Deduplicate decimal32/decimal64 to decimal128 conversion function (#1…

…6236) Closes #16194 This PR deduplicates the `convert_data_to_decimal128` function from `to_arrow.cu`, `writer_impl.cu` and `to_arrow_device.cu` to a common location. Authors: - Muhammad Haseeb (https://github.com/mhaseeb123) - Vyas Ramasubramani (https://github.com/vyasr) Approvers: - Vukasin Milovanovic (https://github.com/vuule) - Nghia Truong (https://github.com/ttnghia) - Vyas Ramasubramani (https://github.com/vyasr) URL: #16236
rapidsai · Jul 27, 2024 · 24997fd · 24997fd
1 parent 473dec5
commit 24997fd
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diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
@@ -365,6 +365,7 @@ add_library(
   src/interop/dlpack.cpp
   src/interop/from_arrow.cu
   src/interop/arrow_utilities.cpp
+  src/interop/decimal_conversion_utilities.cu
   src/interop/to_arrow.cu
   src/interop/to_arrow_device.cu
   src/interop/to_arrow_host.cu

diff --git a/cpp/src/interop/decimal_conversion_utilities.cu b/cpp/src/interop/decimal_conversion_utilities.cu
@@ -0,0 +1,70 @@
+/*
+ * 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 "decimal_conversion_utilities.cuh"
+
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/detail/utilities/linked_column.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+
+#include <type_traits>
+
+namespace cudf {
+namespace detail {
+
+template <typename DecimalType>
+std::unique_ptr<rmm::device_buffer> convert_decimals_to_decimal128(
+  cudf::column_view const& column, rmm::cuda_stream_view stream, rmm::device_async_resource_ref mr)
+{
+  static_assert(std::is_same_v<DecimalType, int32_t> or std::is_same_v<DecimalType, int64_t>,
+                "Only int32 and int64 decimal types can be converted to decimal128.");
+
+  constexpr size_type BIT_WIDTH_RATIO = sizeof(__int128_t) / sizeof(DecimalType);
+  auto buf = std::make_unique<rmm::device_buffer>(column.size() * sizeof(__int128_t), stream, mr);
+
+  thrust::for_each(rmm::exec_policy_nosync(stream, mr),
+                   thrust::make_counting_iterator(0),
+                   thrust::make_counting_iterator(column.size()),
+                   [in  = column.begin<DecimalType>(),
+                    out = reinterpret_cast<DecimalType*>(buf->data()),
+                    BIT_WIDTH_RATIO] __device__(auto in_idx) {
+                     auto const out_idx = in_idx * BIT_WIDTH_RATIO;
+                     // the lowest order bits are the value, the remainder
+                     // simply matches the sign bit to satisfy the two's
+                     // complement integer representation of negative numbers.
+                     out[out_idx] = in[in_idx];
+#pragma unroll BIT_WIDTH_RATIO - 1
+                     for (auto i = 1; i < BIT_WIDTH_RATIO; ++i) {
+                       out[out_idx + i] = in[in_idx] < 0 ? -1 : 0;
+                     }
+                   });
+
+  return buf;
+}
+
+// Instantiate templates for int32_t and int64_t decimal types
+template std::unique_ptr<rmm::device_buffer> convert_decimals_to_decimal128<int32_t>(
+  cudf::column_view const& column, rmm::cuda_stream_view stream, rmm::device_async_resource_ref mr);
+
+template std::unique_ptr<rmm::device_buffer> convert_decimals_to_decimal128<int64_t>(
+  cudf::column_view const& column, rmm::cuda_stream_view stream, rmm::device_async_resource_ref mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/interop/decimal_conversion_utilities.cuh b/cpp/src/interop/decimal_conversion_utilities.cuh
@@ -0,0 +1,44 @@
+/*
+ * 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.
+ */
+
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/resource_ref.hpp>
+
+#include <type_traits>
+
+namespace cudf::detail {
+
+/**
+ * @brief Convert decimal32 and decimal64 numeric data to decimal128 and return the device vector
+ *
+ * @tparam DecimalType to convert from
+ *
+ * @param column A view of the input columns
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource to use for device memory allocation
+ *
+ * @return A device vector containing the converted decimal128 data
+ */
+template <typename DecimalType>
+std::unique_ptr<rmm::device_buffer> convert_decimals_to_decimal128(
+  cudf::column_view const& input, rmm::cuda_stream_view stream, rmm::device_async_resource_ref mr);
+
+}  // namespace cudf::detail
diff --git a/cpp/src/interop/to_arrow.cu b/cpp/src/interop/to_arrow.cu
@@ -15,6 +15,7 @@
  */
 
