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grouped_rolling_test.cpp
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grouped_rolling_test.cpp
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/*
* 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.
* 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 "rolling_test.hpp"
#include <cudf_test/base_fixture.hpp>
#include <cudf_test/column_utilities.hpp>
#include <cudf_test/column_wrapper.hpp>
#include <cudf_test/cudf_gtest.hpp>
#include <cudf_test/iterator_utilities.hpp>
#include <cudf_test/type_lists.hpp>
#include <cudf/aggregation.hpp>
#include <cudf/detail/aggregation/aggregation.hpp>
#include <cudf/rolling.hpp>
#include <cudf/table/table_view.hpp>
#include <cudf/utilities/bit.hpp>
#include <thrust/host_vector.h>
#include <thrust/iterator/counting_iterator.h>
#include <src/rolling/detail/rolling.hpp>
const std::string cuda_func{
R"***(
template <typename OutType, typename InType>
__device__ void CUDA_GENERIC_AGGREGATOR(OutType *ret, InType *in_col, cudf::size_type start,
cudf::size_type count) {
OutType val = 0;
for (cudf::size_type i = 0; i < count; i++) {
val += in_col[start + i];
}
*ret = val;
}
)***"};
const std::string ptx_func{
R"***(
//
// Generated by NVIDIA NVVM Compiler
//
// Compiler Build ID: CL-24817639
// Cuda compilation tools, release 10.0, V10.0.130
// Based on LLVM 3.4svn
//
.version 6.3
.target sm_70
.address_size 64
// .globl _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE
.common .global .align 8 .u64 _ZN08NumbaEnv8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE;
.visible .func (.param .b32 func_retval0) _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE(
.param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_0,
.param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_1,
.param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_2,
.param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_3,
.param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_4,
.param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_5,
.param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_6,
.param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_7
)
{
.reg .pred %p<3>;
.reg .b32 %r<6>;
.reg .b64 %rd<18>;
ld.param.u64 %rd6, [_ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_0];
ld.param.u64 %rd7, [_ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_5];
ld.param.u64 %rd8, [_ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_6];
ld.param.u64 %rd9, [_ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_7];
mov.u64 %rd15, 0;
mov.u64 %rd16, %rd15;
BB0_1:
mov.u64 %rd2, %rd16;
mov.u32 %r5, 0;
setp.ge.s64 %p1, %rd15, %rd8;
mov.u64 %rd17, %rd15;
@%p1 bra BB0_3;
mul.lo.s64 %rd12, %rd15, %rd9;
add.s64 %rd13, %rd12, %rd7;
ld.u32 %r5, [%rd13];
add.s64 %rd17, %rd15, 1;
BB0_3:
cvt.s64.s32 %rd14, %r5;
add.s64 %rd16, %rd14, %rd2;
setp.lt.s64 %p2, %rd15, %rd8;
mov.u64 %rd15, %rd17;
@%p2 bra BB0_1;
st.u64 [%rd6], %rd2;
mov.u32 %r4, 0;
st.param.b32 [func_retval0+0], %r4;
ret;
}
)***"};
template <typename T>
class GroupedRollingTest : public cudf::test::BaseFixture {
protected:
// input as column_wrapper
void run_test_col(cudf::table_view const& keys,
cudf::column_view const& input,
std::vector<cudf::size_type> const& expected_grouping,
cudf::size_type const& preceding_window,
cudf::size_type const& following_window,
cudf::size_type min_periods,
cudf::rolling_aggregation const& op)
{
std::unique_ptr<cudf::column> output;
// wrap windows
EXPECT_NO_THROW(output = cudf::grouped_rolling_window(
keys, input, preceding_window, following_window, min_periods, op));
auto reference = create_reference_output(
op, input, expected_grouping, preceding_window, following_window, min_periods);
CUDF_TEST_EXPECT_COLUMNS_EQUAL(*output, *reference);
}
void run_test_col_agg(cudf::table_view const& keys,
cudf::column_view const& input,
std::vector<cudf::size_type> const& expected_grouping,
cudf::size_type preceding_window,
cudf::size_type following_window,
cudf::size_type min_periods)
{
// Skip grouping-tests on bool8 keys. sort_helper does not support this.
