Merge branch 'branch-0.19' of https://github.com/rapidsai/cudf into f…

…ix-groupby-tests-sort

cpp/include/cudf/detail/gather.cuh

@@ -142,7 +142,11 @@ void gather_helper(InputItr source_itr,
 // Error case when no other overload or specialization is available
 template <typename Element, typename Enable = void>
 struct column_gatherer_impl {
-  std::unique_ptr<column> operator()(...) { CUDF_FAIL("Unsupported type in gather."); }
+  template <typename... Args>
+  std::unique_ptr<column> operator()(Args&&...)
+  {
+    CUDF_FAIL("Unsupported type in gather.");
+  }
 };
 
 /**

cpp/include/cudf/detail/scatter.cuh

@@ -81,7 +81,11 @@ auto scatter_to_gather(MapIterator scatter_map_begin,
 
 template <typename Element, typename Enable = void>
 struct column_scatterer_impl {
-  std::unique_ptr<column> operator()(...) const { CUDF_FAIL("Unsupported type for scatter."); }
+  template <typename... Args>
+  std::unique_ptr<column> operator()(Args&&...) const
+  {
+    CUDF_FAIL("Unsupported type for scatter.");
+  }
 };
 
 template <typename Element>

cpp/include/cudf/detail/utilities/hash_functions.cuh

@@ -20,6 +20,7 @@
 #include <cudf/detail/utilities/assert.cuh>
 #include <cudf/fixed_point/fixed_point.hpp>
 #include <cudf/strings/string_view.cuh>
+#include <cudf/types.hpp>
 #include <hash/hash_constants.hpp>
 
 using hash_value_type = uint32_t;
@@ -231,6 +232,9 @@ MD5ListHasher::operator()<string_view>(column_device_view data_col,
 }
 
 struct MD5Hash {
+  MD5Hash() = default;
+  constexpr MD5Hash(uint32_t seed) : m_seed(seed) {}
+
   void __device__ finalize(md5_intermediate_data* hash_state, char* result_location) const
   {
     auto const full_length = (static_cast<uint64_t>(hash_state->message_length)) << 3;
@@ -302,6 +306,9 @@ struct MD5Hash {
   {
     md5_process(col.element<T>(row_index), hash_state);
   }
+
+ private:
+  uint32_t m_seed{cudf::DEFAULT_HASH_SEED};
 };
 
 template <>
@@ -372,7 +379,7 @@ struct MurmurHash3_32 {
   using result_type   = hash_value_type;
 
   MurmurHash3_32() = default;
-  CUDA_HOST_DEVICE_CALLABLE MurmurHash3_32(uint32_t seed) : m_seed(seed) {}
+  constexpr MurmurHash3_32(uint32_t seed) : m_seed(seed) {}
 
   CUDA_DEVICE_CALLABLE uint32_t rotl32(uint32_t x, int8_t r) const
   {
@@ -469,7 +476,7 @@ struct MurmurHash3_32 {
   }
 
  private:
-  uint32_t m_seed{0};
+  uint32_t m_seed{cudf::DEFAULT_HASH_SEED};
 };
 
 template <>
@@ -564,7 +571,7 @@ struct SparkMurmurHash3_32 {
   using result_type   = hash_value_type;
 
   SparkMurmurHash3_32() = default;
-  CUDA_HOST_DEVICE_CALLABLE SparkMurmurHash3_32(uint32_t seed) : m_seed(seed) {}
+  constexpr SparkMurmurHash3_32(uint32_t seed) : m_seed(seed) {}
 
   CUDA_DEVICE_CALLABLE uint32_t rotl32(uint32_t x, int8_t r) const
   {
@@ -636,7 +643,7 @@ struct SparkMurmurHash3_32 {
   }
 
  private:
-  uint32_t m_seed{0};
+  uint32_t m_seed{cudf::DEFAULT_HASH_SEED};
 };
 
 template <>
@@ -772,6 +779,8 @@ SparkMurmurHash3_32<double>::operator()(double const& key) const
 template <typename Key>
 struct IdentityHash {
   using result_type = hash_value_type;
+  IdentityHash()    = default;
+  constexpr IdentityHash(uint32_t seed) : m_seed(seed) {}
 