 #include "arrow_utilities.hpp"
+#include "decimal_conversion_utilities.cuh"
 #include "detail/arrow_allocator.hpp"
 
 #include <cudf/column/column.hpp>
@@ -158,8 +159,11 @@ std::shared_ptr<arrow::Array> unsupported_decimals_to_arrow(column_view input,
                                                             arrow::MemoryPool* ar_mr,
                                                             rmm::cuda_stream_view stream)
 {
-  auto buf =
-    detail::decimals_to_arrow<DeviceType>(input, stream, rmm::mr::get_current_device_resource());
+  auto buf = detail::convert_decimals_to_decimal128<DeviceType>(
+    input, stream, rmm::mr::get_current_device_resource());
+
+  // Synchronize stream here to ensure the decimal128 buffer is ready.
+  stream.synchronize();
 
   auto const buf_size_in_bytes = buf->size();
   auto data_buffer             = allocate_arrow_buffer(buf_size_in_bytes, ar_mr);

diff --git a/cpp/src/interop/to_arrow_device.cu b/cpp/src/interop/to_arrow_device.cu
@@ -15,6 +15,7 @@
  */
 
 #include "arrow_utilities.hpp"
+#include "decimal_conversion_utilities.cuh"
 
 #include <cudf/column/column.hpp>
 #include <cudf/column/column_view.hpp>
@@ -141,7 +142,9 @@ int construct_decimals(cudf::column_view input,
   nanoarrow::UniqueArray tmp;
   NANOARROW_RETURN_NOT_OK(initialize_array(tmp.get(), NANOARROW_TYPE_DECIMAL128, input));
 
-  auto buf = detail::decimals_to_arrow<DeviceType>(input, stream, mr);
+  auto buf = detail::convert_decimals_to_decimal128<DeviceType>(input, stream, mr);
+  // Synchronize stream here to ensure the decimal128 buffer is ready.
+  stream.synchronize();
   NANOARROW_RETURN_NOT_OK(set_buffer(std::move(buf), fixed_width_data_buffer_idx, tmp.get()));
 
   ArrowArrayMove(tmp.get(), out);

diff --git a/cpp/src/interop/to_arrow_host.cu b/cpp/src/interop/to_arrow_host.cu
@@ -15,6 +15,7 @@
  */
 
 #include "arrow_utilities.hpp"
+#include "decimal_conversion_utilities.cuh"
 
 #include <cudf/column/column_view.hpp>
 #include <cudf/detail/interop.hpp>
@@ -50,41 +51,6 @@
 namespace cudf {
 namespace detail {
 
-template <typename DeviceType>
-std::unique_ptr<rmm::device_buffer> decimals_to_arrow(cudf::column_view input,
-                                                      rmm::cuda_stream_view stream,
-                                                      rmm::device_async_resource_ref mr)
-{
-  constexpr size_type BIT_WIDTH_RATIO = sizeof(__int128_t) / sizeof(DeviceType);
-  auto buf = std::make_unique<rmm::device_buffer>(input.size() * sizeof(__int128_t), stream, mr);
-
-  auto count = thrust::counting_iterator<size_type>(0);
-  thrust::for_each(rmm::exec_policy(stream, mr),
-                   count,
-                   count + input.size(),
-                   [in  = input.begin<DeviceType>(),
-                    out = reinterpret_cast<DeviceType*>(buf->data()),
-                    BIT_WIDTH_RATIO] __device__(auto in_idx) {
-                     auto const out_idx = in_idx * BIT_WIDTH_RATIO;
-                     // the lowest order bits are the value, the remainder
-                     // simply matches the sign bit to satisfy the two's
-                     // complement integer representation of negative numbers.
-                     out[out_idx] = in[in_idx];
-#pragma unroll BIT_WIDTH_RATIO - 1
-                     for (auto i = 1; i < BIT_WIDTH_RATIO; ++i) {
-                       out[out_idx + i] = in[in_idx] < 0 ? -1 : 0;
-                     }
-                   });
-
-  return buf;
-}
-
-template std::unique_ptr<rmm::device_buffer> decimals_to_arrow<int32_t>(
-  cudf::column_view input, rmm::cuda_stream_view stream, rmm::device_async_resource_ref mr);
-
-template std::unique_ptr<rmm::device_buffer> decimals_to_arrow<int64_t>(
-  cudf::column_view input, rmm::cuda_stream_view stream, rmm::device_async_resource_ref mr);
-
 namespace {
 