if (keys.num_columns() > 0 && cudf::is_boolean(keys.column(0).type())) { return; }
// test all supported aggregators
run_test_col(keys,
input,
expected_grouping,
preceding_window,
following_window,
min_periods,
*cudf::make_min_aggregation<cudf::rolling_aggregation>());
run_test_col(keys,
input,
expected_grouping,
preceding_window,
following_window,
min_periods,
*cudf::make_count_aggregation<cudf::rolling_aggregation>());
run_test_col(
keys,
input,
expected_grouping,
preceding_window,
following_window,
min_periods,
*cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE));
run_test_col(keys,
input,
expected_grouping,
preceding_window,
following_window,
min_periods,
*cudf::make_max_aggregation<cudf::rolling_aggregation>());
if (!cudf::is_timestamp(input.type())) {
run_test_col(keys,
input,
expected_grouping,
preceding_window,
following_window,
min_periods,
*cudf::make_sum_aggregation<cudf::rolling_aggregation>());
run_test_col(keys,
input,
expected_grouping,
preceding_window,
following_window,
min_periods,
*cudf::make_mean_aggregation<cudf::rolling_aggregation>());
}
run_test_col(keys,
input,
expected_grouping,
preceding_window,
following_window,
min_periods,
*cudf::make_row_number_aggregation<cudf::rolling_aggregation>());
// >>> test UDFs <<<
if (input.type() == cudf::data_type{cudf::type_id::INT32} && !input.has_nulls()) {
auto cuda_udf_agg = cudf::make_udf_aggregation<cudf::rolling_aggregation>(
cudf::udf_type::CUDA, cuda_func, cudf::data_type{cudf::type_id::INT64});
run_test_col(keys,
input,
expected_grouping,
preceding_window,
following_window,
min_periods,
*cuda_udf_agg);
auto ptx_udf_agg = cudf::make_udf_aggregation<cudf::rolling_aggregation>(
cudf::udf_type::PTX, ptx_func, cudf::data_type{cudf::type_id::INT64});
run_test_col(keys,
input,
expected_grouping,
preceding_window,
following_window,
min_periods,
*ptx_udf_agg);
}
}
private:
// use SFINAE to only instantiate for supported combinations
// specialization for COUNT_VALID, COUNT_ALL
template <bool include_nulls>
std::unique_ptr<cudf::column> create_count_reference_output(
cudf::column_view const& input,
std::vector<cudf::size_type> const& group_offsets,
cudf::size_type const& preceding_window,
cudf::size_type const& following_window,
cudf::size_type min_periods)
{
cudf::size_type num_rows = input.size();
thrust::host_vector<cudf::size_type> ref_data(num_rows);
thrust::host_vector<bool> ref_valid(num_rows);
// input data and mask
std::vector<cudf::bitmask_type> in_valid = cudf::test::bitmask_to_host(input);
cudf::bitmask_type* valid_mask = in_valid.data();
for (cudf::size_type i = 0; i < num_rows; i++) {
// load sizes
min_periods = std::max(min_periods, 1); // at least one observation is required
// compute bounds
auto group_end_index = std::upper_bound(group_offsets.begin(), group_offsets.end(), i);
auto group_start_index = group_end_index - 1;
cudf::size_type start = std::min(num_rows, std::max(0, i - preceding_window + 1));
cudf::size_type end = std::min(num_rows, std::max(0, i + following_window + 1));
cudf::size_type start_index = std::max(*group_start_index, std::min(start, end));
cudf::size_type end_index = std::min(*group_end_index, std::max(start, end));
// aggregate
cudf::size_type count = 0;
for (cudf::size_type j = start_index; j < end_index; j++) {
if (include_nulls || !input.nullable() || cudf::bit_is_set(valid_mask, j)) count++;
}
ref_valid[i] = ((end_index - start_index) >= min_periods);
if (ref_valid[i]) ref_data[i] = count;
}
cudf::test::fixed_width_column_wrapper<cudf::size_type> col(
ref_data.begin(), ref_data.end(), ref_valid.begin());
return col.release();
}
std::unique_ptr<cudf::column> create_row_number_reference_output(
cudf::column_view const& input,
std::vector<cudf::size_type> const& group_offsets,
cudf::size_type const& preceding_window,
cudf::size_type const& following_window,
cudf::size_type min_periods)
{
cudf::size_type num_rows = input.size();
thrust::host_vector<cudf::size_type> ref_data(num_rows);
thrust::host_vector<bool> ref_valid(num_rows);