   /**
    * @brief  Combines two hash values into a new single hash value. Called
@@ -784,7 +793,7 @@ struct IdentityHash {
    *
    * @returns A hash value that intelligently combines the lhs and rhs hash values
    */
-  CUDA_HOST_DEVICE_CALLABLE result_type hash_combine(result_type lhs, result_type rhs) const
+  constexpr result_type hash_combine(result_type lhs, result_type rhs) const
   {
     result_type combined{lhs};
 
@@ -794,19 +803,22 @@ struct IdentityHash {
   }
 
   template <typename return_type = result_type>
-  CUDA_HOST_DEVICE_CALLABLE std::enable_if_t<!std::is_arithmetic<Key>::value, return_type>
-  operator()(Key const& key) const
+  constexpr std::enable_if_t<!std::is_arithmetic<Key>::value, return_type> operator()(
+    Key const& key) const
   {
     cudf_assert(false && "IdentityHash does not support this data type");
     return 0;
   }
 
   template <typename return_type = result_type>
-  CUDA_HOST_DEVICE_CALLABLE std::enable_if_t<std::is_arithmetic<Key>::value, return_type>
-  operator()(Key const& key) const
+  constexpr std::enable_if_t<std::is_arithmetic<Key>::value, return_type> operator()(
+    Key const& key) const
   {
     return static_cast<result_type>(key);
   }
+
+ private:
+  uint32_t m_seed{cudf::DEFAULT_HASH_SEED};
 };
 
 template <typename Key>

cpp/include/cudf/hashing.hpp

@@ -39,7 +39,7 @@ std::unique_ptr<column> hash(
   table_view const& input,
   hash_id hash_function                     = hash_id::HASH_MURMUR3,
   std::vector<uint32_t> const& initial_hash = {},
-  uint32_t seed                             = 0,
+  uint32_t seed                             = DEFAULT_HASH_SEED,
   rmm::mr::device_memory_resource* mr       = rmm::mr::get_current_device_resource());
 
 /** @} */  // end of group

cpp/include/cudf/partitioning.hpp

@@ -83,6 +83,9 @@ std::pair<std::unique_ptr<table>, std::vector<size_type>> partition(
  * @param input The table to partition
  * @param columns_to_hash Indices of input columns to hash
  * @param num_partitions The number of partitions to use
+ * @param hash_function Optional hash id that chooses the hash function to use
+ * @param seed Optional seed value to the hash function
+ * @param stream CUDA stream used for device memory operations and kernel launches
  * @param mr Device memory resource used to allocate the returned table's device memory.
  *
  * @returns An output table and a vector of row offsets to each partition
@@ -92,6 +95,7 @@ std::pair<std::unique_ptr<table>, std::vector<size_type>> hash_partition(
   std::vector<size_type> const& columns_to_hash,
   int num_partitions,
   hash_id hash_function               = hash_id::HASH_MURMUR3,
+  uint32_t seed                       = DEFAULT_HASH_SEED,
   rmm::cuda_stream_view stream        = rmm::cuda_stream_default,
   rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
 

cpp/include/cudf/table/row_operators.cuh

@@ -428,6 +428,7 @@ template <template <typename> class hash_function, bool has_nulls = true>
 class element_hasher_with_seed {
  public:
   element_hasher_with_seed() = default;
+  __device__ element_hasher_with_seed(uint32_t seed) : _seed{seed} {}
   __device__ element_hasher_with_seed(uint32_t seed, hash_value_type null_hash)
     : _seed{seed}, _null_hash(null_hash)
   {
@@ -448,7 +449,7 @@ class element_hasher_with_seed {
   }
 
  private:
-  uint32_t _seed{0};
+  uint32_t _seed{DEFAULT_HASH_SEED};
   hash_value_type _null_hash{std::numeric_limits<hash_value_type>::max()};
 };
 
@@ -463,13 +464,22 @@ class row_hasher {
  public:
   row_hasher() = delete;
   row_hasher(table_device_view t) : _table{t} {}
+  row_hasher(table_device_view t, uint32_t seed) : _table{t}, _seed(seed) {}
 
   __device__ auto operator()(size_type row_index) const
   {
     auto hash_combiner = [](hash_value_type lhs, hash_value_type rhs) {
       return hash_function<hash_value_type>{}.hash_combine(lhs, rhs);
     };
 
