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@@ -44,7 +44,6 @@ jobs: | |
| container_image: "rapidsai/ci-conda:latest" | ||
| run_script: "ci/test_cpp_memcheck.sh" | ||
| static-configure: | ||
| needs: checks | ||
| secrets: inherit | ||
| uses: rapidsai/shared-workflows/.github/workflows/[email protected] | ||
| with: | ||
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| /* | ||
| * 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. | ||
| */ | ||
|
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| #include <benchmarks/common/generate_input.hpp> | ||
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| #include <cudf/types.hpp> | ||
| #include <cudf/unary.hpp> | ||
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| #include <nvbench/nvbench.cuh> | ||
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| #include <type_traits> | ||
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| // This benchmark compares the cost of converting decimal <--> floating point | ||
| template <typename InputType, typename OutputType> | ||
| void bench_cast_decimal(nvbench::state& state, nvbench::type_list<InputType, OutputType>) | ||
| { | ||
| static constexpr bool is_input_floating = std::is_floating_point_v<InputType>; | ||
| static constexpr bool is_output_floating = std::is_floating_point_v<OutputType>; | ||
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| static constexpr bool is_double = | ||
| std::is_same_v<InputType, double> || std::is_same_v<OutputType, double>; | ||
| static constexpr bool is_32bit = | ||
| std::is_same_v<InputType, numeric::decimal32> || std::is_same_v<OutputType, numeric::decimal32>; | ||
| static constexpr bool is_128bit = std::is_same_v<InputType, numeric::decimal128> || | ||
| std::is_same_v<OutputType, numeric::decimal128>; | ||
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| // Skip floating --> floating and decimal --> decimal | ||
| if constexpr (is_input_floating == is_output_floating) { | ||
| state.skip("Meaningless conversion."); | ||
| return; | ||
| } | ||
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| // Skip float <--> dec128 | ||
| if constexpr (!is_double && is_128bit) { | ||
| state.skip("Ignoring float <--> dec128."); | ||
| return; | ||
| } | ||
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| // Get settings | ||
| auto const num_rows = static_cast<cudf::size_type>(state.get_int64("num_rows")); | ||
| auto const exp_mode = state.get_int64("exp_range"); | ||
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| // Exponent range: Range size is 10^6 | ||
| // These probe the edges of the float and double ranges, as well as more common values | ||
| int const exp_min_array[] = {-307, -37, -14, -3, 8, 31, 301}; | ||
| int const exp_range_size = 6; | ||
| int const exp_min = exp_min_array[exp_mode]; | ||
| int const exp_max = exp_min + exp_range_size; | ||
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| // With exp range size of 6, decimal output (generated or casted-to) has 7 digits of precision | ||
| int const extra_digits_precision = 1; | ||
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| // Exclude end range of double from float test | ||
| if (!is_double && ((exp_mode == 0) || (exp_mode == 6))) { | ||
| state.skip("Range beyond end of float tests."); | ||
| return; | ||
| } | ||
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| // The current float <--> decimal conversion algorithm is limited | ||
| static constexpr bool is_64bit = !is_32bit && !is_128bit; | ||
| if (is_32bit && (exp_mode != 3)) { | ||
| state.skip("Decimal32 conversion only works up to scale factors of 10^9."); | ||
| return; | ||
| } | ||
| if (is_64bit && ((exp_mode < 2) || (exp_mode > 4))) { | ||
| state.skip("Decimal64 conversion only works up to scale factors of 10^18."); | ||
| return; | ||
| } | ||
| if (is_128bit && ((exp_mode == 0) || (exp_mode == 6))) { | ||
| state.skip("Decimal128 conversion only works up to scale factors of 10^38."); | ||
| return; | ||
| } | ||
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| // Type IDs | ||
| auto const input_id = cudf::type_to_id<InputType>(); | ||
| auto const output_id = cudf::type_to_id<OutputType>(); | ||
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| // Create data profile and scale | ||
| auto const [output_scale, profile] = [&]() { | ||
| if constexpr (is_input_floating) { | ||
| // Range for generated floating point values | ||
| auto get_pow10 = [](auto exp10) { | ||
| return std::pow(static_cast<InputType>(10), static_cast<InputType>(exp10)); | ||
| }; | ||
| InputType const floating_range_min = get_pow10(exp_min); | ||
| InputType const floating_range_max = get_pow10(exp_max); | ||
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| // With exp range size of 6, output has 7 decimal digits of precision | ||
| auto const decimal_output_scale = exp_min - extra_digits_precision; | ||
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| // Input distribution | ||
| data_profile const profile = data_profile_builder().distribution( | ||
| input_id, distribution_id::NORMAL, floating_range_min, floating_range_max); | ||
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| return std::pair{decimal_output_scale, profile}; | ||
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| } else { // Generating decimals | ||
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| using decimal_rep_type = typename InputType::rep; | ||
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| // For exp range size 6 and precision 7, generates ints between 10 and 10^7, | ||
| // with scale factor of: exp_max - 7. This matches floating point generation. | ||
| int const digits_precision = exp_range_size + extra_digits_precision; | ||
| auto const decimal_input_scale = numeric::scale_type{exp_max - digits_precision}; | ||
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| // Range for generated integer values | ||
| auto get_pow10 = [](auto exp10) { | ||
| return numeric::detail::ipow<decimal_rep_type, numeric::Radix::BASE_10>(exp10); | ||
| }; | ||
| auto const decimal_range_min = get_pow10(digits_precision - exp_range_size); | ||
| auto const decimal_range_max = get_pow10(digits_precision); | ||
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| // Input distribution | ||
| data_profile const profile = data_profile_builder().distribution(input_id, | ||
| distribution_id::NORMAL, | ||
| decimal_range_min, | ||
| decimal_range_max, | ||
| decimal_input_scale); | ||
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| return std::pair{0, profile}; | ||
| } | ||
| }(); | ||
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| // Generate input data | ||
| auto const input_col = create_random_column(input_id, row_count{num_rows}, profile); | ||
| auto const input_view = input_col->view(); | ||
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| // Output type | ||
| auto const output_type = | ||
| !is_input_floating ? cudf::data_type(output_id) : cudf::data_type(output_id, output_scale); | ||
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| // Stream | ||
| auto stream = cudf::get_default_stream(); | ||
| state.set_cuda_stream(nvbench::make_cuda_stream_view(stream.value())); | ||
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| // Run benchmark | ||
| state.exec(nvbench::exec_tag::sync, | ||
| [&](nvbench::launch&) { cudf::cast(input_view, output_type); }); | ||
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| // Throughput statistics | ||
| state.add_element_count(num_rows); | ||
| state.add_global_memory_reads<InputType>(num_rows); | ||
| state.add_global_memory_writes<OutputType>(num_rows); | ||
| } | ||
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| // Data types | ||
| using data_types = | ||
| nvbench::type_list<float, double, numeric::decimal32, numeric::decimal64, numeric::decimal128>; | ||
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| NVBENCH_BENCH_TYPES(bench_cast_decimal, NVBENCH_TYPE_AXES(data_types, data_types)) | ||
| .set_name("decimal_floating_conversion") | ||
| .set_type_axes_names({"InputType", "OutputType"}) | ||
| .add_int64_power_of_two_axis("num_rows", {28}) | ||
| .add_int64_axis("exp_range", nvbench::range(0, 6)); |
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