// input data and mask
std::vector<cudf::bitmask_type> in_valid = cudf::test::bitmask_to_host(input);
for (cudf::size_type i = 0; i < num_rows; i++) {
// load sizes
min_periods = std::max(min_periods, 1); // at least one observation is required
// compute bounds
auto group_end_index = std::upper_bound(group_offsets.begin(), group_offsets.end(), i);
auto group_start_index = group_end_index - 1;
cudf::size_type start = std::min(num_rows, std::max(0, i - preceding_window + 1));
cudf::size_type end = std::min(num_rows, std::max(0, i + following_window + 1));
cudf::size_type start_index = std::max(*group_start_index, std::min(start, end));
cudf::size_type end_index = std::min(*group_end_index, std::max(start, end));
// aggregate
cudf::size_type count{end_index - start_index};
cudf::size_type row_number{i - start_index + 1};
ref_valid[i] = (count >= min_periods);
ref_data[i] = row_number;
}
cudf::test::fixed_width_column_wrapper<cudf::size_type> col(
ref_data.begin(), ref_data.end(), ref_valid.begin());
return col.release();
}
template <typename agg_op,
cudf::aggregation::Kind k,
typename OutputType,
bool is_mean,
std::enable_if_t<is_rolling_supported<T, k>()>* = nullptr>
std::unique_ptr<cudf::column> create_reference_output(
cudf::column_view const& input,
std::vector<cudf::size_type> const& group_offsets,
cudf::size_type const& preceding_window,
cudf::size_type const& following_window,
cudf::size_type min_periods)
{
cudf::size_type num_rows = input.size();
thrust::host_vector<OutputType> ref_data(num_rows);
thrust::host_vector<bool> ref_valid(num_rows);
// input data and mask
auto [in_col, in_valid] = cudf::test::to_host<T>(input);
cudf::bitmask_type* valid_mask = in_valid.data();
agg_op op;
for (cudf::size_type i = 0; i < num_rows; i++) {
auto val = agg_op::template identity<OutputType>();
// load sizes
min_periods = std::max(min_periods, 1); // at least one observation is required
// compute bounds
auto group_end_index = std::upper_bound(group_offsets.begin(), group_offsets.end(), i);
auto group_start_index = group_end_index - 1;
cudf::size_type start = std::min(
num_rows, std::max(0, i - preceding_window + 1)); // Preceding window includes current row.
cudf::size_type end = std::min(num_rows, std::max(0, i + following_window + 1));
cudf::size_type start_index = std::max(*group_start_index, std::min(start, end));
cudf::size_type end_index = std::min(*group_end_index, std::max(start, end));
// aggregate
cudf::size_type count = 0;
for (cudf::size_type j = start_index; j < end_index; j++) {
if (!input.nullable() || cudf::bit_is_set(valid_mask, j)) {
val = op(static_cast<OutputType>(in_col[j]), val);
count++;
}
}
ref_valid[i] = (count >= min_periods);
if (ref_valid[i]) {
cudf::detail::rolling_store_output_functor<OutputType, is_mean>{}(ref_data[i], val, count);
}
}
cudf::test::fixed_width_column_wrapper<OutputType> col(
ref_data.begin(), ref_data.end(), ref_valid.begin());
return col.release();
}
template <typename agg_op,
cudf::aggregation::Kind k,
typename OutputType,
bool is_mean,
std::enable_if_t<!is_rolling_supported<T, k>()>* = nullptr>
std::unique_ptr<cudf::column> create_reference_output(
cudf::column_view const& input,
std::vector<cudf::size_type> const& group_offsets,
cudf::size_type const& preceding_window_col,
cudf::size_type const& following_window_col,
cudf::size_type min_periods)
{
CUDF_FAIL("Unsupported combination of type and aggregation");
}
std::unique_ptr<cudf::column> create_reference_output(
cudf::rolling_aggregation const& op,
cudf::column_view const& input,
std::vector<cudf::size_type> const& group_offsets,
cudf::size_type const& preceding_window,
cudf::size_type const& following_window,
cudf::size_type min_periods)
{
// unroll aggregation types
switch (op.kind) {
case cudf::aggregation::SUM:
return create_reference_output<cudf::DeviceSum,
cudf::aggregation::SUM,
cudf::detail::target_type_t<T, cudf::aggregation::SUM>,
false>(
input, group_offsets, preceding_window, following_window, min_periods);
case cudf::aggregation::MIN:
return create_reference_output<cudf::DeviceMin,
cudf::aggregation::MIN,
cudf::detail::target_type_t<T, cudf::aggregation::MIN>,
false>(
input, group_offsets, preceding_window, following_window, min_periods);
case cudf::aggregation::MAX:
return create_reference_output<cudf::DeviceMax,
cudf::aggregation::MAX,
cudf::detail::target_type_t<T, cudf::aggregation::MAX>,
false>(
input, group_offsets, preceding_window, following_window, min_periods);
case cudf::aggregation::COUNT_VALID:
return create_count_reference_output<false>(
input, group_offsets, preceding_window, following_window, min_periods);
case cudf::aggregation::COUNT_ALL:
return create_count_reference_output<true>(
input, group_offsets, preceding_window, following_window, min_periods);
case cudf::aggregation::ROW_NUMBER:
return create_row_number_reference_output(
input, group_offsets, preceding_window, following_window, min_periods);
case cudf::aggregation::MEAN:
return create_reference_output<cudf::DeviceSum,
cudf::aggregation::MEAN,
cudf::detail::target_type_t<T, cudf::aggregation::MEAN>,
true>(
input, group_offsets, preceding_window, following_window, min_periods);
// >>> UDFs <<<
case cudf::aggregation::CUDA:
return create_reference_output<cudf::DeviceSum,
cudf::aggregation::SUM,
cudf::detail::target_type_t<T, cudf::aggregation::SUM>,
false>(
input, group_offsets, preceding_window, following_window, min_periods);
case cudf::aggregation::PTX:
return create_reference_output<cudf::DeviceSum,
cudf::aggregation::SUM,
cudf::detail::target_type_t<T, cudf::aggregation::SUM>,
false>(
input, group_offsets, preceding_window, following_window, min_periods);
default: return cudf::test::fixed_width_column_wrapper<T>({}).release();
}
}
};
// // ------------- expected failures --------------------
class GroupedRollingErrorTest : public cudf::test::BaseFixture {};
// negative sizes
TEST_F(GroupedRollingErrorTest, NegativeMinPeriods)
{
// Construct agg column.
const std::vector<cudf::size_type> col_data{0, 1, 2, 0, 4};
const std::vector<bool> col_valid{true, true, true, false, true};
cudf::test::fixed_width_column_wrapper<cudf::size_type> input{
col_data.begin(), col_data.end(), col_valid.begin()};
// Construct Grouping keys table-view.
auto const N_ELEMENTS{col_data.size()};
const std::vector<cudf::size_type> grouping_key_vec(N_ELEMENTS, 0);
cudf::test::fixed_width_column_wrapper<cudf::size_type> grouping_keys_col(
grouping_key_vec.begin(), grouping_key_vec.end(), col_valid.begin());
const cudf::table_view grouping_keys{std::vector<cudf::column_view>{grouping_keys_col}};
EXPECT_THROW(
cudf::grouped_rolling_window(
grouping_keys, input, 2, 2, -2, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
cudf::logic_error);
}
TEST_F(GroupedRollingErrorTest, EmptyInput)
{
cudf::test::fixed_width_column_wrapper<int32_t> empty_col{};
std::unique_ptr<cudf::column> output;
const cudf::table_view grouping_keys{std::vector<cudf::column_view>{}};
EXPECT_NO_THROW(
output = cudf::grouped_rolling_window(
grouping_keys, empty_col, 2, 0, 2, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()));
EXPECT_EQ(output->size(), 0);
}
// incorrect type/aggregation combo: sum of timestamps
TEST_F(GroupedRollingErrorTest, SumTimestampNotSupported)
{
constexpr cudf::size_type size{10};
cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep> input_D(
thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep> input_s(
thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
cudf::test::fixed_width_column_wrapper<cudf::timestamp_ms, cudf::timestamp_ms::rep> input_ms(
thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
cudf::test::fixed_width_column_wrapper<cudf::timestamp_us, cudf::timestamp_us::rep> input_us(
thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
cudf::test::fixed_width_column_wrapper<cudf::timestamp_ns, cudf::timestamp_ns::rep> input_ns(
thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
// Construct table-view of grouping keys.
std::vector<cudf::size_type> grouping_keys_vec(size, 0); // `size` elements, each == 0.