+    // Hash the first column w/ the seed
+    auto const initial_hash =
+      hash_combiner(hash_value_type{0},
+                    type_dispatcher(_table.column(0).type(),
+                                    element_hasher_with_seed<hash_function, has_nulls>{_seed},
+                                    _table.column(0),
+                                    row_index));
+
     // Hashes an element in a column
     auto hasher = [=](size_type column_index) {
       return cudf::type_dispatcher(_table.column(column_index).type(),
@@ -479,16 +489,19 @@ class row_hasher {
     };
 
     // Hash each element and combine all the hash values together
-    return thrust::transform_reduce(thrust::seq,
-                                    thrust::make_counting_iterator(0),
-                                    thrust::make_counting_iterator(_table.num_columns()),
-                                    hasher,
-                                    hash_value_type{0},
-                                    hash_combiner);
+    return thrust::transform_reduce(
+      thrust::seq,
+      // note that this starts at 1 and not 0 now since we already hashed the first column
+      thrust::make_counting_iterator(1),
+      thrust::make_counting_iterator(_table.num_columns()),
+      hasher,
+      initial_hash,
+      hash_combiner);
   }
 
  private:
   table_device_view _table;
+  uint32_t _seed{DEFAULT_HASH_SEED};
 };
 
 /**

cpp/include/cudf/types.hpp

@@ -339,5 +339,10 @@ enum class hash_id {
   HASH_SPARK_MURMUR3    ///< Spark Murmur3 hash function
 };
 
+/**
+ * @brief The default seed value for hash functions
+ */
+static constexpr uint32_t DEFAULT_HASH_SEED = 0;
+
 /** @} */
 }  // namespace cudf

cpp/src/copying/copy.cu

@@ -31,7 +31,11 @@ namespace {
 
 template <typename T, typename Enable = void>
 struct copy_if_else_functor_impl {
-  std::unique_ptr<column> operator()(...) { CUDF_FAIL("Unsupported type for copy_if_else."); }
+  template <typename... Args>
+  std::unique_ptr<column> operator()(Args&&...)
+  {
+    CUDF_FAIL("Unsupported type for copy_if_else.");
+  }
 };
 
 template <typename T>

cpp/src/partitioning/partitioning.cu

@@ -448,6 +448,7 @@ std::pair<std::unique_ptr<table>, std::vector<size_type>> hash_partition_table(
   table_view const& input,
   table_view const& table_to_hash,
   size_type num_partitions,
+  uint32_t seed,
   rmm::cuda_stream_view stream,
   rmm::mr::device_memory_resource* mr)
 {
@@ -481,7 +482,7 @@ std::pair<std::unique_ptr<table>, std::vector<size_type>> hash_partition_table(
   auto row_partition_offset = rmm::device_vector<size_type>(num_rows);
 
   auto const device_input = table_device_view::create(table_to_hash, stream);
-  auto const hasher       = row_hasher<hash_function, hash_has_nulls>(*device_input);
+  auto const hasher       = row_hasher<hash_function, hash_has_nulls>(*device_input, seed);
 
   // If the number of partitions is a power of two, we can compute the partition
   // number of each row more efficiently with bitwise operations
@@ -725,6 +726,7 @@ std::pair<std::unique_ptr<table>, std::vector<size_type>> hash_partition(
   table_view const& input,
   std::vector<size_type> const& columns_to_hash,
   int num_partitions,
+  uint32_t seed,
   rmm::cuda_stream_view stream,
   rmm::mr::device_memory_resource* mr)
 {
@@ -737,10 +739,10 @@ std::pair<std::unique_ptr<table>, std::vector<size_type>> hash_partition(
 
