c, name=name) for c, name in zip(table.columns(), names, strict=True) ) + @classmethod + def deserialize( + cls, header: Mapping[str, Any], frames: tuple[memoryview, plc.gpumemoryview] + ) -> Self: + """ + Create a DataFrame from a serialized representation returned by `.serialize()`. + + Parameters + ---------- + header + The (unpickled) metadata required to reconstruct the object. + frames + Two-tuple of frames (a memoryview and a gpumemoryview). + + Returns + ------- + DataFrame + The deserialized DataFrame. + """ + packed_metadata, packed_gpu_data = frames + table = plc.contiguous_split.unpack_from_memoryviews( + packed_metadata, packed_gpu_data + ) + return cls( + Column(c, **kw) + for c, kw in zip(table.columns(), header["columns_kwargs"], strict=True) + ) + + def serialize( + self, + ) -> tuple[Mapping[str, Any], tuple[memoryview, plc.gpumemoryview]]: + """ + Serialize the table into header and frames. + + Follows the Dask serialization scheme with a picklable header (dict) and + a tuple of frames (in this case a contiguous host and device buffer). + + To enable dask support, dask serializers must be registered + + >>> from cudf_polars.experimental.dask_serialize import register + >>> register() + + Returns + ------- + header + A dict containing any picklable metadata required to reconstruct the object. + frames + Two-tuple of frames suitable for passing to `unpack_from_memoryviews` + """ + packed = plc.contiguous_split.pack(self.table) + + # Keyword arguments for `Column.__init__`. + columns_kwargs = [ + { + "is_sorted": col.is_sorted, + "order": col.order, + "null_order": col.null_order, + "name": col.name, + } + for col in self.columns + ] + header = { + "columns_kwargs": columns_kwargs, + # Dask Distributed uses "type-serialized" to dispatch deserialization + "type-serialized": pickle.dumps(type(self)), + "frame_count": 2, + } + return header, packed.release() + def sorted_like( self, like: DataFrame, /, *, subset: Set[str] | None = None ) -> Self: diff --git a/python/cudf_polars/cudf_polars/experimental/__init__.py b/python/cudf_polars/cudf_polars/experimental/__init__.py new file mode 100644 index 00000000000..6fd93bf5157 --- /dev/null +++ b/python/cudf_polars/cudf_polars/experimental/__init__.py @@ -0,0 +1,8 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. +# SPDX-License-Identifier: Apache-2.0 + +"""Experimental features, which can change without any deprecation period.""" + +from __future__ import annotations + +__all__: list[str] = [] diff --git a/python/cudf_polars/cudf_polars/experimental/dask_serialize.py b/python/cudf_polars/cudf_polars/experimental/dask_serialize.py new file mode 100644 index 00000000000..aae78e07690 --- /dev/null +++ b/python/cudf_polars/cudf_polars/experimental/dask_serialize.py @@ -0,0 +1,59 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. +# SPDX-License-Identifier: Apache-2.0 + +"""Dask serialization.""" + +from __future__ import annotations + +from distributed.protocol import dask_deserialize, dask_serialize +from distributed.protocol.cuda import cuda_deserialize, cuda_serialize +from distributed.utils import log_errors + +import pylibcudf as plc +import rmm + +from cudf_polars.containers import DataFrame + +__all__ = ["register"] + + +def register() -> None: + """Register dask serialization routines for DataFrames.""" + + @cuda_serialize.register(DataFrame) + def _(x: DataFrame): + with log_errors(): + header, frames = x.serialize() + return header, list(frames) # Dask expect a list of frames + + @cuda_deserialize.register(DataFrame) + def _(header, frames): + with log_errors(): + assert len(frames) == 2 + return DataFrame.deserialize(header, tuple(frames)) + + @dask_serialize.register(DataFrame) + def _(x: DataFrame): + with log_errors(): + header, (metadata, gpudata) = x.serialize() + + # For robustness, we check that the gpu data is contiguous + cai = gpudata.__cuda_array_interface__ + assert len(cai["shape"]) == 1 + assert cai["strides"] is None or cai["strides"] == (1,) + assert cai["typestr"] == "|u1" + nbytes = cai["shape"][0] + + # Copy the gpudata to host memory + gpudata_on_host = memoryview( + rmm.DeviceBuffer(ptr=gpudata.ptr, size=nbytes).copy_to_host() + ) + return header, (metadata, gpudata_on_host) + + @dask_deserialize.register(DataFrame) + def _(header, frames) -> DataFrame: + with log_errors(): + assert len(frames) == 2 + # Copy the second frame (the gpudata in host memory) back to the gpu + frames = frames[0], plc.gpumemoryview(rmm.DeviceBuffer.to_device(frames[1])) + return DataFrame.deserialize(header, frames) diff --git a/python/cudf_polars/pyproject.toml b/python/cudf_polars/pyproject.toml index 785e87391e7..e665d42ab1a 100644 --- a/python/cudf_polars/pyproject.toml +++ b/python/cudf_polars/pyproject.toml @@ -35,10 +35,14 @@ classifiers = [ [project.optional-dependencies] test = [ + "numpy>=1.23,<3.0a0", "pytest-cov", "pytest-xdist", "pytest<8", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. +experimental = [ + "rapids-dask-dependency==24.12.*,>=0.0.0a0", +] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. [project.urls] Homepage = "https://github.com/rapidsai/cudf" diff --git a/python/cudf_polars/tests/containers/test_dataframe.py b/python/cudf_polars/tests/containers/test_dataframe.py index d68c8d90163..83729371a9c 100644 --- a/python/cudf_polars/tests/containers/test_dataframe.py +++ b/python/cudf_polars/tests/containers/test_dataframe.py @@ -3,9 +3,11 @@ from __future__ import annotations +import pyarrow as pa import pytest import polars as pl +from polars.testing.asserts import assert_frame_equal import pylibcudf as plc @@ -161,3 +163,24 @@ def test_empty_name_roundtrips_overlap(): def test_empty_name_roundtrips_no_overlap(): df = pl.LazyFrame({"": [1, 2, 3], "b": [4, 5, 6]}) assert_gpu_result_equal(df) + + +@pytest.mark.parametrize( + "arrow_tbl", + [ + pa.table([]), + pa.table({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}), + pa.table({"a": [1, 2, 3]}), + pa.table({"a": [1], "b": [2], "c": [3]}), + pa.table({"a": ["a", "bb", "ccc"]}), + pa.table({"a": [1, 2, None], "b": [None, 3, 4]}), + ], +) +def test_serialization_roundtrip(arrow_tbl): + plc_tbl = plc.interop.from_arrow(arrow_tbl) + df = DataFrame.from_table(plc_tbl, names=arrow_tbl.column_names) + + header, frames = df.serialize() + res = DataFrame.deserialize(header, frames) + + assert_frame_equal(df.to_polars(), res.to_polars()) diff --git a/python/cudf_polars/tests/experimental/__init__.py b/python/cudf_polars/tests/experimental/__init__.py new file mode 100644 index 00000000000..db71916f3c4 --- /dev/null +++ b/python/cudf_polars/tests/experimental/__init__.py @@ -0,0 +1,8 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. +# SPDX-License-Identifier: Apache-2.0 + +"""Testing experimental features.""" + +from __future__ import annotations + +__all__: list[str] = [] diff --git a/python/cudf_polars/tests/experimental/test_dask_serialize.py b/python/cudf_polars/tests/experimental/test_dask_serialize.py new file mode 100644 index 00000000000..e556b7e4445 --- /dev/null +++ b/python/cudf_polars/tests/experimental/test_dask_serialize.py @@ -0,0 +1,40 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +import pyarrow as pa +import pytest +from distributed.protocol import deserialize, serialize + +from polars.testing.asserts import assert_frame_equal + +import pylibcudf as plc + +from cudf_polars.containers import DataFrame +from cudf_polars.experimental.dask_serialize import register + +# Must register serializers before running tests +register() + + +@pytest.mark.parametrize( + "arrow_tbl", + [ + pa.table([]), + pa.table({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}), + pa.table({"a": [1, 2, 3]}), + pa.table({"a": [1], "b": [2], "c": [3]}), + pa.table({"a": ["a", "bb", "ccc"]}), + pa.table({"a": [1, 2, None], "b": [None, 3, 4]}), + ], +) +@pytest.mark.parametrize("protocol", ["cuda", "dask"]) +def test_dask_serialization_roundtrip(arrow_tbl, protocol): + plc_tbl = plc.interop.from_arrow(arrow_tbl) + df = DataFrame.from_table(plc_tbl, names=arrow_tbl.column_names) + + header, frames = serialize(df, on_error="raise", serializers=[protocol]) + res = deserialize(header, frames, deserializers=[protocol]) + + assert_frame_equal(df.to_polars(), res.to_polars()) From 4cd40eedefdfe713df1a263a4fa0e723995520c5 Mon Sep 17 00:00:00 2001 From: Chong Gao Date: Thu, 14 Nov 2024 23:18:19 +0800 Subject: [PATCH 2/9] Java JNI for Multiple contains (#17281) This is Java JNI interface for [multiple contains PR](https://github.com/rapidsai/cudf/pull/16900) Authors: - Chong Gao (https://github.com/res-life) Approvers: - Alessandro Bellina (https://github.com/abellina) - Robert (Bobby) Evans (https://github.com/revans2) URL: https://github.com/rapidsai/cudf/pull/17281 --- .../main/java/ai/rapids/cudf/ColumnView.java | 37 +++++++++++++++++++ java/src/main/native/src/ColumnViewJni.cpp | 20 ++++++++++ .../java/ai/rapids/cudf/ColumnVectorTest.java | 24 ++++++++++++ 3 files changed, 81 insertions(+) diff --git a/java/src/main/java/ai/rapids/cudf/ColumnView.java b/java/src/main/java/ai/rapids/cudf/ColumnView.java index 6bd4e06c47e..098c68f0596 100644 --- a/java/src/main/java/ai/rapids/cudf/ColumnView.java +++ b/java/src/main/java/ai/rapids/cudf/ColumnView.java @@ -3332,6 +3332,36 @@ public final ColumnVector stringContains(Scalar compString) { return new ColumnVector(stringContains(getNativeView(), compString.getScalarHandle())); } + /** + * @brief Searches for the given target strings within each string in the provided column + * + * Each column in the result table corresponds to the result for the target string at the same + * ordinal. i.e. 0th column is the BOOL8 column result for the 0th target string, 1th for 1th, + * etc. + * + * If the target is not found for a string, false is returned for that entry in the output column. + * If the target is an empty string, true is returned