const cudf::table_view grouping_keys{
std::vector<cudf::column_view>{cudf::test::fixed_width_column_wrapper<cudf::size_type>(
grouping_keys_vec.begin(), grouping_keys_vec.end())}};
EXPECT_THROW(
cudf::grouped_rolling_window(
grouping_keys, input_D, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
cudf::logic_error);
EXPECT_THROW(
cudf::grouped_rolling_window(
grouping_keys, input_s, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
cudf::logic_error);
EXPECT_THROW(
cudf::grouped_rolling_window(
grouping_keys, input_ms, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
cudf::logic_error);
EXPECT_THROW(
cudf::grouped_rolling_window(
grouping_keys, input_us, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
cudf::logic_error);
EXPECT_THROW(
cudf::grouped_rolling_window(
grouping_keys, input_ns, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
cudf::logic_error);
}
TYPED_TEST_SUITE(GroupedRollingTest, cudf::test::FixedWidthTypesWithoutFixedPoint);
TYPED_TEST(GroupedRollingTest, SimplePartitionedStaticWindowsWithGroupKeys)
{
auto const col_data = {0, 10, 20, 30, 40, 50, 60, 70, 80, 90};
const cudf::size_type DATA_SIZE{static_cast<cudf::size_type>(col_data.size())};
const std::vector<bool> col_mask(DATA_SIZE, true);
cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> input(
col_data.begin(), col_data.end(), col_mask.begin());
// 2 grouping keys, with effectively 3 groups of at most 4 rows each:
// 1. key_0 {0, 0, 0, ...0}
// 2. key_1 {0, 0, 0, 0, 1, 1, 1, 1, 2, 2}
std::vector<int64_t> key_0_vec(DATA_SIZE, 0);
std::vector<int64_t> key_1_vec;
int i{0};
std::generate_n(
std::back_inserter(key_1_vec), DATA_SIZE, [&i]() { return i++ / 4; }); // Groups of 4.
const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_0(key_0_vec.begin(),
key_0_vec.end());
const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_1(key_1_vec.begin(),
key_1_vec.end());
const cudf::table_view grouping_keys{std::vector<cudf::column_view>{key_0, key_1}};
cudf::size_type preceding_window = 2;
cudf::size_type following_window = 1;
std::vector<cudf::size_type> expected_group_offsets{0, 4, 8, DATA_SIZE};
this->run_test_col_agg(
grouping_keys, input, expected_group_offsets, preceding_window, following_window, 1);
}
TYPED_TEST(GroupedRollingTest, SimplePartitionedStaticWindowWithNoGroupKeys)
{
auto const col_data =
cudf::test::make_type_param_vector<TypeParam>({0, 10, 20, 30, 40, 50, 60, 70, 80, 90});
const cudf::size_type DATA_SIZE{static_cast<cudf::size_type>(col_data.size())};
const std::vector<bool> col_mask(DATA_SIZE, true);
cudf::test::fixed_width_column_wrapper<TypeParam> input(
col_data.begin(), col_data.end(), col_mask.begin());
const cudf::table_view grouping_keys{std::vector<cudf::column_view>{}};
cudf::size_type preceding_window = 2;
cudf::size_type following_window = 1;
std::vector<cudf::size_type> expected_group_offsets{0, DATA_SIZE};
this->run_test_col_agg(
grouping_keys, input, expected_group_offsets, preceding_window, following_window, 1);
}
// all rows are invalid
TYPED_TEST(GroupedRollingTest, AllInvalid)
{
auto const col_data =
cudf::test::make_type_param_vector<TypeParam>({0, 10, 20, 30, 40, 50, 60, 70, 80, 90});
const cudf::size_type DATA_SIZE{static_cast<cudf::size_type>(col_data.size())};
const std::vector<bool> col_mask(DATA_SIZE, false);
cudf::test::fixed_width_column_wrapper<TypeParam> input(
col_data.begin(), col_data.end(), col_mask.begin());
// 2 grouping keys, with effectively 3 groups of at most 4 rows each:
// 1. key_0 {0, 0, 0, ...0}
// 2. key_1 {0, 0, 0, 0, 1, 1, 1, 1, 2, 2}
std::vector<int64_t> key_0_vec(DATA_SIZE, 0);
std::vector<int64_t> key_1_vec;
int i{0};
std::generate_n(
std::back_inserter(key_1_vec), DATA_SIZE, [&i]() { return i++ / 4; }); // Groups of 4.