   if (has_nulls(table_to_hash)) {
     return hash_partition_table<hash_function, true>(
-      input, table_to_hash, num_partitions, stream, mr);
+      input, table_to_hash, num_partitions, seed, stream, mr);
   } else {
     return hash_partition_table<hash_function, false>(
-      input, table_to_hash, num_partitions, stream, mr);
+      input, table_to_hash, num_partitions, seed, stream, mr);
   }
 }
 }  // namespace local
@@ -771,6 +773,7 @@ std::pair<std::unique_ptr<table>, std::vector<size_type>> hash_partition(
   std::vector<size_type> const& columns_to_hash,
   int num_partitions,
   hash_id hash_function,
+  uint32_t seed,
   rmm::cuda_stream_view stream,
   rmm::mr::device_memory_resource* mr)
 {
@@ -783,10 +786,10 @@ std::pair<std::unique_ptr<table>, std::vector<size_type>> hash_partition(
           CUDF_FAIL("IdentityHash does not support this data type");
       }
       return detail::local::hash_partition<IdentityHash>(
-        input, columns_to_hash, num_partitions, stream, mr);
+        input, columns_to_hash, num_partitions, seed, stream, mr);
     case (hash_id::HASH_MURMUR3):
       return detail::local::hash_partition<MurmurHash3_32>(
-        input, columns_to_hash, num_partitions, stream, mr);
+        input, columns_to_hash, num_partitions, seed, stream, mr);
     default: CUDF_FAIL("Unsupported hash function in hash_partition");
   }
 }

cpp/src/replace/nulls.cu

@@ -424,7 +424,7 @@ std::unique_ptr<cudf::column> replace_nulls(cudf::column_view const& input,
   CUDF_EXPECTS(replacement.size() == input.size(), "Column size mismatch");
 
   if (input.is_empty()) { return cudf::empty_like(input); }
-  if (!input.has_nulls()) { return std::make_unique<cudf::column>(input); }
+  if (!input.has_nulls()) { return std::make_unique<cudf::column>(input, stream, mr); }
 
   return cudf::type_dispatcher<dispatch_storage_type>(
     input.type(), replace_nulls_column_kernel_forwarder{}, input, replacement, stream, mr);

cpp/tests/partitioning/hash_partition_test.cpp

@@ -214,6 +214,34 @@ TEST_F(HashPartition, UnsupportedHashFunction)
     cudf::logic_error);
 }
 
+TEST_F(HashPartition, CustomSeedValue)
+{
+  fixed_width_column_wrapper<float> floats({1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f});
+  fixed_width_column_wrapper<int16_t> integers({1, 2, 3, 4, 5, 6, 7, 8});
+  strings_column_wrapper strings({"a", "bb", "ccc", "d", "ee", "fff", "gg", "h"});
+  auto input = cudf::table_view({floats, integers, strings});
+
+  auto columns_to_hash = std::vector<cudf::size_type>({0, 2});
+
+  cudf::size_type const num_partitions = 3;
+  std::unique_ptr<cudf::table> output1, output2;
+  std::vector<cudf::size_type> offsets1, offsets2;
+  std::tie(output1, offsets1) = cudf::hash_partition(
+    input, columns_to_hash, num_partitions, cudf::hash_id::HASH_MURMUR3, 12345);
+  std::tie(output2, offsets2) = cudf::hash_partition(
+    input, columns_to_hash, num_partitions, cudf::hash_id::HASH_MURMUR3, 12345);
+
+  // Expect output to have size num_partitions
+  EXPECT_EQ(static_cast<size_t>(num_partitions), offsets1.size());
+  EXPECT_EQ(offsets1.size(), offsets2.size());
+
+  // Expect output to have same shape as input
+  CUDF_TEST_EXPECT_TABLE_PROPERTIES_EQUAL(input, output1->view());
+
+  // Expect deterministic result from hashing the same input
+  CUDF_TEST_EXPECT_TABLES_EQUAL(output1->view(), output2->view());
+}
+
 template <typename T>
 class HashPartitionFixedWidth : public cudf::test::BaseFixture {
 };

docs/cudf/source/basics.rst

@@ -34,6 +34,8 @@ The following table lists all of cudf types. For methods requiring dtype argumen
 +------------------------+------------------+-------------------------------------------------------------------------------------+---------------------------------------------+
 | Boolean                |                  | np.bool_                                                                            | ``'bool'``                                  |
 +------------------------+------------------+-------------------------------------------------------------------------------------+---------------------------------------------+
+| Decimal                | Decimal64Dtype   | (none)                                                                              | (none)                                      |
++------------------------+------------------+-------------------------------------------------------------------------------------+---------------------------------------------+
 
 **Note: All dtypes above are Nullable**