for all non-null entries in the output column. + * + * Any null input strings return corresponding null entries in the output columns. + * + * input = ["a", "b", "c"] + * targets = ["a", "c"] + * output is a table with two boolean columns: + * column 0: [true, false, false] + * column 1: [false, false, true] + * + * @param targets UTF-8 encoded strings to search for in each string in `input` + * @return BOOL8 columns + */ + public final ColumnVector[] stringContains(ColumnView targets) { + assert type.equals(DType.STRING) : "column type must be a String"; + assert targets.getType().equals(DType.STRING) : "targets type must be a string"; + assert targets.getNullCount() == 0 : "targets must not contain nulls"; + assert targets.getRowCount() > 0 : "targets must not be empty"; + long[] resultPointers = stringContainsMulti(getNativeView(), targets.getNativeView()); + return Arrays.stream(resultPointers).mapToObj(ColumnVector::new).toArray(ColumnVector[]::new); + } + /** * Replaces values less than `lo` in `input` with `lo`, * and values greater than `hi` with `hi`. @@ -4437,6 +4467,13 @@ private static native long stringReplaceWithBackrefs(long columnView, String pat */ private static native long stringContains(long cudfViewHandle, long compString) throws CudfException; + /** + * Native method for searching for the given target strings within each string in the provided column. + * @param cudfViewHandle native handle of the cudf::column_view being operated on. + * @param targetViewHandle handle of the column view containing the strings being searched for. + */ + private static native long[] stringContainsMulti(long cudfViewHandle, long targetViewHandle) throws CudfException; + /** * Native method for extracting results from a regex program pattern. Returns a table handle. * diff --git a/java/src/main/native/src/ColumnViewJni.cpp b/java/src/main/native/src/ColumnViewJni.cpp index 72f0ad19912..90902a24bbe 100644 --- a/java/src/main/native/src/ColumnViewJni.cpp +++ b/java/src/main/native/src/ColumnViewJni.cpp @@ -64,6 +64,7 @@ #include #include #include +#include #include #include #include @@ -2827,4 +2828,23 @@ JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_toHex(JNIEnv* env, jclass } CATCH_STD(env, 0); } + +JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_ColumnView_stringContainsMulti( + JNIEnv* env, jobject j_object, jlong j_view_handle, jlong j_target_view_handle) +{ + JNI_NULL_CHECK(env, j_view_handle, "column is null", 0); + JNI_NULL_CHECK(env, j_target_view_handle, "targets is null", 0); + + try { + cudf::jni::auto_set_device(env); + auto* column_view = reinterpret_cast(j_view_handle); + auto* targets_view = reinterpret_cast(j_target_view_handle); + auto const strings_column = cudf::strings_column_view(*column_view); + auto const targets_column = cudf::strings_column_view(*targets_view); + auto contains_results = cudf::strings::contains_multiple(strings_column, targets_column); + return cudf::jni::convert_table_for_return(env, std::move(contains_results)); + } + CATCH_STD(env, 0); +} + } // extern "C" diff --git a/java/src/test/java/ai/rapids/cudf/ColumnVectorTest.java b/java/src/test/java/ai/rapids/cudf/ColumnVectorTest.java index 14c290b300a..d1a1ff2c95c 100644 --- a/java/src/test/java/ai/rapids/cudf/ColumnVectorTest.java +++ b/java/src/test/java/ai/rapids/cudf/ColumnVectorTest.java @@ -3828,6 +3828,30 @@ void testStringOpsEmpty() { } } + @Test + void testStringContainsMulti() { + ColumnVector[] results = null; + try (ColumnVector haystack = ColumnVector.fromStrings("tést strings", + "Héllo cd", + "1 43 42 7", + "scala spark 42 other", + null, + ""); + ColumnVector targets = ColumnVector.fromStrings("é", "42"); + ColumnVector expected0 = ColumnVector.fromBoxedBooleans(true, true, false, false, null, false); + ColumnVector expected1 = ColumnVector.fromBoxedBooleans(false, false, true, true, null, false)) { + results = haystack.stringContains(targets); + assertColumnsAreEqual(results[0], expected0); + assertColumnsAreEqual(results[1], expected1); + } finally { + if (results != null) { + for (ColumnVector c : results) { + c.close(); + } + } + } + } + @Test void testStringFindOperations() { try (ColumnVector testStrings = ColumnVector.fromStrings("", null, "abCD", "1a\"\u0100B1", "a\"\u0100B1", "1a\"\u0100B", From d93c3fc65a7f29918239cc4ad50929a1ad3829e1 Mon Sep 17 00:00:00 2001 From: Vyas Ramasubramani Date: Thu, 14 Nov 2024 11:33:39 -0800 Subject: [PATCH 3/9] Add version config (#17312) Resolves #3155. Authors: - Vyas Ramasubramani (https://github.com/vyasr) Approvers: - Bradley Dice (https://github.com/bdice) - Robert Maynard (https://github.com/robertmaynard) URL: https://github.com/rapidsai/cudf/pull/17312 --- cpp/CMakeLists.txt | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index a3815748e2c..1c13f65fe3c 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -36,6 +36,8 @@ if(CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA" AND CMAKE_CUDA_COMPILER_VERSION VERS ) endif() +rapids_cmake_write_version_file(include/cudf/version_config.hpp) + # Needed because GoogleBenchmark changes the state of FindThreads.cmake, causing subsequent runs to # have different values for the `Threads::Threads` target. Setting this flag ensures # `Threads::Threads` is the same value in first run and subsequent runs. @@ -1126,6 +1128,9 @@ install( DESTINATION ${lib_dir} EXPORT cudf-exports ) +install(FILES ${CUDF_BINARY_DIR}/include/cudf/version_config.hpp + DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/cudf +) set(_components_export_string) if(TARGET cudftestutil) From a7194f612adfb5ab9591d5f1bfb5bde4efe97eb7 Mon Sep 17 00:00:00 2001 From: Vukasin Milovanovic Date: Thu, 14 Nov 2024 11:37:46 -0800 Subject: [PATCH 4/9] Fix reading of single-row unterminated CSV files (#17305) Fixed the logic in the CSV reader that led to empty output instead of producing a table with a single column and one row. Added tests to make sure the new logic does not cause regressions. Also did some small clean up around the fix. Authors: - Vukasin Milovanovic (https://github.com/vuule) Approvers: - Bradley Dice (https://github.com/bdice) - David Wendt (https://github.com/davidwendt) URL: https://github.com/rapidsai/cudf/pull/17305 --- cpp/src/io/csv/reader_impl.cu | 42 +++++++++++++----------------- python/cudf/cudf/tests/test_csv.py | 17 ++++++++++++ 2 files changed, 35 insertions(+), 24 deletions(-) diff --git a/cpp/src/io/csv/reader_impl.cu b/cpp/src/io/csv/reader_impl.cu index 72fca75c56b..6c84b53db46 100644 --- a/cpp/src/io/csv/reader_impl.cu +++ b/cpp/src/io/csv/reader_impl.cu @@ -118,47 +118,41 @@ string removeQuotes(string str, char quotechar) } /** - * @brief Parse the first row to set the column names in the raw_csv parameter. - * The first row can be either the header row, or the first data row + * @brief Parse a row of input to get the column names. The row can either be the header, or the + * first data row. If the header is not used, column names are generated automatically. */ -std::vector get_column_names(std::vector const& header, +std::vector get_column_names(std::vector const& row, parse_options_view const& parse_opts, int header_row, std::string prefix) { - std::vector col_names; - - // If there is only a single character then it would be the terminator - if (header.size() <= 1) { return col_names; } - - std::vector first_row = header; + // Empty row, return empty column names vector + if (row.empty()) { return {}; } + std::vector col_names; bool quotation = false; - for (size_t pos = 0, prev = 0; pos < first_row.size(); ++pos) { + for (size_t pos = 0, prev = 0; pos < row.size(); ++pos) { // Flip the quotation flag if current character is a quotechar - if (first_row[pos] == parse_opts.quotechar) { - quotation = !quotation; - } + if (row[pos] == parse_opts.quotechar) { quotation = !quotation; } // Check if end of a column/row - else if (pos == first_row.size() - 1 || - (!quotation && first_row[pos] == parse_opts.terminator) || - (!quotation && first_row[pos] == parse_opts.delimiter)) { + if (pos == row.size() - 1 || (!quotation && row[pos] == parse_opts.terminator) || + (!quotation && row[pos] == parse_opts.delimiter)) { // This is the header, add the column name if (header_row >= 0) { // Include the current character, in case the line is not terminated int col_name_len = pos - prev + 1; // Exclude the delimiter/terminator is present - if (first_row[pos] == parse_opts.delimiter || first_row[pos] == parse_opts.terminator) { + if (row[pos] == parse_opts.delimiter || row[pos] == parse_opts.terminator) { --col_name_len; } // Also exclude '\r' character at the end of the column name if it's // part of the terminator - if (col_name_len > 0 && parse_opts.terminator == '\n' && first_row[pos] == '\n' && - first_row[pos - 1] == '\r') { + if (col_name_len > 0 && parse_opts.terminator == '\n' && row[pos] == '\n' && + row[pos - 1] == '\r') { --col_name_len; } - string const new_col_name(first_row.data() + prev, col_name_len); + string const new_col_name(row.data() + prev, col_name_len); col_names.push_back(removeQuotes(new_col_name, parse_opts.quotechar)); } else { // This is the first data row, add the automatically generated name @@ -166,14 +160,14 @@ std::vector get_column_names(std::vector const& header, } // Stop parsing when we hit the line terminator; relevant when there is - // a blank line following the header. In this case, first_row includes + // a blank line following the header. In this case, row includes // multiple line terminators at the end, as the new recStart belongs to // a line that comes after the blank line(s) - if (!quotation && first_row[pos] == parse_opts.terminator) { break; } + if (!quotation && row[pos] == parse_opts.terminator) { break; } // Skip adjacent delimiters if delim_whitespace is set - while (parse_opts.multi_delimiter && pos < first_row.size() && - first_row[pos] == parse_opts.delimiter && first_row[pos + 1] == parse_opts.delimiter) { + while (parse_opts.multi_delimiter && pos < row.size() && row[pos] == parse_opts.delimiter && + row[pos + 1] == parse_opts.delimiter) { ++pos; } prev = pos + 1; diff --git a/python/cudf/cudf/tests/test_csv.py b/python/cudf/cudf/tests/test_csv.py index 8800275bf67..ac772c47e3a 100644 --- a/python/cudf/cudf/tests/test_csv.py +++ b/python/cudf/cudf/tests/test_csv.py @@ -2277,3 +2277,20 @@ def test_read_header_none_pandas_compat_column_type(): result = cudf.read_csv(StringIO(data), header=None).columns expected = pd.read_csv(StringIO(data), header=None).columns pd.testing.assert_index_equal(result, expected, exact=True) + + +@pytest.mark.parametrize("buffer", ["1", '"one"']) +def test_read_single_unterminated_row(buffer): + gdf = cudf.read_csv(StringIO(buffer), header=None) + assert_eq(gdf.shape, (1, 1)) + + +@pytest.mark.parametrize("buffer", ["\n", "\r\n"]) +def test_read_empty_only_row(buffer): + gdf = cudf.read_csv(StringIO(buffer), header=None) + assert_eq(gdf.shape, (0, 0)) + + +def test_read_empty_only_row_custom_terminator(): + gdf = cudf.read_csv(StringIO("*"), header=None, lineterminator="*") + assert_eq(gdf.shape, (0, 0)) From 66c5a2d724f14b7062d97c87cbf782fca9f9fabb Mon Sep 17 00:00:00 2001 From: James Lamb Date: Thu, 14 Nov 2024 17:26:29 -0600 Subject: [PATCH 5/9] prefer wheel-provided libcudf.so in load_library(), use RTLD_LOCAL (#17316) Contributes to https://github.com/rapidsai/build-planning/issues/118 Modifies `libcudf.load_library()` in the following ways: * prefer wheel-provided `libcudf.so` to system installation * expose environment variable `RAPIDS_LIBCUDF_PREFER_SYSTEM_LIBRARY` for switching that preference * load `libcudf.so` with `RTLD_LOCAL`, to prevent adding symbols to the global namespace ([dlopen docs](https://linux.die.net/man/3/dlopen)) ## Notes for Reviewers ### How I tested this Locally (x86_64, CUDA 12, Python 3.12), built `libcudf`, `pylibcudf`, and `cudf` wheels from this branch, then `libcuspatial` and `cuspatial` from the corresponding cuspatial branch. Ran `cuspatial`'s unit tests, and tried setting the environment variable and inspecting `ld`'s logs to confirm that the environment variable changed the loading and search behavior. e.g. ```shell # clear ld cache to avoid cheating rm -f /etc/ld.so.cache ldconfig # try using an env variable to say "prefer the system-installed version" LD_DEBUG=libs \ LD_DEBUG_OUTPUT=/tmp/out.txt \ RAPIDS_LIBCUDF_PREFER_SYSTEM_LIBRARY=true \ python -c "import cuspatial; print(cuspatial.__version__)" cat /tmp/out.txt.* > prefer-system.txt # (then manually looked through those logs to confirm it searched places like /usr/lib64 and /lib64) ``` # Authors: - James Lamb (https://github.com/jameslamb) Approvers: - Vyas Ramasubramani (https://github.com/vyasr) - Bradley Dice (https://github.com/bdice) URL: https://github.com/rapidsai/cudf/pull/17316 --- python/libcudf/libcudf/load.py | 70 ++++++++++++++++++++++++---------- 1 file changed, 49 insertions(+), 21 deletions(-) diff --git a/python/libcudf/libcudf/load.py b/python/libcudf/libcudf/load.py index bf27ecfa7f5..a91fbb7aecf 100644 --- a/python/libcudf/libcudf/load.py +++ b/python/libcudf/libcudf/load.py @@ -16,8 +16,36 @@ import ctypes import os +# Loading with RTLD_LOCAL adds the library itself to the loader's +# loaded library cache without loading any symbols into the global +# namespace. This allows libraries that express a dependency on +# this library to be loaded later and successfully satisfy this dependency +# without polluting the global symbol table with symbols from +# libcudf that could conflict with symbols from other DSOs. +PREFERRED_LOAD_FLAG = ctypes.RTLD_LOCAL + + +def _load_system_installation(soname: str): + """Try to dlopen() the library indicated by ``soname`` + Raises ``OSError`` if library cannot be loaded. + """ + return ctypes.CDLL(soname, PREFERRED_LOAD_FLAG) + + +def _load_wheel_installation(soname: str): + """Try to dlopen() the library indicated by ``soname`` + + Returns ``None`` if the library cannot be loaded. + """ + if os.path.isfile( + lib := os.path.join(os.path.dirname(__file__), "lib64", soname) + ): + return ctypes.CDLL(lib, PREFERRED_LOAD_FLAG) + return None + def load_library(): + """Dynamically load libcudf.so and its dependencies""" try: # libkvikio must be loaded before libcudf because libcudf references its symbols import libkvikio @@ -29,28 +57,28 @@ def load_library(): # we assume the library is discoverable on system paths. pass - # Dynamically load libcudf.so. Prefer a system library if one is present to - # avoid clobbering symbols that other packages might expect, but if no - # other library is present use the one in the wheel. + prefer_system_installation = ( + os.getenv("RAPIDS_LIBCUDF_PREFER_SYSTEM_LIBRARY", "false").lower() + != "false" + ) + + soname = "libcudf.so" libcudf_lib = None - try: - libcudf_lib = ctypes.CDLL("libcudf.so", ctypes.RTLD_GLOBAL) - except OSError: - # If neither of these directories contain the library, we assume we are in an - # environment where the C++ library is already installed somewhere else and the - # CMake build of the libcudf Python package was a no-op. - # - # Note that this approach won't work for real editable installs of the libcudf package. - # scikit-build-core has limited support for importlib.resources so there isn't a clean - # way to support that case yet. - for lib_dir in ("lib", "lib64"): - if os.path.isfile( - lib := os.path.join( - os.path.dirname(__file__), lib_dir, "libcudf.so" - ) - ): - libcudf_lib = ctypes.CDLL(lib, ctypes.RTLD_GLOBAL) - break + if prefer_system_installation: + # Prefer a system library if one is present to + # avoid clobbering symbols that other packages might expect, but if no + # other library is present use the one in the wheel. + try: + libcudf_lib = _load_system_installation(soname) + except OSError: + libcudf_lib = _load_wheel_installation(soname) + else: + # Prefer the libraries bundled in this package. If they aren't found + # (which might be the case in builds where the library was prebuilt before + # packaging the wheel), look for a system installation. + libcudf_lib = _load_wheel_installation(soname) + if libcudf_lib is None: + libcudf_lib = _load_system_installation(soname) # The caller almost never needs to do anything with this library, but no # harm in offering the option since this object at least provides a handle From 927ae9c19e9ae2bcf1a0d5db4b61380e325f5bc9 Mon Sep 17 00:00:00 2001 From: Mike Wilson Date: Thu, 14 Nov 2024 19:14:19 -0500 Subject: [PATCH 6/9] Do not exclude nanoarrow and flatbuffers from installation if statically linked (#17322) Had an issue crop up in spark-rapids-jni where we statically link arrow and the build started to fail due to change #17308. Authors: - Mike Wilson (https://github.com/hyperbolic2346) Approvers: - Gera Shegalov (https://github.com/gerashegalov) - Vyas Ramasubramani (https://github.com/vyasr) - Bradley Dice (https://github.com/bdice) - Kyle Edwards (https://github.com/KyleFromNVIDIA) URL: https://github.com/rapidsai/cudf/pull/17322 --- cpp/cmake/thirdparty/get_flatbuffers.cmake | 9 +++++++-- cpp/cmake/thirdparty/get_nanoarrow.cmake | 9 +++++++-- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/cpp/cmake/thirdparty/get_flatbuffers.cmake b/cpp/cmake/thirdparty/get_flatbuffers.cmake index 6a889566300..39521049c85 100644 --- a/cpp/cmake/thirdparty/get_flatbuffers.cmake +++ b/cpp/cmake/thirdparty/get_flatbuffers.cmake @@ -15,14 +15,19 @@ # Use CPM to find or clone flatbuffers function(find_and_configure_flatbuffers VERSION) + if(NOT BUILD_SHARED_LIBS) + set(_exclude_from_all EXCLUDE_FROM_ALL FALSE) + else() + set(_exclude_from_all EXCLUDE_FROM_ALL TRUE) + endif() + rapids_cpm_find( flatbuffers ${VERSION} GLOBAL_TARGETS flatbuffers CPM_ARGS GIT_REPOSITORY https://github.com/google/flatbuffers.git GIT_TAG v${VERSION} - GIT_SHALLOW TRUE - EXCLUDE_FROM_ALL TRUE + GIT_SHALLOW TRUE ${_exclude_from_all} ) rapids_export_find_package_root( diff --git a/cpp/cmake/thirdparty/get_nanoarrow.cmake b/cpp/cmake/thirdparty/get_nanoarrow.cmake index 4fc742dea2e..c440643037b 100644 --- a/cpp/cmake/thirdparty/get_nanoarrow.cmake +++ b/cpp/cmake/thirdparty/get_nanoarrow.cmake @@ -19,14 +19,19 @@ function(find_and_configure_nanoarrow) set(cudf_patch_dir "${CMAKE_CURRENT_FUNCTION_LIST_DIR}/patches") rapids_cpm_package_override("${cudf_patch_dir}/nanoarrow_override.json") + if(NOT BUILD_SHARED_LIBS) + set(_exclude_from_all EXCLUDE_FROM_ALL FALSE) + else() + set(_exclude_from_all EXCLUDE_FROM_ALL TRUE) + endif() + # Currently we need to always build nanoarrow so we don't pickup a previous installed version set(CPM_DOWNLOAD_nanoarrow ON) rapids_cpm_find( nanoarrow 0.6.0.dev GLOBAL_TARGETS nanoarrow CPM_ARGS - OPTIONS "BUILD_SHARED_LIBS OFF" "NANOARROW_NAMESPACE cudf" - EXCLUDE_FROM_ALL TRUE + OPTIONS "BUILD_SHARED_LIBS OFF" "NANOARROW_NAMESPACE cudf" ${_exclude_from_all} ) set_target_properties(nanoarrow PROPERTIES POSITION_INDEPENDENT_CODE ON) rapids_export_find_package_root(BUILD nanoarrow "${nanoarrow_BINARY_DIR}" EXPORT_SET cudf-exports) From 8a9131a124321e8f3f604750811b716604830faf Mon Sep 17 00:00:00 2001 From: "Robert (Bobby) Evans" Date: Thu, 14 Nov 2024 18:59:36 -0600 Subject: [PATCH 7/9] Update java datetime APIs to match CUDF. (#17329) This updates the java APIs related to datetime processing so that they match the CUDF APIs. Authors: - Robert (Bobby) Evans (https://github.com/revans2) Approvers: - MithunR (https://github.com/mythrocks) - Jason Lowe (https://github.com/jlowe) - Gera Shegalov (https://github.com/gerashegalov) URL: https://github.com/rapidsai/cudf/pull/17329 --- .../main/java/ai/rapids/cudf/ColumnView.java | 104 ++++++--- .../ai/rapids/cudf/DateTimeComponent.java | 74 +++++++ .../cudf/DateTimeRoundingFrequency.java | 38 ++++ java/src/main/native/src/ColumnViewJni.cpp | 143 ++++++++----- .../cudf/TimestampColumnVectorTest.java | 198 +++++++++++++++++- 5 files changed, 477 insertions(+), 80 deletions(-) create mode 100644 java/src/main/java/ai/rapids/cudf/DateTimeComponent.java create mode 100644 java/src/main/java/ai/rapids/cudf/DateTimeRoundingFrequency.java diff --git a/java/src/main/java/ai/rapids/cudf/ColumnView.java b/java/src/main/java/ai/rapids/cudf/ColumnView.java index 098c68f0596..b9239af0c1b 100644 --- a/java/src/main/java/ai/rapids/cudf/ColumnView.java +++ b/java/src/main/java/ai/rapids/cudf/ColumnView.java @@ -917,6 +917,16 @@ public final ColumnVector mergeAndSetValidity(BinaryOp mergeOp, ColumnView... co // DATE/TIME ///////////////////////////////////////////////////////////////////////////// + /** + * Extract a particular date time component from a timestamp. + * @param component what should be extracted + * @return a column with the extracted information in it. + */ + public final ColumnVector extractDateTimeComponent(DateTimeComponent component) { + assert type.isTimestampType(); + return new ColumnVector(extractDateTimeComponent(getNativeView(), component.getNativeId())); + } + /** * Get year from a timestamp. *

