/*******************************************************************************
* Copyright 2023 Intel 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.
*******************************************************************************/
#ifndef ONEDAL_DATA_PARALLEL
#define ONEDAL_DATA_PARALLEL
#endif
#include <memory>
#include <numeric>
#include <iostream>
#include <algorithm>
#include "example_util/utils.hpp"
#include "oneapi/dal/table/csr.hpp"
#include "oneapi/dal/table/common.hpp"
#include "oneapi/dal/table/csr_accessor.hpp"
// Generate offsets expecting the table
// to have various amounts of data
// in each row
template <typename Index = std::int64_t>
dal::array<Index> generate_offsets(sycl::queue& queue,
std::int64_t row_count,
std::int64_t column_count) {
const std::int64_t offset_count = row_count + 1l;
Index* const raw_data = new Index[offset_count];
for (std::int64_t row = 0l; row < offset_count; ++row) {
raw_data[row] = static_cast<Index>(row);
}
// Offsets are a cumulative sum of element counts
std::partial_sum(raw_data, raw_data + offset_count, raw_data);
auto on_host = dal::array<Index>(raw_data,
offset_count, //
[](Index* const ptr) -> void {
delete[] ptr;
});
return to_device(queue, on_host);
}
// Generate indices knowing, from the `offsets` array,
// how many elements should be in each row
template <typename Index = std::int64_t>
dal::array<Index> generate_indices(sycl::queue& queue,
std::int64_t column_count,
const dal::array<Index>& offsets_on_device) {
dal::array<Index> offsets = to_host(offsets_on_device);
const std::int64_t offset_count = offsets.get_count();
const std::int64_t row_count = offset_count - 1l;
const std::int64_t element_count = offsets[row_count];
Index* const raw_data = new Index[element_count];
for (std::int64_t row = 0l; row < row_count; ++row) {
const std::int64_t first = offsets[row];
const std::int64_t last = offsets[row + 1l];
const std::int64_t count = last - first;
// Fill the lower right triangular
// part of the table (which is non-trivial)
for (std::int64_t col = 0l; col < count; ++col) {
const std::int64_t column = column_count - col - 1l;
raw_data[first + col] = static_cast<Index>(column);
}
std::sort(raw_data + first, raw_data + last);
}
auto on_host = dal::array<Index>(raw_data,
element_count, //
[](Index* const ptr) -> void {
delete[] ptr;
});
return to_device(queue, on_host);
}
// Generate data for the table in a deterministic way
template <typename Type = float, typename Index = std::int64_t>
dal::array<Type> generate_data(sycl::queue& queue, const dal::array<Index>& offsets) {
const std::int64_t offset_count = offsets.get_count();
const std::int64_t element_count = offsets[offset_count - 1];
Type* const raw_data = new Type[element_count];
for (std::int64_t i = 0l; i < element_count; ++i) {
raw_data[i] = static_cast<Type>(element_count - i);
}
auto on_host = dal::array<Type>(raw_data,
element_count, //
[](Type* const ptr) -> void {
delete[] ptr;
});
return to_device(queue, on_host);
}
void run(sycl::queue& queue) {
constexpr std::int64_t row_count = 4;
constexpr std::int64_t column_count = 10;
// Generate data on a device
auto offsets = generate_offsets(queue, row_count, column_count);
auto indices = generate_indices(queue, column_count, offsets);
auto data = generate_data(queue, offsets);
// Wrap previously generated pieces of data
dal::csr_table test_table = dal::csr_table::wrap(data, //
indices,
offsets,
column_count,
dal::sparse_indexing::zero_based);
// Sanity checks for the table shape
std::cout << "Number of rows in table: " << test_table.get_row_count() << '\n';
std::cout << "Number of columns in table: " << test_table.get_column_count() << '\n';
// Check the type of abstract table
const bool is_csr = test_table.get_kind() == dal::csr_table::kind();
std::cout << "Is CSR table: " << is_csr << '\n';
// Extract CSR slice of data on the device
dal::csr_accessor<const float> accessor{ test_table };
auto [slice_data, slice_indices, slice_offsets] = accessor.pull(queue, { 1, 3 });
// Move data to be readable on CPU
dal::array<float> data_on_host = to_host(slice_data);
std::cout << "Slice of elements (compressed): " << data_on_host << '\n';
// Move indices to be readable on CPU
dal::array<std::int64_t> indices_on_host = to_host(slice_indices);
std::cout << "Slice of indices (compressed): " << indices_on_host << '\n';
// Move offsets to be readable on CPU
dal::array<std::int64_t> offsets_on_host = to_host(slice_offsets);
std::cout << "Slice of offsets: " << offsets_on_host << std::endl;
}
int main(int argc, char** argv) {
// Go through different devices
for (auto d : list_devices()) {
std::cout << "Running on " << d.get_platform().get_info<sycl::info::platform::name>()
<< ", " << d.get_info<sycl::info::device::name>() << "\n"
<< std::endl;
auto q = sycl::queue{ d };
run(q);
}
return 0;
}