const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_0(key_0_vec.begin(),
key_0_vec.end());
const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_1(key_1_vec.begin(),
key_1_vec.end());
const cudf::table_view grouping_keys{std::vector<cudf::column_view>{key_0, key_1}};
cudf::size_type preceding_window = 2;
cudf::size_type following_window = 1;
std::vector<cudf::size_type> expected_group_offsets{0, 4, 8, DATA_SIZE};
this->run_test_col_agg(
grouping_keys, input, expected_group_offsets, preceding_window, following_window, 1);
}
// window = following_window = 0
TYPED_TEST(GroupedRollingTest, ZeroWindow)
{
auto const col_data = {0, 10, 20, 30, 40, 50, 60, 70, 80, 90};
const cudf::size_type DATA_SIZE{static_cast<cudf::size_type>(col_data.size())};
const std::vector<bool> col_mask(DATA_SIZE, true);
cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> input(
col_data.begin(), col_data.end(), col_mask.begin());
// 2 grouping keys, with effectively 3 groups of at most 4 rows each:
// 1. key_0 {0, 0, 0, ...0}
// 2. key_1 {0, 0, 0, 0, 1, 1, 1, 1, 2, 2}
std::vector<int64_t> key_0_vec(DATA_SIZE, 0);
std::vector<int64_t> key_1_vec;
int i{0};
std::generate_n(
std::back_inserter(key_1_vec), DATA_SIZE, [&i]() { return i++ / 4; }); // Groups of 4.
const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_0(key_0_vec.begin(),
key_0_vec.end());
const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_1(key_1_vec.begin(),
key_1_vec.end());
const cudf::table_view grouping_keys{std::vector<cudf::column_view>{key_0, key_1}};
cudf::size_type preceding_window = 1;
cudf::size_type following_window = 0;
std::vector<cudf::size_type> expected_group_offsets{0, 4, 8, DATA_SIZE};
this->run_test_col_agg(
grouping_keys, input, expected_group_offsets, preceding_window, following_window, 1);
}
using GroupedRollingTestInts = GroupedRollingTest<int32_t>;
TEST_F(GroupedRollingTestInts, SumLargeWindow)
{
cudf::test::fixed_width_column_wrapper<int32_t, int32_t> input({1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
cudf::size_type preceding_window = 2147483640;
cudf::size_type following_window = 2147483642;
cudf::table_view groupby_keys;
auto result =
cudf::grouped_rolling_window(groupby_keys,
input,
preceding_window,
following_window,
1,
*cudf::make_sum_aggregation<cudf::rolling_aggregation>());
cudf::test::fixed_width_column_wrapper<int64_t, int32_t> expected(
{10, 10, 10, 10, 10, 10, 10, 10, 10, 10});
CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
}
// ------------- non-fixed-width types --------------------
using GroupedRollingTestStrings = GroupedRollingTest<cudf::string_view>;
TEST_F(GroupedRollingTestStrings, StringsUnsupportedOperators)
{
cudf::test::strings_column_wrapper input{{"This", "is", "not", "a", "string", "type"},
{true, true, true, false, true, false}};
const cudf::size_type DATA_SIZE{static_cast<cudf::column_view>(input).size()};
const std::vector<cudf::size_type> key_col_vec(DATA_SIZE, 0);
const cudf::table_view key_cols{
std::vector<cudf::column_view>{cudf::test::fixed_width_column_wrapper<cudf::size_type>(
key_col_vec.begin(), key_col_vec.end())}};
EXPECT_THROW(
cudf::grouped_rolling_window(
key_cols, input, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
cudf::logic_error);
EXPECT_THROW(
cudf::grouped_rolling_window(
key_cols, input, 2, 2, 0, *cudf::make_mean_aggregation<cudf::rolling_aggregation>()),
cudf::logic_error);
}
template <typename T>
class GroupedTimeRangeRollingTest : public cudf::test::BaseFixture {
protected:
// input as column_wrapper
void run_test_col(cudf::table_view const& keys,
cudf::column_view const& timestamp_column,
cudf::order const& timestamp_order,
cudf::column_view const& input,
std::vector<cudf::size_type> const& expected_grouping,
cudf::size_type const& preceding_window_in_days,
cudf::size_type const& following_window_in_days,
cudf::size_type min_periods,
cudf::rolling_aggregation const& op)
{
std::unique_ptr<cudf::column> output;
// wrap windows
EXPECT_NO_THROW(output = cudf::grouped_time_range_rolling_window(keys,
timestamp_column,
timestamp_order,
input,
preceding_window_in_days,
following_window_in_days,
min_periods,
op));
auto reference = create_reference_output(op,
timestamp_column,
timestamp_order,
input,
expected_grouping,
preceding_window_in_days,
following_window_in_days,
min_periods);
CUDF_TEST_EXPECT_COLUMNS_EQUAL(*output, *reference);
}
void run_test_col_agg(cudf::table_view const& keys,
cudf::column_view const& timestamp_column,
cudf::order const& timestamp_order,
cudf::column_view const& input,
std::vector<cudf::size_type> const& expected_grouping,
cudf::size_type preceding_window_in_days,
cudf::size_type following_window_in_days,
cudf::size_type min_periods)
{
// Skip grouping-tests on bool8 keys. sort_helper does not support this.