@@ -925,8 +935,7 @@ public final ColumnVector mergeAndSetValidity(BinaryOp mergeOp, ColumnView... co * @return - A new INT16 vector allocated on the GPU. */ public final ColumnVector year() { - assert type.isTimestampType(); - return new ColumnVector(year(getNativeView())); + return extractDateTimeComponent(DateTimeComponent.YEAR); } /** @@ -937,8 +946,7 @@ public final ColumnVector year() { * @return - A new INT16 vector allocated on the GPU. */ public final ColumnVector month() { - assert type.isTimestampType(); - return new ColumnVector(month(getNativeView())); + return extractDateTimeComponent(DateTimeComponent.MONTH); } /** @@ -949,8 +957,7 @@ public final ColumnVector month() { * @return - A new INT16 vector allocated on the GPU. */ public final ColumnVector day() { - assert type.isTimestampType(); - return new ColumnVector(day(getNativeView())); + return extractDateTimeComponent(DateTimeComponent.DAY); } /** @@ -961,8 +968,7 @@ public final ColumnVector day() { * @return - A new INT16 vector allocated on the GPU. */ public final ColumnVector hour() { - assert type.hasTimeResolution(); - return new ColumnVector(hour(getNativeView())); + return extractDateTimeComponent(DateTimeComponent.HOUR); } /** @@ -973,8 +979,7 @@ public final ColumnVector hour() { * @return - A new INT16 vector allocated on the GPU. */ public final ColumnVector minute() { - assert type.hasTimeResolution(); - return new ColumnVector(minute(getNativeView())); + return extractDateTimeComponent(DateTimeComponent.MINUTE); } /** @@ -985,8 +990,7 @@ public final ColumnVector minute() { * @return A new INT16 vector allocated on the GPU. */ public final ColumnVector second() { - assert type.hasTimeResolution(); - return new ColumnVector(second(getNativeView())); + return extractDateTimeComponent(DateTimeComponent.SECOND); } /** @@ -997,8 +1001,7 @@ public final ColumnVector second() { * @return A new INT16 vector allocated on the GPU. Monday=1, ..., Sunday=7 */ public final ColumnVector weekDay() { - assert type.isTimestampType(); - return new ColumnVector(weekDay(getNativeView())); + return extractDateTimeComponent(DateTimeComponent.WEEKDAY); } /** @@ -1045,6 +1048,16 @@ public final ColumnVector addCalendricalMonths(ColumnView months) { return new ColumnVector(addCalendricalMonths(getNativeView(), months.getNativeView())); } + /** + * Add the specified number of months to the timestamp. + * @param months must be a INT16 scalar indicating the number of months to add. A negative number + * of months works too. + * @return the updated timestamp + */ + public final ColumnVector addCalendricalMonths(Scalar months) { + return new ColumnVector(addScalarCalendricalMonths(getNativeView(), months.getScalarHandle())); + } + /** * Check to see if the year for this timestamp is a leap year or not. * @return BOOL8 vector of results @@ -1053,6 +1066,45 @@ public final ColumnVector isLeapYear() { return new ColumnVector(isLeapYear(getNativeView())); } + /** + * Extract the number of days in the month + * @return INT16 column of the number of days in the corresponding month + */ + public final ColumnVector daysInMonth() { + assert type.isTimestampType(); + return new ColumnVector(daysInMonth(getNativeView())); + } + + /** + * Round the timestamp up to the given frequency keeping the type the same. + * @param freq what part of the timestamp to round. + * @return a timestamp with the same type, but rounded up. + */ + public final ColumnVector dateTimeCeil(DateTimeRoundingFrequency freq) { + assert type.isTimestampType(); + return new ColumnVector(dateTimeCeil(getNativeView(), freq.getNativeId())); + } + + /** + * Round the timestamp down to the given frequency keeping the type the same. + * @param freq what part of the timestamp to round. + * @return a timestamp with the same type, but rounded down. + */ + public final ColumnVector dateTimeFloor(DateTimeRoundingFrequency freq) { + assert type.isTimestampType(); + return new ColumnVector(dateTimeFloor(getNativeView(), freq.getNativeId())); + } + + /** + * Round the timestamp (half up) to the given frequency keeping the type the same. + * @param freq what part of the timestamp to round. + * @return a timestamp with the same type, but rounded (half up). + */ + public final ColumnVector dateTimeRound(DateTimeRoundingFrequency freq) { + assert type.isTimestampType(); + return new ColumnVector(dateTimeRound(getNativeView(), freq.getNativeId())); + } + /** * Rounds all the values in a column to the specified number of decimal places. * @@ -4721,19 +4773,7 @@ private static native long segmentedGather(long sourceColumnHandle, long gatherM private static native long unaryOperation(long viewHandle, int op); - private static native long year(long viewHandle) throws CudfException; - - private static native long month(long viewHandle) throws CudfException; - - private static native long day(long viewHandle) throws CudfException; - - private static native long hour(long viewHandle) throws CudfException; - - private static native long minute(long viewHandle) throws CudfException; - - private static native long second(long viewHandle) throws CudfException; - - private static native long weekDay(long viewHandle) throws CudfException; + private static native long extractDateTimeComponent(long viewHandle, int component); private static native long lastDayOfMonth(long viewHandle) throws CudfException; @@ -4743,8 +4783,18 @@ private static native long segmentedGather(long sourceColumnHandle, long gatherM private static native long addCalendricalMonths(long tsViewHandle, long monthsViewHandle); + private static native long addScalarCalendricalMonths(long tsViewHandle, long scalarHandle); + private static native long isLeapYear(long viewHandle) throws CudfException; + private static native long daysInMonth(long viewHandle) throws CudfException; + + private static native long dateTimeCeil(long viewHandle, int freq); + + private static native long dateTimeFloor(long viewHandle, int freq); + + private static native long dateTimeRound(long viewHandle, int freq); + private static native boolean containsScalar(long columnViewHaystack, long scalarHandle) throws CudfException; private static native long containsVector(long valuesHandle, long searchSpaceHandle) throws CudfException; diff --git a/java/src/main/java/ai/rapids/cudf/DateTimeComponent.java b/java/src/main/java/ai/rapids/cudf/DateTimeComponent.java new file mode 100644 index 00000000000..0f1618e29fb --- /dev/null +++ b/java/src/main/java/ai/rapids/cudf/DateTimeComponent.java @@ -0,0 +1,74 @@ +/* + * + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * + */ + +package ai.rapids.cudf; + +/** + * Types of datetime components that may be extracted. + */ +public enum DateTimeComponent { + /** + * year as an INT16 + */ + YEAR(0), + /** + * month 1 - jan, as an INT16 + */ + MONTH(1), + /** + * Day of the month as an INT16 + */ + DAY(2), + /** + * day of the week, Monday=1, ..., Sunday=7 as an INT16 + */ + WEEKDAY(3), + /** + * hour of the day 24-hour clock as an INT16 + */ + HOUR(4), + /** + * minutes past the hour as an INT16 + */ + MINUTE(5), + /** + * seconds past the minute as an INT16 + */ + SECOND(6), + /** + * milliseconds past the seconds as an INT16 + */ + MILLISECOND(7), + /** + * microseconds past the millisecond as an INT16 + */ + MICROSECOND(8), + /** + * nanoseconds past the microsecond as an INT16 + */ + NANOSECOND(9); + + final int id; + DateTimeComponent(int id) { + this.id = id; + } + + public int getNativeId() { + return id; + } +} diff --git a/java/src/main/java/ai/rapids/cudf/DateTimeRoundingFrequency.java b/java/src/main/java/ai/rapids/cudf/DateTimeRoundingFrequency.java new file mode 100644 index 00000000000..44a7a2f279d --- /dev/null +++ b/java/src/main/java/ai/rapids/cudf/DateTimeRoundingFrequency.java @@ -0,0 +1,38 @@ +/* + * + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * + */ + +package ai.rapids.cudf; + +public enum DateTimeRoundingFrequency { + DAY(0), + HOUR(1), + MINUTE(2), + SECOND(3), + MILLISECOND(4), + MICROSECOND(5), + NANOSECOND(6); + + final int id; + DateTimeRoundingFrequency(int id) { + this.id = id; + } + + public int getNativeId() { + return id; + } +} diff --git a/java/src/main/native/src/ColumnViewJni.cpp b/java/src/main/native/src/ColumnViewJni.cpp index 90902a24bbe..6a59ae3ddd5 100644 --- a/java/src/main/native/src/ColumnViewJni.cpp +++ b/java/src/main/native/src/ColumnViewJni.cpp @@ -128,6 +128,20 @@ std::size_t calc_device_memory_size(cudf::column_view const& view, bool const pa }); } +cudf::datetime::rounding_frequency as_rounding_freq(jint freq) +{ + switch (freq) { + case 0: return cudf::datetime::rounding_frequency::DAY; + case 1: return cudf::datetime::rounding_frequency::HOUR; + case 2: return cudf::datetime::rounding_frequency::MINUTE; + case 3: return cudf::datetime::rounding_frequency::SECOND; + case 4: return cudf::datetime::rounding_frequency::MILLISECOND; + case 5: return cudf::datetime::rounding_frequency::MICROSECOND; + case 6: return cudf::datetime::rounding_frequency::NANOSECOND; + default: throw std::invalid_argument("Invalid rounding_frequency"); + } +} + } // anonymous namespace extern "C" { @@ -1100,147 +1114,172 @@ JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_round( CATCH_STD(env, 0); } -JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_year(JNIEnv* env, jclass, jlong input_ptr) +JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_extractDateTimeComponent(JNIEnv* env, + jclass, + jlong input_ptr, + jint component) { JNI_NULL_CHECK(env, input_ptr, "input is null", 0); try { cudf::jni::auto_set_device(env); cudf::column_view const* input = reinterpret_cast(input_ptr); - return release_as_jlong(cudf::datetime::extract_year(*input)); + cudf::datetime::datetime_component comp; + switch (component) { + case 0: comp = cudf::datetime::datetime_component::YEAR; break; + case 1: comp = cudf::datetime::datetime_component::MONTH; break; + case 2: comp = cudf::datetime::datetime_component::DAY; break; + case 3: comp = cudf::datetime::datetime_component::WEEKDAY; break; + case 4: comp = cudf::datetime::datetime_component::HOUR; break; + case 5: comp = cudf::datetime::datetime_component::MINUTE; break; + case 6: comp = cudf::datetime::datetime_component::SECOND; break; + case 7: comp = cudf::datetime::datetime_component::MILLISECOND; break; + case 8: comp = cudf::datetime::datetime_component::MICROSECOND; break; + case 9: comp = cudf::datetime::datetime_component::NANOSECOND; break; + default: JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "Invalid component", 0); + } + return release_as_jlong(cudf::datetime::extract_datetime_component(*input, comp)); } CATCH_STD(env, 0); } -JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_month(JNIEnv* env, jclass, jlong input_ptr) +JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_lastDayOfMonth(JNIEnv* env, + jclass, + jlong input_ptr) { JNI_NULL_CHECK(env, input_ptr, "input is null", 0); try { cudf::jni::auto_set_device(env); cudf::column_view const* input = reinterpret_cast(input_ptr); - return release_as_jlong(cudf::datetime::extract_month(*input)); + return release_as_jlong(cudf::datetime::last_day_of_month(*input)); } CATCH_STD(env, 0); } -JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_day(JNIEnv* env, jclass, jlong input_ptr) +JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_dayOfYear(JNIEnv* env, + jclass, + jlong input_ptr) { JNI_NULL_CHECK(env, input_ptr, "input is null", 0); try { cudf::jni::auto_set_device(env); cudf::column_view const* input = reinterpret_cast(input_ptr); - return release_as_jlong(cudf::datetime::extract_day(*input)); + return release_as_jlong(cudf::datetime::day_of_year(*input)); } CATCH_STD(env, 0); } -JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_hour(JNIEnv* env, jclass, jlong input_ptr) +JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_quarterOfYear(JNIEnv* env, + jclass, + jlong input_ptr) { JNI_NULL_CHECK(env, input_ptr, "input is null", 0); try { cudf::jni::auto_set_device(env); cudf::column_view const* input = reinterpret_cast(input_ptr); - return release_as_jlong(cudf::datetime::extract_hour(*input)); + return release_as_jlong(cudf::datetime::extract_quarter(*input)); } CATCH_STD(env, 0); } -JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_minute(JNIEnv* env, jclass, jlong input_ptr) +JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_addCalendricalMonths(JNIEnv* env, + jclass, + jlong ts_ptr, + jlong months_ptr) { - JNI_NULL_CHECK(env, input_ptr, "input is null", 0); + JNI_NULL_CHECK(env, ts_ptr, "ts is null", 0); + JNI_NULL_CHECK(env, months_ptr, "months is null", 0); try { cudf::jni::auto_set_device(env); - cudf::column_view const* input = reinterpret_cast(input_ptr); - return release_as_jlong(cudf::datetime::extract_minute(*input)); + cudf::column_view const* ts = reinterpret_cast(ts_ptr); + cudf::column_view const* months = reinterpret_cast(months_ptr); + return release_as_jlong(cudf::datetime::add_calendrical_months(*ts, *months)); } CATCH_STD(env, 0); } -JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_second(JNIEnv* env, jclass, jlong input_ptr) +JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_addScalarCalendricalMonths(JNIEnv* env, + jclass, + jlong ts_ptr, + jlong months_ptr) { - JNI_NULL_CHECK(env, input_ptr, "input is null", 0); + JNI_NULL_CHECK(env, ts_ptr, "ts is null", 0); + JNI_NULL_CHECK(env, months_ptr, "months is null", 0); try { cudf::jni::auto_set_device(env); - cudf::column_view const* input = reinterpret_cast(input_ptr); - return release_as_jlong(cudf::datetime::extract_second(*input)); + cudf::column_view const* ts = reinterpret_cast(ts_ptr); + cudf::scalar const* months = reinterpret_cast(months_ptr); + return release_as_jlong(cudf::datetime::add_calendrical_months(*ts, *months)); } CATCH_STD(env, 0); } -JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_weekDay(JNIEnv* env, jclass, jlong input_ptr) +JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_isLeapYear(JNIEnv* env, + jclass, + jlong input_ptr) { JNI_NULL_CHECK(env, input_ptr, "input is null", 0); try { cudf::jni::auto_set_device(env); cudf::column_view const* input = reinterpret_cast(input_ptr); - return release_as_jlong(cudf::datetime::extract_weekday(*input)); + return release_as_jlong(cudf::datetime::is_leap_year(*input)); } CATCH_STD(env, 0); } -JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_lastDayOfMonth(JNIEnv* env, - jclass, - jlong input_ptr) +JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_daysInMonth(JNIEnv* env, + jclass, + jlong input_ptr) { JNI_NULL_CHECK(env, input_ptr, "input is null", 0); try { cudf::jni::auto_set_device(env); cudf::column_view const* input = reinterpret_cast(input_ptr); - return release_as_jlong(cudf::datetime::last_day_of_month(*input)); + return release_as_jlong(cudf::datetime::days_in_month(*input)); } CATCH_STD(env, 0); } -JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_dayOfYear(JNIEnv* env, - jclass, - jlong input_ptr) +JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_dateTimeCeil(JNIEnv* env, + jclass, + jlong input_ptr, + jint freq) { JNI_NULL_CHECK(env, input_ptr, "input is null", 0); try { cudf::jni::auto_set_device(env); - cudf::column_view const* input = reinterpret_cast(input_ptr); - return release_as_jlong(cudf::datetime::day_of_year(*input)); + cudf::column_view const* input = reinterpret_cast(input_ptr); + cudf::datetime::rounding_frequency n_freq = as_rounding_freq(freq); + return release_as_jlong(cudf::datetime::ceil_datetimes(*input, n_freq)); } CATCH_STD(env, 0); } -JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_quarterOfYear(JNIEnv* env, +JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_dateTimeFloor(JNIEnv* env, jclass, - jlong input_ptr) + jlong input_ptr, + jint freq) { JNI_NULL_CHECK(env, input_ptr, "input is null", 0); try { cudf::jni::auto_set_device(env); - cudf::column_view const* input = reinterpret_cast(input_ptr); - return release_as_jlong(cudf::datetime::extract_quarter(*input)); + cudf::column_view const* input = reinterpret_cast(input_ptr); + cudf::datetime::rounding_frequency n_freq = as_rounding_freq(freq); + return release_as_jlong(cudf::datetime::floor_datetimes(*input, n_freq)); } CATCH_STD(env, 0); } -JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_addCalendricalMonths(JNIEnv* env, - jclass, - jlong ts_ptr, - jlong months_ptr) -{ - JNI_NULL_CHECK(env, ts_ptr, "ts is null", 0); - JNI_NULL_CHECK(env, months_ptr, "months is null", 0); - try { - cudf::jni::auto_set_device(env); - cudf::column_view const* ts = reinterpret_cast(ts_ptr); - cudf::column_view const* months = reinterpret_cast(months_ptr); - return release_as_jlong(cudf::datetime::add_calendrical_months(*ts, *months)); - } - CATCH_STD(env, 0); -} - -JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_isLeapYear(JNIEnv* env, - jclass, - jlong input_ptr) +JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_dateTimeRound(JNIEnv* env, + jclass, + jlong input_ptr, + jint freq) { JNI_NULL_CHECK(env, input_ptr, "input is null", 0); try { cudf::jni::auto_set_device(env); - cudf::column_view const* input = reinterpret_cast(input_ptr); - return release_as_jlong(cudf::datetime::is_leap_year(*input)); + cudf::column_view const* input = reinterpret_cast(input_ptr); + cudf::datetime::rounding_frequency n_freq = as_rounding_freq(freq); + return release_as_jlong(cudf::datetime::round_datetimes(*input, n_freq)); } CATCH_STD(env, 0); } diff --git a/java/src/test/java/ai/rapids/cudf/TimestampColumnVectorTest.java b/java/src/test/java/ai/rapids/cudf/TimestampColumnVectorTest.java index c22acac747e..bac83310c99 100644 --- a/java/src/test/java/ai/rapids/cudf/TimestampColumnVectorTest.java +++ b/java/src/test/java/ai/rapids/cudf/TimestampColumnVectorTest.java @@ -1,6 +1,6 @@ /* * - * Copyright (c) 2019-2020, NVIDIA CORPORATION. + * Copyright (c) 2019-2024, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -237,6 +237,69 @@ public void getSecond() { } } + @Test + public void getExtractMillis() { + try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS)) { + assert timestampColumnVector.getType().equals(DType.TIMESTAMP_MILLISECONDS); + try (ColumnVector tmp = timestampColumnVector.extractDateTimeComponent(DateTimeComponent.MILLISECOND); + HostColumnVector result = tmp.copyToHost()) { + assertEquals(238, result.getShort(0)); + assertEquals(115, result.getShort(1)); + assertEquals(929, result.getShort(2)); + } + } + + try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(TIMES_S); + ColumnVector tmp = timestampColumnVector.extractDateTimeComponent(DateTimeComponent.MILLISECOND); + HostColumnVector