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| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -51,15 +51,25 @@ jobs: | |
| - '**' | ||
| - '!CONTRIBUTING.md' | ||
| - '!README.md' | ||
| - '!ci/cudf_pandas_scripts/**' | ||
| - '!docs/**' | ||
| - '!img/**' | ||
| - '!java/**' | ||
| - '!notebooks/**' | ||
| - '!python/**' | ||
| test_cudf_pandas: | ||
| - '**' | ||
| - '!CONTRIBUTING.md' | ||
| - '!README.md' | ||
| - '!docs/**' | ||
| - '!img/**' | ||
| - '!java/**' | ||
| - '!notebooks/**' | ||
| test_java: | ||
| - '**' | ||
| - '!CONTRIBUTING.md' | ||
| - '!README.md' | ||
| - '!ci/cudf_pandas_scripts/**' | ||
| - '!docs/**' | ||
| - '!img/**' | ||
| - '!notebooks/**' | ||
|
|
@@ -68,11 +78,13 @@ jobs: | |
| - '**' | ||
| - '!CONTRIBUTING.md' | ||
| - '!README.md' | ||
| - '!ci/cudf_pandas_scripts/**' | ||
| - '!java/**' | ||
| test_python: | ||
| - '**' | ||
| - '!CONTRIBUTING.md' | ||
| - '!README.md' | ||
| - '!ci/cudf_pandas_scripts/**' | ||
| - '!docs/**' | ||
| - '!img/**' | ||
| - '!java/**' | ||
|
|
@@ -263,7 +275,7 @@ jobs: | |
| needs: [wheel-build-cudf, changed-files] | ||
| secrets: inherit | ||
| uses: rapidsai/shared-workflows/.github/workflows/[email protected] | ||
| if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python | ||
| if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python || fromJSON(needs.changed-files.outputs.changed_file_groups).test_cudf_pandas | ||
| with: | ||
| # This selects "ARCH=amd64 + the latest supported Python + CUDA". | ||
| matrix_filter: map(select(.ARCH == "amd64")) | group_by(.CUDA_VER|split(".")|map(tonumber)|.[0]) | map(max_by([(.PY_VER|split(".")|map(tonumber)), (.CUDA_VER|split(".")|map(tonumber))])) | ||
|
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@@ -274,7 +286,7 @@ jobs: | |
| needs: [wheel-build-cudf, changed-files] | ||
| secrets: inherit | ||
| uses: rapidsai/shared-workflows/.github/workflows/[email protected] | ||
| if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python | ||
| if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python || fromJSON(needs.changed-files.outputs.changed_file_groups).test_cudf_pandas | ||
| with: | ||
| # This selects "ARCH=amd64 + the latest supported Python + CUDA". | ||
| matrix_filter: map(select(.ARCH == "amd64")) | group_by(.CUDA_VER|split(".")|map(tonumber)|.[0]) | map(max_by([(.PY_VER|split(".")|map(tonumber)), (.CUDA_VER|split(".")|map(tonumber))])) | ||
|
|
||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,123 @@ | ||
| /* | ||
| * 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. | ||
| */ | ||
|
|
||
| #include <cudf_test/column_wrapper.hpp> | ||
|
|
||
| #include <cudf/detail/tdigest/tdigest.hpp> | ||
| #include <cudf/utilities/default_stream.hpp> | ||
|
|
||
| #include <rmm/exec_policy.hpp> | ||
|
|
||
| #include <cuda/functional> | ||
| #include <thrust/copy.h> | ||
| #include <thrust/execution_policy.h> | ||
|
|
||
| #include <nvbench/nvbench.cuh> | ||
|
|
||
| void bm_tdigest_merge(nvbench::state& state) | ||
| { | ||
| auto const num_tdigests = static_cast<cudf::size_type>(state.get_int64("num_tdigests")); | ||
| auto const tdigest_size = static_cast<cudf::size_type>(state.get_int64("tdigest_size")); | ||
| auto const tdigests_per_group = | ||
| static_cast<cudf::size_type>(state.get_int64("tdigests_per_group")); | ||
| auto const max_centroids = static_cast<cudf::size_type>(state.get_int64("max_centroids")); | ||
| auto const num_groups = num_tdigests / tdigests_per_group; | ||
| auto const total_centroids = num_tdigests * tdigest_size; | ||
|
|
||
| auto stream = cudf::get_default_stream(); | ||
| auto mr = rmm::mr::get_current_device_resource(); | ||
|
|
||
| constexpr int base_value = 5; | ||
|
|
||
| // construct inner means/weights | ||
| auto val_iter = cudf::detail::make_counting_transform_iterator( | ||
| 0, cuda::proclaim_return_type<double>([tdigest_size](cudf::size_type i) { | ||