 struct dispatch_to_arrow_host {
@@ -156,7 +122,9 @@ struct dispatch_to_arrow_host {
     NANOARROW_RETURN_NOT_OK(initialize_array(tmp.get(), NANOARROW_TYPE_DECIMAL128, column));
 
     NANOARROW_RETURN_NOT_OK(populate_validity_bitmap(ArrowArrayValidityBitmap(tmp.get())));
-    auto buf = detail::decimals_to_arrow<DeviceType>(column, stream, mr);
+    auto buf = detail::convert_decimals_to_decimal128<DeviceType>(column, stream, mr);
+    // No need to synchronize stream here as populate_data_buffer uses the same stream to copy data
+    // to host.
     NANOARROW_RETURN_NOT_OK(
       populate_data_buffer(device_span<__int128_t const>(
                              reinterpret_cast<const __int128_t*>(buf->data()), column.size()),

diff --git a/cpp/src/io/parquet/writer_impl.cu b/cpp/src/io/parquet/writer_impl.cu
@@ -22,6 +22,7 @@
 #include "arrow_schema_writer.hpp"
 #include "compact_protocol_reader.hpp"
 #include "compact_protocol_writer.hpp"
+#include "interop/decimal_conversion_utilities.cuh"
 #include "io/comp/nvcomp_adapter.hpp"
 #include "io/parquet/parquet.hpp"
 #include "io/parquet/parquet_gpu.hpp"
@@ -1601,58 +1602,20 @@ size_t column_index_buffer_size(EncColumnChunk* ck,
   return ck->ck_stat_size * num_pages + column_index_truncate_length + padding + size_struct_size;
 }
 