result = tmp.copyToHost()) { + assertEquals(0, result.getShort(0)); + assertEquals(0, result.getShort(1)); + assertEquals(0, result.getShort(2)); + } + } + + @Test + public void getExtractMicro() { + try (ColumnVector timestampColumnVector = ColumnVector.timestampMicroSecondsFromLongs(TIMES_US)) { + assert timestampColumnVector.getType().equals(DType.TIMESTAMP_MICROSECONDS); + try (ColumnVector tmp = timestampColumnVector.extractDateTimeComponent(DateTimeComponent.MICROSECOND); + HostColumnVector result = tmp.copyToHost()) { + assertEquals(297, result.getShort(0)); + assertEquals(254, result.getShort(1)); + assertEquals(861, result.getShort(2)); + } + } + + try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(TIMES_S); + ColumnVector tmp = timestampColumnVector.extractDateTimeComponent(DateTimeComponent.MICROSECOND); + HostColumnVector result = tmp.copyToHost()) { + assertEquals(0, result.getShort(0)); + assertEquals(0, result.getShort(1)); + assertEquals(0, result.getShort(2)); + } + } + + @Test + public void getExtractNano() { + try (ColumnVector timestampColumnVector = ColumnVector.timestampNanoSecondsFromLongs(TIMES_NS)) { + assert timestampColumnVector.getType().equals(DType.TIMESTAMP_NANOSECONDS); + try (ColumnVector tmp = timestampColumnVector.extractDateTimeComponent(DateTimeComponent.NANOSECOND); + HostColumnVector result = tmp.copyToHost()) { + assertEquals(531, result.getShort(0)); + assertEquals(330, result.getShort(1)); + assertEquals(604, result.getShort(2)); + } + } + + try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(TIMES_S); + ColumnVector tmp = timestampColumnVector.extractDateTimeComponent(DateTimeComponent.NANOSECOND); + HostColumnVector result = tmp.copyToHost()) { + assertEquals(0, result.getShort(0)); + assertEquals(0, result.getShort(1)); + assertEquals(0, result.getShort(2)); + } + } + @Test public void testWeekDay() { try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS); @@ -350,6 +413,59 @@ public void testAddMonths() { } } + @Test + public void testAddMonthsScalar() { + long[] EXPECTED = new long[]{ + -129290327762L, //'1965-11-26 14:01:12.238' Friday + 1533384000115L, //'2018-08-04 12:00:00.115' Saturday + 1677310332929L, //'2023-03-25 07:32:12.929' Saturday + -129290327762L, //'1965-12-26 14:01:12.238' Sunday + 1533384000115L}; //'2018-09-04 12:00:00.115' Tuesday + try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS); + Scalar months = Scalar.fromShort((short)1); + ColumnVector result = timestampColumnVector.addCalendricalMonths(months); + ColumnVector expected = ColumnVector.timestampMilliSecondsFromLongs(EXPECTED)) { + assertColumnsAreEqual(expected, result); + } + } + + @Test + public void testDaysInMonth() { + Integer[] DAYS = new Integer[] { + 0, // Jan 1, 1970 + 31, // Feb 1, 1970 + 59, // Mar 1, 1970 + 90, // Apr 1, 1970 + 120, // May 1, 1970 + 151, // June 1, 1970 + 181, // July 1, 1970 + 212, // Aug 1, 1970 + 243, // Sep 1, 1970 + 273, // OCt 1, 1970 + 304, // Nov 1, 1970 + 334 // Dec 1 1970 + }; + short[] EXPECTED = new short[]{ + 31, // Jan 1970 + 28, // Feb 1970 + 31, // Mar 1970 + 30, // Apr 1970 + 31, // May 1970 + 30, // June 1970 + 31, // July 1970 + 31, // Aug 1970 + 30, // Sep 1970 + 31, // Oct 1970 + 30, // Nov 1970 + 31 // Dec 1970 + }; + try (ColumnVector timestampColumnVector = ColumnVector.timestampDaysFromBoxedInts(DAYS); + ColumnVector result = timestampColumnVector.daysInMonth(); + ColumnVector expected = ColumnVector.fromShorts(EXPECTED)) { + assertColumnsAreEqual(expected, result); + } + } + @Test public void testIsLeapYear() { Boolean[] EXPECTED = new Boolean[]{false, false, false, false, false}; @@ -383,6 +499,86 @@ public void testIsLeapYear() { } } + @Test + public void testCeilDays() { + long[] EXPECTED_NS = new long[]{ + -131932800000000000L, //'1965-10-27 00:00:00.000000000' + 1530748800000000000L, //'2018-07-05 00:00:00.000000000' + 1674691200000000000L, //'2023-01-26 00:00:00.000000000' + -131932800000000000L, //'1965-10-27 00:00:00.000000000' + 1530748800000000000L}; //'2018-07-05 00:00:00.000000000' + try (ColumnVector timestampColumnVector = ColumnVector.timestampNanoSecondsFromLongs(TIMES_NS); + ColumnVector result = timestampColumnVector.dateTimeCeil(DateTimeRoundingFrequency.DAY); + ColumnVector expected = ColumnVector.timestampNanoSecondsFromLongs(EXPECTED_NS)) { + assertColumnsAreEqual(expected, result); + } + long[] EXPECTED_US = new long[]{ + -131932800000000L, //'1965-10-27 00:00:00.000000' + 1530748800000000L, //'2018-07-05 00:00:00.000000' + 1674691200000000L, //'2023-01-26 00:00:00.000000' + -131932800000000L, //'1965-10-27 00:00:00.000000' + 1530748800000000L}; //'2018-07-05 00:00:00.000000' + try (ColumnVector timestampColumnVector = ColumnVector.timestampMicroSecondsFromLongs(TIMES_US); + ColumnVector result = timestampColumnVector.dateTimeCeil(DateTimeRoundingFrequency.DAY); + ColumnVector expected = ColumnVector.timestampMicroSecondsFromLongs(EXPECTED_US)) { + assertColumnsAreEqual(expected, result); + } + } + + @Test + public void testFloorDays() { + long[] EXPECTED_NS = new long[]{ + -132019200000000000L, //'1965-10-26 00:00:00.000000000' + 1530662400000000000L, //'2018-07-04 00:00:00.000000000' + 1674604800000000000L, //'2023-01-25 00:00:00.000000000' + -132019200000000000L, //'1965-10-26 00:00:00.000000000' + 1530662400000000000L}; //'2018-07-04 00:00:00.000000000' + try (ColumnVector timestampColumnVector = ColumnVector.timestampNanoSecondsFromLongs(TIMES_NS); + ColumnVector result = timestampColumnVector.dateTimeFloor(DateTimeRoundingFrequency.DAY); + ColumnVector expected = ColumnVector.timestampNanoSecondsFromLongs(EXPECTED_NS)) { + assertColumnsAreEqual(expected, result); + } + + long[] EXPECTED_US = new long[]{ + -132019200000000L, //'1965-10-26 00:00:00.000000' + 1530662400000000L, //'2018-07-04 00:00:00.000000' + 1674604800000000L, //'2023-01-25 00:00:00.000000' + -132019200000000L, //'1965-10-26 00:00:00.000000' + 1530662400000000L}; //'2018-07-04 00:00:00.000000' + try (ColumnVector timestampColumnVector = ColumnVector.timestampMicroSecondsFromLongs(TIMES_US); + ColumnVector result = timestampColumnVector.dateTimeFloor(DateTimeRoundingFrequency.DAY); + ColumnVector expected = ColumnVector.timestampMicroSecondsFromLongs(EXPECTED_US)) { + assertColumnsAreEqual(expected, result); + } + } + + @Test + public void testRoundDays() { + long[] EXPECTED_NS = new long[]{ + -131932800000000000L, //'1965-10-27 00:00:00.000000000' + 1530748800000000000L, //'2018-07-05 00:00:00.000000000' + 1674604800000000000L, //'2023-01-25 00:00:00.000000000' + -131932800000000000L, //'1965-10-27 00:00:00.000000000' + 1530748800000000000L}; //'2018-07-05 00:00:00.000000000' + try (ColumnVector timestampColumnVector = ColumnVector.timestampNanoSecondsFromLongs(TIMES_NS); + ColumnVector result = timestampColumnVector.dateTimeRound(DateTimeRoundingFrequency.DAY); + ColumnVector expected = ColumnVector.timestampNanoSecondsFromLongs(EXPECTED_NS)) { + assertColumnsAreEqual(expected, result); + } + + long[] EXPECTED_US = new long[]{ + -131932800000000L, //'1965-10-27 00:00:00.000000' + 1530748800000000L, //'2018-07-05 00:00:00.000000' + 1674604800000000L, //'2023-01-25 00:00:00.000000' + -131932800000000L, //'1965-10-27 00:00:00.000000' + 1530748800000000L}; //'2018-07-05 00:00:00.000000' + try (ColumnVector timestampColumnVector = ColumnVector.timestampMicroSecondsFromLongs(TIMES_US); + ColumnVector result = timestampColumnVector.dateTimeRound(DateTimeRoundingFrequency.DAY); + ColumnVector expected = ColumnVector.timestampMicroSecondsFromLongs(EXPECTED_US)) { + assertColumnsAreEqual(expected, result); + } + } + @Test public void testCastToTimestamp() { try (ColumnVector timestampMillis = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS); From d67d017e8c3176ae214165869d5bb4b02c4207a0 Mon Sep 17 00:00:00 2001 From: Matthew Roeschke <10647082+mroeschke@users.noreply.github.com> Date: Thu, 14 Nov 2024 17:25:26 -0800 Subject: [PATCH 8/9] Remove cudf._lib.avro in favor of inlining pylicudf (#17319) Contributes to https://github.com/rapidsai/cudf/issues/17317 Authors: - Matthew Roeschke (https://github.com/mroeschke) Approvers: - Vyas Ramasubramani (https://github.com/vyasr) URL: https://github.com/rapidsai/cudf/pull/17319 --- python/cudf/cudf/_lib/CMakeLists.txt | 1 - python/cudf/cudf/_lib/__init__.py | 1 - python/cudf/cudf/_lib/avro.pyx | 33 ---------------------------- python/cudf/cudf/_lib/utils.pxd | 2 +- python/cudf/cudf/_lib/utils.pyx | 2 +- python/cudf/cudf/io/avro.py | 23 ++++++++++++++----- 6 files changed, 20 insertions(+), 42 deletions(-) delete mode 100644 python/cudf/cudf/_lib/avro.pyx diff --git a/python/cudf/cudf/_lib/CMakeLists.txt b/python/cudf/cudf/_lib/CMakeLists.txt index 5d4b5421f16..41a7db2285a 100644 --- a/python/cudf/cudf/_lib/CMakeLists.txt +++ b/python/cudf/cudf/_lib/CMakeLists.txt @@ -14,7 +14,6 @@ set(cython_sources aggregation.pyx - avro.pyx binaryop.pyx column.pyx concat.pyx diff --git a/python/cudf/cudf/_lib/__init__.py b/python/cudf/cudf/_lib/__init__.py index 918edb6d3f1..57df6899a22 100644 --- a/python/cudf/cudf/_lib/__init__.py +++ b/python/cudf/cudf/_lib/__init__.py @@ -2,7 +2,6 @@ import numpy as np from . import ( - avro, binaryop, concat, copying, diff --git a/python/cudf/cudf/_lib/avro.pyx b/python/cudf/cudf/_lib/avro.pyx deleted file mode 100644 index b1759635a36..00000000000 --- a/python/cudf/cudf/_lib/avro.pyx +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright (c) 2020-2024, NVIDIA CORPORATION. - -from cudf._lib.utils cimport data_from_pylibcudf_io - -import pylibcudf as plc -from pylibcudf.io.types import SourceInfo - - -cpdef read_avro(datasource, columns=None, skip_rows=0, num_rows=-1): - """ - Cython function to call libcudf read_avro, see `read_avro`. - - See Also - -------- - cudf.io.avro.read_avro - """ - - num_rows = -1 if num_rows is None else num_rows - skip_rows = 0 