if (keys.num_columns() > 0 && cudf::is_boolean(keys.column(0).type())) { return; }
// test all supported aggregators
run_test_col(keys,
timestamp_column,
timestamp_order,
input,
expected_grouping,
preceding_window_in_days,
following_window_in_days,
min_periods,
*cudf::make_min_aggregation<cudf::rolling_aggregation>());
run_test_col(keys,
timestamp_column,
timestamp_order,
input,
expected_grouping,
preceding_window_in_days,
following_window_in_days,
min_periods,
*cudf::make_count_aggregation<cudf::rolling_aggregation>());
run_test_col(
keys,
timestamp_column,
timestamp_order,
input,
expected_grouping,
preceding_window_in_days,
following_window_in_days,
min_periods,
*cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE));
run_test_col(keys,
timestamp_column,
timestamp_order,
input,
expected_grouping,
preceding_window_in_days,
following_window_in_days,
min_periods,
*cudf::make_max_aggregation<cudf::rolling_aggregation>());
if (!cudf::is_timestamp(input.type())) {
run_test_col(keys,
timestamp_column,
timestamp_order,
input,
expected_grouping,
preceding_window_in_days,
following_window_in_days,
min_periods,
*cudf::make_sum_aggregation<cudf::rolling_aggregation>());
run_test_col(keys,
timestamp_column,
timestamp_order,
input,
expected_grouping,
preceding_window_in_days,
following_window_in_days,
min_periods,
*cudf::make_mean_aggregation<cudf::rolling_aggregation>());
}
run_test_col(keys,
timestamp_column,
timestamp_order,
input,
expected_grouping,
preceding_window_in_days,
following_window_in_days,
min_periods,
*cudf::make_row_number_aggregation<cudf::rolling_aggregation>());
}
private:
// use SFINAE to only instantiate for supported combinations
// specialization for COUNT_VALID, COUNT_ALL
template <bool include_nulls>
std::unique_ptr<cudf::column> create_count_reference_output(
cudf::column_view const& timestamp_column,
cudf::order const& timestamp_order,
cudf::column_view const& input,
std::vector<cudf::size_type> const& group_offsets,
cudf::size_type const& preceding_window_in_days,
cudf::size_type const& following_window_in_days,
cudf::size_type min_periods)
{
assert(timestamp_column.type().id() == cudf::type_id::TIMESTAMP_DAYS); // Testing with DAYS.
auto timestamp_vec = cudf::test::to_host<int32_t>(timestamp_column).first;
cudf::size_type num_rows = input.size();
thrust::host_vector<cudf::size_type> ref_data(num_rows);
thrust::host_vector<bool> ref_valid(num_rows);
// input data and mask
std::vector<cudf::bitmask_type> in_valid = cudf::test::bitmask_to_host(input);
cudf::bitmask_type* valid_mask = in_valid.data();
for (cudf::size_type i = 0; i < num_rows; i++) {
// load sizes
min_periods = std::max(min_periods, 1); // at least one observation is required
// compute bounds
auto group_end_index = std::upper_bound(group_offsets.begin(), group_offsets.end(), i);
auto group_start_index = group_end_index - 1;
cudf::size_type start_index = i;
cudf::size_type end_index = i;
if (timestamp_order == cudf::order::ASCENDING) {
while ((start_index - 1) >= *group_start_index &&
timestamp_vec[start_index - 1] >= (timestamp_vec[i] - preceding_window_in_days)) {
--start_index;
}
while ((end_index + 1) < *group_end_index &&
timestamp_vec[end_index + 1] <= (timestamp_vec[i] + following_window_in_days)) {
++end_index;
}
++end_index; // One past the last.
} else {
while ((start_index - 1) >= *group_start_index &&
timestamp_vec[start_index - 1] <= (timestamp_vec[i] + preceding_window_in_days)) {
--start_index;
}
while ((end_index + 1) < *group_end_index &&
timestamp_vec[end_index + 1] >= (timestamp_vec[i] - following_window_in_days)) {
++end_index;
}
++end_index; // One past the last.