-/**
- * @brief Convert decimal32 and decimal64 data to decimal128 and return the device vector
- *
- * @tparam DecimalType to convert from
- *
- * @param column A view of the input columns
- * @param stream CUDA stream used for device memory operations and kernel launches
- *
- * @return A device vector containing the converted decimal128 data
- */
-template <typename DecimalType>
-rmm::device_uvector<__int128_t> convert_data_to_decimal128(column_view const& column,
-                                                           rmm::cuda_stream_view stream)
-{
-  size_type constexpr BIT_WIDTH_RATIO = sizeof(__int128_t) / sizeof(DecimalType);
-
-  rmm::device_uvector<__int128_t> d128_buffer(column.size(), stream);
-
-  thrust::for_each(rmm::exec_policy_nosync(stream),
-                   thrust::make_counting_iterator(0),
-                   thrust::make_counting_iterator(column.size()),
-                   [in  = column.begin<DecimalType>(),
-                    out = reinterpret_cast<DecimalType*>(d128_buffer.data()),
-                    BIT_WIDTH_RATIO] __device__(auto in_idx) {
-                     auto const out_idx = in_idx * BIT_WIDTH_RATIO;
-                     // The lowest order bits are the value, the remainder
-                     // simply matches the sign bit to satisfy the two's
-                     // complement integer representation of negative numbers.
-                     out[out_idx] = in[in_idx];
-#pragma unroll BIT_WIDTH_RATIO - 1
-                     for (auto i = 1; i < BIT_WIDTH_RATIO; ++i) {
-                       out[out_idx + i] = in[in_idx] < 0 ? -1 : 0;
-                     }
-                   });
-
-  return d128_buffer;
-}
-
 /**
  * @brief Function to convert decimal32 and decimal64 columns to decimal128 data,
  *        update the input table metadata, and return a new vector of column views.
  *
  * @param[in,out] table_meta The table metadata
- * @param[in,out] d128_vectors Vector containing the computed decimal128 data buffers.
+ * @param[in,out] d128_buffers Buffers containing the converted decimal128 data.
  * @param input The input table
  * @param stream CUDA stream used for device memory operations and kernel launches
  *
  * @return A device vector containing the converted decimal128 data
  */
 std::vector<column_view> convert_decimal_columns_and_metadata(
   table_input_metadata& table_meta,
-  std::vector<rmm::device_uvector<__int128_t>>& d128_vectors,
+  std::vector<std::unique_ptr<rmm::device_buffer>>& d128_buffers,
   table_view const& table,
   rmm::cuda_stream_view stream)
 {
@@ -1673,28 +1636,30 @@ std::vector<column_view> convert_decimal_columns_and_metadata(
     switch (column.type().id()) {
       case type_id::DECIMAL32:
         // Convert data to decimal128 type
-        d128_vectors.emplace_back(convert_data_to_decimal128<int32_t>(column, stream));
+        d128_buffers.emplace_back(cudf::detail::convert_decimals_to_decimal128<int32_t>(
+          column, stream, rmm::mr::get_current_device_resource()));
         // Update metadata
         metadata.set_decimal_precision(MAX_DECIMAL32_PRECISION);
         metadata.set_type_length(size_of(data_type{type_id::DECIMAL128, column.type().scale()}));
         // Create a new column view from the d128 data vector
         return {data_type{type_id::DECIMAL128, column.type().scale()},
                 column.size(),
-                d128_vectors.back().data(),
+                d128_buffers.back()->data(),
                 column.null_mask(),
                 column.null_count(),
                 column.offset(),
                 converted_children};
       case type_id::DECIMAL64:
         // Convert data to decimal128 type
-        d128_vectors.emplace_back(convert_data_to_decimal128<int64_t>(column, stream));
+        d128_buffers.emplace_back(cudf::detail::convert_decimals_to_decimal128<int64_t>(
+          column, stream, rmm::mr::get_current_device_resource()));
         // Update metadata
         metadata.set_decimal_precision(MAX_DECIMAL64_PRECISION);
         metadata.set_type_length(size_of(data_type{type_id::DECIMAL128, column.type().scale()}));
         // Create a new column view from the d128 data vector
         return {data_type{type_id::DECIMAL128, column.type().scale()},
                 column.size(),
-                d128_vectors.back().data(),
+                d128_buffers.back()->data(),
                 column.null_mask(),
                 column.null_count(),
                 column.offset(),
@@ -1722,6 +1687,9 @@ std::vector<column_view> convert_decimal_columns_and_metadata(
     std::back_inserter(converted_column_views),
     [&](auto elem) { return convert_column(thrust::get<0>(elem), thrust::get<1>(elem)); });
 
+  // Synchronize stream here to ensure all decimal128 buffers are ready.
+  stream.synchronize();
+
   return converted_column_views;
 }
 
@@ -1780,13 +1748,13 @@ auto convert_table_to_parquet_data(table_input_metadata& table_meta,
                                    rmm::cuda_stream_view stream)
 {
   // Container to store decimal128 converted data if needed
-  std::vector<rmm::device_uvector<__int128_t>> d128_vectors;
+  std::vector<std::unique_ptr<rmm::device_buffer>> d128_buffers;
 
   // Convert decimal32/decimal64 data to decimal128 if writing arrow schema
   // and initialize LinkedColVector
   auto vec = table_to_linked_columns(
     (write_arrow_schema)
-      ? table_view({convert_decimal_columns_and_metadata(table_meta, d128_vectors, input, stream)})
+      ? table_view({convert_decimal_columns_and_metadata(table_meta, d128_buffers, input, stream)})
       : input);
 
   auto schema_tree = construct_parquet_schema_tree(