if skip_rows is None else skip_rows - - if not isinstance(num_rows, int) or num_rows < -1: - raise TypeError("num_rows must be an int >= -1") - if not isinstance(skip_rows, int) or skip_rows < 0: - raise TypeError("skip_rows must be an int >= 0") - - return data_from_pylibcudf_io( - plc.io.avro.read_avro( - SourceInfo([datasource]), - columns, - skip_rows, - num_rows - ) - ) diff --git a/python/cudf/cudf/_lib/utils.pxd b/python/cudf/cudf/_lib/utils.pxd index 7254db5c43d..623c5064a1a 100644 --- a/python/cudf/cudf/_lib/utils.pxd +++ b/python/cudf/cudf/_lib/utils.pxd @@ -11,7 +11,7 @@ from pylibcudf.libcudf.table.table cimport table, table_view cdef data_from_unique_ptr( unique_ptr[table] c_tbl, column_names, index_names=*) cdef data_from_pylibcudf_table(tbl, column_names, index_names=*) -cdef data_from_pylibcudf_io(tbl_with_meta, column_names = *, index_names = *) +cpdef data_from_pylibcudf_io(tbl_with_meta, column_names = *, index_names = *) cdef data_from_table_view( table_view tv, object owner, object column_names, object index_names=*) cdef table_view table_view_from_columns(columns) except * diff --git a/python/cudf/cudf/_lib/utils.pyx b/python/cudf/cudf/_lib/utils.pyx index 9e5b99f64eb..292de82e4c4 100644 --- a/python/cudf/cudf/_lib/utils.pyx +++ b/python/cudf/cudf/_lib/utils.pyx @@ -316,7 +316,7 @@ cdef data_from_pylibcudf_table(tbl, column_names, index_names=None): index_names ) -cdef data_from_pylibcudf_io(tbl_with_meta, column_names=None, index_names=None): +cpdef data_from_pylibcudf_io(tbl_with_meta, column_names=None, index_names=None): """ Unpacks the TableWithMetadata from libcudf I/O into a dict of columns and an Index (cuDF format) diff --git a/python/cudf/cudf/io/avro.py b/python/cudf/cudf/io/avro.py index 964bd02b03e..11730e98c95 100644 --- a/python/cudf/cudf/io/avro.py +++ b/python/cudf/cudf/io/avro.py @@ -1,7 +1,9 @@ # Copyright (c) 2019-2024, NVIDIA CORPORATION. +import pylibcudf as plc + import cudf -from cudf import _lib as libcudf +from cudf._lib.utils import data_from_pylibcudf_io from cudf.utils import ioutils @@ -23,8 +25,19 @@ def read_avro( filepath_or_buffer, "read_avro" ) - return cudf.DataFrame._from_data( - *libcudf.avro.read_avro( - filepath_or_buffer, columns, skiprows, num_rows - ) + num_rows = -1 if num_rows is None else num_rows + skip_rows = 0 if skiprows is None else skiprows + + if not isinstance(num_rows, int) or num_rows < -1: + raise TypeError("num_rows must be an int >= -1") + if not isinstance(skip_rows, int) or skip_rows < 0: + raise TypeError("skip_rows must be an int >= 0") + + plc_result = plc.io.avro.read_avro( + plc.io.types.SourceInfo([filepath_or_buffer]), + columns, + skip_rows, + num_rows, ) + + return cudf.DataFrame._from_data(*data_from_pylibcudf_io(plc_result)) From d475dca1a5667b00ec6398aec35d99a86aabb52f Mon Sep 17 00:00:00 2001 From: GALI PREM SAGAR Date: Thu, 14 Nov 2024 22:40:33 -0600 Subject: [PATCH 9/9] Fix various issues with `replace` API and add support in `datetime` and `timedelta` columns (#17331) This PR: - [x] Adds support for `find_and_replace` in `DateTimeColumn` and `TimeDeltaColumn`, such that when `.replace` is called on a series or dataframe with these columns, we don't error and replace the values correctly. - [x] Fixed various type combination edge cases that were previously incorrectly handled and updated stale tests associated with them. - [x] Added a small parquet file in pytests that has multiple rows that uncovered these bugs. Authors: - GALI PREM SAGAR (https://github.com/galipremsagar) Approvers: - Vyas Ramasubramani (https://github.com/vyasr) URL: https://github.com/rapidsai/cudf/pull/17331 --- python/cudf/cudf/core/_compat.py | 1 + python/cudf/cudf/core/column/datetime.py | 21 +++- python/cudf/cudf/core/column/numerical.py | 47 +++++-- python/cudf/cudf/core/column/timedelta.py | 53 +++++++- .../parquet/replace_multiple_rows.parquet | Bin 0 -> 941697 bytes python/cudf/cudf/tests/test_replace.py | 119 +++++++++++++----- python/cudf/cudf/utils/dtypes.py | 18 ++- python/cudf/cudf/utils/utils.py | 38 ++++++ 8 files changed, 242 insertions(+), 55 deletions(-) create mode 100644 python/cudf/cudf/tests/data/parquet/replace_multiple_rows.parquet diff --git a/python/cudf/cudf/core/_compat.py b/python/cudf/cudf/core/_compat.py index 871ffc6269d..7dba0dc8a70 100644 --- a/python/cudf/cudf/core/_compat.py +++ b/python/cudf/cudf/core/_compat.py @@ -8,5 +8,6 @@ PANDAS_GE_210 = PANDAS_VERSION >= version.parse("2.1.0") +PANDAS_GT_214 = PANDAS_VERSION > version.parse("2.1.4") PANDAS_GE_220 = PANDAS_VERSION >= version.parse("2.2.0") PANDAS_LT_300 = PANDAS_VERSION < version.parse("3.0.0") diff --git a/python/cudf/cudf/core/column/datetime.py b/python/cudf/cudf/core/column/datetime.py index b6dc250e64d..bd0d72b9bc0 100644 --- a/python/cudf/cudf/core/column/datetime.py +++ b/python/cudf/cudf/core/column/datetime.py @@ -28,7 +28,10 @@ from cudf.core.column import ColumnBase, as_column, column, string from cudf.core.column.timedelta import _unit_to_nanoseconds_conversion from cudf.utils.dtypes import _get_base_dtype -from cudf.utils.utils import _all_bools_with_nulls +from cudf.utils.utils import ( + _all_bools_with_nulls, + _datetime_timedelta_find_and_replace, +) if TYPE_CHECKING: from collections.abc import Sequence @@ -630,6 +633,22 @@ def quantile( ) return result.astype(self.dtype) + def find_and_replace( + self, + to_replace: ColumnBase, + replacement: ColumnBase, + all_nan: bool = False, + ) -> DatetimeColumn: + return cast( + DatetimeColumn, + _datetime_timedelta_find_and_replace( + original_column=self, + to_replace=to_replace, + replacement=replacement, + all_nan=all_nan, + ), + ) + def _binaryop(self, other: ColumnBinaryOperand, op: str) -> ColumnBase: reflect, op = self._check_reflected_op(op) other = self._wrap_binop_normalization(other) diff --git a/python/cudf/cudf/core/column/numerical.py b/python/cudf/cudf/core/column/numerical.py index 620cae65374..f79496ed0ec 100644 --- a/python/cudf/cudf/core/column/numerical.py +++ b/python/cudf/cudf/core/column/numerical.py @@ -511,24 +511,41 @@ def find_and_replace( ): return self.copy() - to_replace_col = _normalize_find_and_replace_input( - self.dtype, to_replace - ) + try: + to_replace_col = _normalize_find_and_replace_input( + self.dtype, to_replace + ) + except TypeError: + # if `to_replace` cannot be normalized to the current dtype, + # that means no value of `to_replace` is present in self, + # Hence there is no point of proceeding further. + return self.copy() + if all_nan: replacement_col = column.as_column(replacement, dtype=self.dtype) else: - replacement_col = _normalize_find_and_replace_input( - self.dtype, replacement - ) + try: + replacement_col = _normalize_find_and_replace_input( + self.dtype, replacement + ) + except TypeError: + # Some floating values can never be converted into signed or unsigned integers + # for those cases, we just need a column of `replacement` constructed + # with its own type for the final type determination below at `find_common_type` + # call. + replacement_col = column.as_column( + replacement, + dtype=self.dtype if len(replacement) <= 0 else None, + ) + common_type = find_common_type( + (to_replace_col.dtype, replacement_col.dtype, self.dtype) + ) if len(replacement_col) == 1 and len(to_replace_col) > 1: replacement_col = column.as_column( - replacement[0], length=len(to_replace_col), dtype=self.dtype + replacement[0], length=len(to_replace_col), dtype=common_type ) elif len(replacement_col) == 1 and len(to_replace_col) == 0: return self.copy() - common_type = find_common_type( - (to_replace_col.dtype, replacement_col.dtype, self.dtype) - ) replaced = self.astype(common_type) df = cudf.DataFrame._from_data( { @@ -718,6 +735,8 @@ def _normalize_find_and_replace_input( if isinstance(col_to_normalize, list): if normalized_column.null_count == len(normalized_column): normalized_column = normalized_column.astype(input_column_dtype) + if normalized_column.can_cast_safely(input_column_dtype): + return normalized_column.astype(input_column_dtype) col_to_normalize_dtype = min_column_type( normalized_column, input_column_dtype ) @@ -728,7 +747,7 @@ def _normalize_find_and_replace_input( if np.isinf(col_to_normalize[0]): return normalized_column col_to_normalize_casted = np.array(col_to_normalize[0]).astype( - input_column_dtype + col_to_normalize_dtype ) if not np.isnan(col_to_normalize_casted) and ( @@ -739,8 +758,8 @@ def _normalize_find_and_replace_input( f"{col_to_normalize[0]} " f"to {input_column_dtype.name}" ) - else: - col_to_normalize_dtype = input_column_dtype + if normalized_column.can_cast_safely(col_to_normalize_dtype): + return normalized_column.astype(col_to_normalize_dtype) elif hasattr(col_to_normalize, "dtype"): col_to_normalize_dtype = col_to_normalize.dtype else: @@ -755,6 +774,8 @@ def _normalize_find_and_replace_input( f"{col_to_normalize_dtype.name} " f"to {input_column_dtype.name}" ) + if not normalized_column.can_cast_safely(input_column_dtype): + return normalized_column return normalized_column.astype(input_column_dtype) diff --git a/python/cudf/cudf/core/column/timedelta.py b/python/cudf/cudf/core/column/timedelta.py index 087d6474e7f..c3ad09cf898 100644 --- a/python/cudf/cudf/core/column/timedelta.py +++ b/python/cudf/cudf/core/column/timedelta.py @@ -16,7 +16,10 @@ from cudf.core.buffer import Buffer, acquire_spill_lock from cudf.core.column import ColumnBase, column, string from cudf.utils.dtypes import np_to_pa_dtype -from cudf.utils.utils import _all_bools_with_nulls +from cudf.utils.utils import ( + _all_bools_with_nulls, + _datetime_timedelta_find_and_replace, +) if TYPE_CHECKING: from collections.abc import Sequence @@ -95,7 +98,7 @@ def __init__( size = data.size // dtype.itemsize size = size - offset if len(children) != 0: - raise ValueError("TimedeltaColumn must have no children.") + raise ValueError("TimeDeltaColumn must have no children.") super().