| return static_cast<double>(base_value + (i % tdigest_size)); | ||
| })); | ||
| auto one_iter = thrust::make_constant_iterator(1); | ||
| cudf::test::fixed_width_column_wrapper<double> means(val_iter, val_iter + total_centroids); | ||
| cudf::test::fixed_width_column_wrapper<double> weights(one_iter, one_iter + total_centroids); | ||
| std::vector<std::unique_ptr<cudf::column>> inner_struct_children; | ||
| inner_struct_children.push_back(means.release()); | ||
| inner_struct_children.push_back(weights.release()); | ||
| cudf::test::structs_column_wrapper inner_struct(std::move(inner_struct_children)); | ||
|
|
||
| // construct the tdigest lists themselves | ||
| auto offset_iter = cudf::detail::make_counting_transform_iterator( | ||
| 0, cuda::proclaim_return_type<cudf::size_type>([tdigest_size](cudf::size_type i) { | ||
| return i * tdigest_size; | ||
| })); | ||
| cudf::test::fixed_width_column_wrapper<int> offsets(offset_iter, offset_iter + num_tdigests + 1); | ||
| auto list_col = cudf::make_lists_column( | ||
| num_tdigests, offsets.release(), inner_struct.release(), 0, {}, stream, mr); | ||
|
|
||
| // min and max columns | ||
| auto min_iter = thrust::make_constant_iterator(base_value); | ||
| auto max_iter = thrust::make_constant_iterator(base_value + (tdigest_size - 1)); | ||
| cudf::test::fixed_width_column_wrapper<double> mins(min_iter, min_iter + num_tdigests); | ||
| cudf::test::fixed_width_column_wrapper<double> maxes(max_iter, max_iter + num_tdigests); | ||
|
|
||
| // assemble the whole thing | ||
| std::vector<std::unique_ptr<cudf::column>> tdigest_children; | ||
| tdigest_children.push_back(std::move(list_col)); | ||
| tdigest_children.push_back(mins.release()); | ||
| tdigest_children.push_back(maxes.release()); | ||
| cudf::test::structs_column_wrapper tdigest(std::move(tdigest_children)); | ||
|
|
||
| rmm::device_uvector<cudf::size_type> group_offsets(num_groups + 1, stream, mr); | ||
| rmm::device_uvector<cudf::size_type> group_labels(num_tdigests, stream, mr); | ||
| auto group_offset_iter = cudf::detail::make_counting_transform_iterator( | ||
| 0, | ||
| cuda::proclaim_return_type<cudf::size_type>( | ||
| [tdigests_per_group] __device__(cudf::size_type i) { return i * tdigests_per_group; })); | ||
| thrust::copy(rmm::exec_policy_nosync(stream, mr), | ||
| group_offset_iter, | ||
| group_offset_iter + num_groups + 1, | ||
| group_offsets.begin()); | ||
| auto group_label_iter = cudf::detail::make_counting_transform_iterator( | ||
| 0, | ||
| cuda::proclaim_return_type<cudf::size_type>( | ||
| [tdigests_per_group] __device__(cudf::size_type i) { return i / tdigests_per_group; })); | ||
| thrust::copy(rmm::exec_policy_nosync(stream, mr), | ||
| group_label_iter, | ||
| group_label_iter + num_tdigests, | ||
| group_labels.begin()); | ||
|
|
||
| state.add_element_count(total_centroids); | ||
|
|
||
| state.set_cuda_stream(nvbench::make_cuda_stream_view(stream.value())); | ||
| state.exec(nvbench::exec_tag::timer | nvbench::exec_tag::sync, | ||
| [&](nvbench::launch& launch, auto& timer) { | ||
| timer.start(); | ||
| auto result = cudf::tdigest::detail::group_merge_tdigest( | ||
| tdigest, group_offsets, group_labels, num_groups, max_centroids, stream, mr); | ||
| timer.stop(); | ||
| }); | ||
| } | ||
|
|
||
| NVBENCH_BENCH(bm_tdigest_merge) | ||
| .set_name("TDigest many tiny groups") | ||
| .add_int64_axis("num_tdigests", {500'000}) | ||
| .add_int64_axis("tdigest_size", {1, 1000}) | ||
| .add_int64_axis("tdigests_per_group", {1}) | ||
| .add_int64_axis("max_centroids", {10000, 1000}); | ||
|
|
||
| NVBENCH_BENCH(bm_tdigest_merge) | ||
| .set_name("TDigest many small groups") | ||
| .add_int64_axis("num_tdigests", {500'000}) | ||
| .add_int64_axis("tdigest_size", {1, 1000}) | ||
| .add_int64_axis("tdigests_per_group", {3}) | ||
| .add_int64_axis("max_centroids", {10000, 1000}); |
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