}
// aggregate
cudf::size_type count = 0;
for (cudf::size_type j = start_index; j < end_index; j++) {
if (include_nulls || !input.nullable() || cudf::bit_is_set(valid_mask, j)) count++;
}
ref_valid[i] = ((end_index - start_index) >= min_periods);
if (ref_valid[i]) ref_data[i] = count;
}
cudf::test::fixed_width_column_wrapper<cudf::size_type> col(
ref_data.begin(), ref_data.end(), ref_valid.begin());
return col.release();
}
std::unique_ptr<cudf::column> create_row_number_reference_output(
cudf::column_view const& timestamp_column,
cudf::order const& timestamp_order,
cudf::column_view const& input,
std::vector<cudf::size_type> const& group_offsets,
cudf::size_type const& preceding_window_in_days,
cudf::size_type const& following_window_in_days,
cudf::size_type min_periods)
{
assert(timestamp_column.type().id() == cudf::type_id::TIMESTAMP_DAYS); // Testing with DAYS.
auto timestamp_vec = cudf::test::to_host<int32_t>(timestamp_column).first;
cudf::size_type num_rows = input.size();
thrust::host_vector<cudf::size_type> ref_data(num_rows);
thrust::host_vector<bool> ref_valid(num_rows);
// input data and mask
std::vector<cudf::bitmask_type> in_valid = cudf::test::bitmask_to_host(input);
for (cudf::size_type i = 0; i < num_rows; i++) {
// load sizes
min_periods = std::max(min_periods, 1); // at least one observation is required
// compute bounds
auto group_end_index = std::upper_bound(group_offsets.begin(), group_offsets.end(), i);
auto group_start_index = group_end_index - 1;
cudf::size_type start_index = i;
cudf::size_type end_index = i;
if (timestamp_order == cudf::order::ASCENDING) {
while ((start_index - 1) >= *group_start_index &&
timestamp_vec[start_index - 1] >= (timestamp_vec[i] - preceding_window_in_days)) {
--start_index;
}
while ((end_index + 1) < *group_end_index &&
timestamp_vec[end_index + 1] <= (timestamp_vec[i] + following_window_in_days)) {
++end_index;
}
++end_index; // One past the last.
} else {
while ((start_index - 1) >= *group_start_index &&
timestamp_vec[start_index - 1] <= (timestamp_vec[i] + preceding_window_in_days)) {
--start_index;
}
while ((end_index + 1) < *group_end_index &&
timestamp_vec[end_index + 1] >= (timestamp_vec[i] - following_window_in_days)) {
++end_index;
}
++end_index; // One past the last.
}
// aggregate
cudf::size_type count{end_index - start_index};
cudf::size_type row_number{i - start_index + 1};
ref_valid[i] = (count >= min_periods);
ref_data[i] = row_number;
}
cudf::test::fixed_width_column_wrapper<cudf::size_type> col(
ref_data.begin(), ref_data.end(), ref_valid.begin());
return col.release();
}
template <typename agg_op,
cudf::aggregation::Kind k,
typename OutputType,
bool is_mean,
std::enable_if_t<is_rolling_supported<T, k>()>* = nullptr>
std::unique_ptr<cudf::column> create_reference_output(
cudf::column_view const& timestamp_column,
cudf::order const& timestamp_order,
cudf::column_view const& input,
std::vector<cudf::size_type> const& group_offsets,
cudf::size_type const& preceding_window_in_days,
cudf::size_type const& following_window_in_days,
cudf::size_type min_periods)
{
assert(timestamp_column.type().id() == cudf::type_id::TIMESTAMP_DAYS); // Testing with DAYS.
auto timestamp_vec = cudf::test::to_host<int32_t>(timestamp_column).first;
cudf::size_type num_rows = input.size();
thrust::host_vector<OutputType> ref_data(num_rows);
thrust::host_vector<bool> ref_valid(num_rows);
// input data and mask
auto [in_col, in_valid] = cudf::test::to_host<T>(input);
cudf::bitmask_type* valid_mask = in_valid.data();
agg_op op;
for (cudf::size_type i = 0; i < num_rows; i++) {
auto val = agg_op::template identity<OutputType>();
// load sizes
min_periods = std::max(min_periods, 1); // at least one observation is required
// compute bounds
auto group_end_index = std::upper_bound(group_offsets.begin(), group_offsets.end(), i);
auto group_start_index = group_end_index - 1;
cudf::size_type start_index = i;
cudf::size_type end_index = i;
if (timestamp_order == cudf::order::ASCENDING) {
while ((start_index - 1) >= *group_start_index &&