__init__( data=data, size=size, @@ -306,6 +309,52 @@ def as_timedelta_column(self, dtype: Dtype) -> TimeDeltaColumn: return self return libcudf.unary.cast(self, dtype=dtype) + def find_and_replace( + self, + to_replace: ColumnBase, + replacement: ColumnBase, + all_nan: bool = False, + ) -> TimeDeltaColumn: + return cast( + TimeDeltaColumn, + _datetime_timedelta_find_and_replace( + original_column=self, + to_replace=to_replace, + replacement=replacement, + all_nan=all_nan, + ), + ) + + def can_cast_safely(self, to_dtype: Dtype) -> bool: + if to_dtype.kind == "m": # type: ignore[union-attr] + to_res, _ = np.datetime_data(to_dtype) + self_res, _ = np.datetime_data(self.dtype) + + max_int = np.iinfo(np.int64).max + + max_dist = np.timedelta64( + self.max().astype(np.int64, copy=False), self_res + ) + min_dist = np.timedelta64( + self.min().astype(np.int64, copy=False), self_res + ) + + self_delta_dtype = np.timedelta64(0, self_res).dtype + + if max_dist <= np.timedelta64(max_int, to_res).astype( + self_delta_dtype + ) and min_dist <= np.timedelta64(max_int, to_res).astype( + self_delta_dtype + ): + return True + else: + return False + elif to_dtype == cudf.dtype("int64") or to_dtype == cudf.dtype("O"): + # can safely cast to representation, or string + return True + else: + return False + def mean(self, skipna=None) -> pd.Timedelta: return pd.Timedelta( cast( diff --git a/python/cudf/cudf/tests/data/parquet/replace_multiple_rows.parquet 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100644 --- a/python/cudf/cudf/tests/test_replace.py +++ b/python/cudf/cudf/tests/test_replace.py @@ -13,6 +13,7 @@ from cudf.core._compat import ( PANDAS_CURRENT_SUPPORTED_VERSION, PANDAS_GE_220, + PANDAS_GT_214, PANDAS_VERSION, ) from cudf.core.dtypes import Decimal32Dtype, Decimal64Dtype, Decimal128Dtype @@ -116,8 +117,10 @@ def test_series_replace(): sr6 = sr1.replace([0, 1], [5, 6]) assert_eq(a6, sr6.to_numpy()) - with pytest.raises(TypeError): - sr1.replace([0, 1], [5.5, 6.5]) + assert_eq( + sr1.replace([0, 1], [5.5, 6.5]), + sr1.to_pandas().replace([0, 1], [5.5, 6.5]), + ) # Series input a8 = np.array([5, 5, 5, 3, 4]) @@ -160,8 +163,10 @@ def test_series_replace_with_nulls(): assert_eq(a6, sr6.to_numpy()) sr1 = cudf.Series([0, 1, 2, 3, 4, None]) - with pytest.raises(TypeError): - sr1.replace([0, 1], [5.5, 6.5]).fillna(-10) + assert_eq( + sr1.replace([0, 1], [5.5, 6.5]).fillna(-10), + sr1.to_pandas().replace([0, 1], [5.5, 6.5]).fillna(-10), + ) # Series input a8 = np.array([-10, -10, -10, 3, 4, -10]) @@ -967,30 +972,37 @@ def test_series_multiple_times_with_nulls(): @pytest.mark.parametrize( "replacement", [128, 128.0, 128.5, 32769, 32769.0, 32769.5] ) -def test_numeric_series_replace_dtype(series_dtype, replacement): +def test_numeric_series_replace_dtype(request, series_dtype, replacement): + request.applymarker( + pytest.mark.xfail( + condition=PANDAS_GT_214 + and ( + ( + series_dtype == "int8" + and replacement in {128, 128.0, 32769, 32769.0} + ) + or ( + series_dtype == "int16" and replacement in {32769, 32769.0} + ) + ), + reason="Pandas throws an AssertionError for these " + "cases and asks us to log a bug, they are trying to " + "avoid a RecursionError which cudf will not run into", + ) + ) psr = pd.Series([0, 1, 2, 3, 4, 5], dtype=series_dtype) sr = cudf.from_pandas(psr) - numpy_replacement = np.array(replacement).astype(sr.dtype)[()] - can_replace = numpy_replacement == replacement + expect = psr.replace(1, replacement) + got = sr.replace(1, replacement) - # Both Scalar - if not can_replace: - with pytest.raises(TypeError): - sr.replace(1, replacement) - else: - expect = psr.replace(1, replacement).astype(psr.dtype) - got = sr.replace(1, replacement) - assert_eq(expect, got) + assert_eq(expect, got) # to_replace is a list, replacement is a scalar - if not can_replace: - with pytest.raises(TypeError): - sr.replace([2, 3], replacement) - else: - expect = psr.replace([2, 3], replacement).astype(psr.dtype) - got = sr.replace([2, 3], replacement) - assert_eq(expect, got) + expect = psr.replace([2, 3], replacement) + got = sr.replace([2, 3], replacement) + + assert_eq(expect, got) # If to_replace is a scalar and replacement is a list with pytest.raises(TypeError): @@ -1001,17 +1013,9 @@ def test_numeric_series_replace_dtype(series_dtype, replacement): sr.replace([0, 1], [replacement]) # Both lists of equal length - if ( - np.dtype(type(replacement)).kind == "f" and sr.dtype.kind in {"i", "u"} - ) or (not can_replace): - with pytest.raises(TypeError): - sr.replace([2, 3], [replacement, replacement]) - else: - expect = psr.replace([2, 3], [replacement, replacement]).astype( - psr.dtype - ) - got = sr.replace([2, 3], [replacement, replacement]) - assert_eq(expect, got) + expect = psr.replace([2, 3], [replacement, replacement]) + got = sr.replace([2, 3], [replacement, replacement]) + assert_eq(expect, got) @pytest.mark.parametrize( @@ -1392,3 +1396,52 @@ def test_replace_with_index_objects(): result = cudf.Series([1, 2]).replace(cudf.Index([1]), cudf.Index([2])) expected = pd.Series([1, 2]).replace(pd.Index([1]), pd.Index([2])) assert_eq(result, expected) + + +# Example test function for datetime series replace +def test_replace_datetime_series(): + # Create a pandas datetime series + pd_series = pd.Series(pd.date_range("20210101", periods=5)) + # Replace a specific datetime value + pd_result = pd_series.replace( + pd.Timestamp("2021-01-02"), pd.Timestamp("2021-01-10") + ) + + # Create a cudf datetime series + cudf_series = cudf.Series(pd.date_range("20210101", periods=5)) + # Replace a specific datetime value + cudf_result = cudf_series.replace( + pd.Timestamp("2021-01-02"), pd.Timestamp("2021-01-10") + ) + + assert_eq(pd_result, cudf_result) + + +# Example test function for timedelta series replace +def test_replace_timedelta_series(): + # Create a pandas timedelta series + pd_series = pd.Series(pd.timedelta_range("1 days", periods=5)) + # Replace a specific timedelta value + pd_result = pd_series.replace( + pd.Timedelta("2 days"), pd.Timedelta("10 days") + ) + + # Create a cudf timedelta series + cudf_series = cudf.Series(pd.timedelta_range("1 days", periods=5)) + # Replace a specific timedelta value + cudf_result = cudf_series.replace( + pd.Timedelta("2 days"), pd.Timedelta("10 days") + ) + + assert_eq(pd_result, cudf_result) + + +def test_replace_multiple_rows(datadir): + path = datadir / "parquet" / "replace_multiple_rows.parquet" + pdf = pd.read_parquet(path) + gdf = cudf.read_parquet(path) + + pdf.replace([np.inf, -np.inf], np.nan, inplace=True) + gdf.replace([np.inf, -np.inf], np.nan, inplace=True) + + assert_eq(pdf, gdf, check_dtype=False) diff --git a/python/cudf/cudf/utils/dtypes.py b/python/cudf/cudf/utils/dtypes.py index b0788bcc0fc..57bf08e6eec 100644 --- a/python/cudf/cudf/utils/dtypes.py +++ b/python/cudf/cudf/utils/dtypes.py @@ -364,13 +364,19 @@ def min_column_type(x, expected_type): if x.null_count == len(x): return x.dtype - if x.dtype.kind == "f": - return get_min_float_dtype(x) - - elif cudf.dtype(expected_type).kind in "iu": - max_bound_dtype = np.min_scalar_type(x.max()) - min_bound_dtype = np.min_scalar_type(x.min()) + min_value, max_value = x.min(), x.max() + either_is_inf = np.isinf(min_value) or np.isinf(max_value) + expected_type = cudf.dtype(expected_type) + if not either_is_inf and expected_type.kind in "i": + max_bound_dtype = min_signed_type(max_value) + min_bound_dtype = min_signed_type(min_value) result_type = np.promote_types(max_bound_dtype, min_bound_dtype) + elif not either_is_inf and expected_type.kind in "u": + max_bound_dtype = min_unsigned_type(max_value) + min_bound_dtype = min_unsigned_type(min_value) + result_type = np.promote_types(max_bound_dtype, min_bound_dtype) + elif x.dtype.kind == "f": + return get_min_float_dtype(x) else: result_type = x.dtype diff --git a/python/cudf/cudf/utils/utils.py b/python/cudf/cudf/utils/utils.py index 7347ec7866a..294253cd119 100644 --- a/python/cudf/cudf/utils/utils.py +++ b/python/cudf/cudf/utils/utils.py @@ -6,6 +6,7 @@ import os import traceback import warnings +from typing import Any import numpy as np import pandas as pd @@ -403,3 +404,40 @@ def _all_bools_with_nulls(lhs, rhs, bool_fill_value): if result_mask is not None: result_col = result_col.set_mask(result_mask.as_mask()) return result_col + + +def _datetime_timedelta_find_and_replace( + original_column: "cudf.core.column.DatetimeColumn" + | "cudf.core.column.TimeDeltaColumn", + to_replace: Any, + replacement: Any, + all_nan: bool = False, +) -> "cudf.core.column.DatetimeColumn" | "cudf.core.column.TimeDeltaColumn": + """ + This is an internal utility to find and replace values in a datetime or + timedelta column. It is used by the `find_and_replace` method of + `DatetimeColumn` and `TimeDeltaColumn`. Centralizing the code in a single + as opposed to duplicating it in both classes. + """ + original_col_class = type(original_column) + if not isinstance(to_replace, original_col_class): + to_replace = cudf.core.column.as_column(to_replace) + if to_replace.can_cast_safely(original_column.dtype): + to_replace = to_replace.astype(original_column.dtype) + if not isinstance(replacement, original_col_class): + replacement = cudf.core.column.as_column(replacement) + if replacement.can_cast_safely(original_column.dtype): + replacement = replacement.astype(original_column.dtype) + if isinstance(to_replace, original_col_class): + to_replace = to_replace.as_numerical_column(dtype=np.dtype("int64")) + if isinstance(replacement, original_col_class): + replacement = replacement.as_numerical_column(dtype=np.dtype("int64")) + try: + result_col = ( + original_column.as_numerical_column(dtype=np.dtype("int64")) + .find_and_replace(to_replace, replacement, all_nan) + .astype(original_column.dtype) + ) + except TypeError: + result_col = original_column.copy(deep=True) + return result_col # type: ignore