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Murmur3 hash kernel cleanup #10143

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merged 5 commits into from
Feb 7, 2022
Merged

Murmur3 hash kernel cleanup #10143

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1376774
Merge kernels into compute_bytes and cleanup kernel code
rwlee Jan 27, 2022
db648d7
formatting and code cleanup
rwlee Jan 28, 2022
291ccb9
more formatting changes
rwlee Jan 28, 2022
5eaa8e6
Apply suggestions from code review
bdice Feb 2, 2022
e5e4914
Merge remote-tracking branch 'upstream/branch-22.04' into rwlee/hash_…
bdice Feb 2, 2022
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232 changes: 90 additions & 142 deletions cpp/include/cudf/detail/utilities/hash_functions.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -37,8 +37,8 @@ namespace detail {
template <typename T>
T __device__ inline normalize_nans_and_zeros(T const& key)
{
if constexpr (is_floating_point<T>()) {
if (isnan(key)) {
if constexpr (cudf::is_floating_point<T>()) {
if (std::isnan(key)) {
return std::numeric_limits<T>::quiet_NaN();
} else if (key == T{0.0}) {
return T{0.0};
Expand Down Expand Up @@ -84,8 +84,7 @@ void __device__ inline uint32ToLowercaseHexString(uint32_t num, char* destinatio
// non-native version will be less than optimal.
template <typename Key>
struct MurmurHash3_32 {
using argument_type = Key;
using result_type = hash_value_type;
using result_type = hash_value_type;

MurmurHash3_32() = default;
constexpr MurmurHash3_32(uint32_t seed) : m_seed(seed) {}
Expand All @@ -105,6 +104,15 @@ struct MurmurHash3_32 {
return h;
}

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@bdice bdice Jan 28, 2022 •
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Note to self for a future PR: Do we need MurmurHash3_32 to be a templated class? Currently the class takes a template parameter Key and has an operator()(Key key) with no template parameters which calls a templated compute(T key). However, the way it's called in row_operators.cuh seems to indicate that we could instead have a plain (non-template) class with a templated operator(). That's the way we typically do type dispatching, and it's reversed here for no clear reason. The calling code uses a type dispatch on element_hasher_with_seed.

(This would probably affect performance and/or compile time but I don't know if it would be better or worse.)

[[nodiscard]] __device__ inline uint32_t getblock32(std::byte const* data,
cudf::size_type offset) const
{
// Read a 4-byte value from the data pointer as individual bytes for safe
// unaligned access (very likely for string types).
auto const q = reinterpret_cast<uint8_t const*>(data + offset);
return q[0] | (q[1] << 8) | (q[2] << 16) | (q[3] << 24);
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}

/* Copyright 2005-2014 Daniel James.
*
* Use, modification and distribution is subject to the Boost Software
Expand All @@ -122,7 +130,7 @@ struct MurmurHash3_32 {
*
* @returns A hash value that intelligently combines the lhs and rhs hash values
*/
__device__ inline result_type hash_combine(result_type lhs, result_type rhs)
[[nodiscard]] __device__ inline result_type hash_combine(result_type lhs, result_type rhs)
{
result_type combined{lhs};

Expand All @@ -131,60 +139,69 @@ struct MurmurHash3_32 {
return combined;
}

result_type __device__ inline operator()(Key const& key) const { return compute(key); }
// TODO Do we need this operator() and/or compute? Probably not both.
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@bdice bdice Feb 2, 2022 •
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Note to self for a future PR: I would try removing the compute method and move its definition to operator(). I think we might be able to safely remove the template <typename T> on compute(T). The operator() template parameter T has to match the class template parameter Key, from what I can see, and may be redundant. Any exceptions to this would probably be solved by removing the class template parameter Key and switching to just an operator() template parameter.

[[nodiscard]] result_type __device__ inline operator()(Key const& key) const
{
return compute(key);
}

// compute wrapper for floating point types
template <typename T, std::enable_if_t<std::is_floating_point<T>::value>* = nullptr>
hash_value_type __device__ inline compute_floating_point(T const& key) const
{
if (key == T{0.0}) {
return compute(T{0.0});
} else if (isnan(key)) {
} else if (std::isnan(key)) {
T nan = std::numeric_limits<T>::quiet_NaN();
return compute(nan);
} else {
return compute(key);
}
}

template <typename TKey>
result_type __device__ inline compute(TKey const& key) const
template <typename T>
result_type __device__ inline compute(T const& key) const
{
constexpr int len = sizeof(argument_type);
uint8_t const* const data = reinterpret_cast<uint8_t const*>(&key);
constexpr int nblocks = len / 4;

uint32_t h1 = m_seed;
constexpr uint32_t c1 = 0xcc9e2d51;
constexpr uint32_t c2 = 0x1b873593;
//----------
// body
uint32_t const* const blocks = reinterpret_cast<uint32_t const*>(data + nblocks * 4);
for (int i = -nblocks; i; i++) {
uint32_t k1 = blocks[i]; // getblock32(blocks,i);
return compute_bytes(reinterpret_cast<std::byte const*>(&key), sizeof(T));
}

result_type __device__ compute_bytes(std::byte const* data, cudf::size_type const len) const
{
constexpr cudf::size_type BLOCK_SIZE = 4;
cudf::size_type const nblocks = len / BLOCK_SIZE;
cudf::size_type const tail_offset = nblocks * BLOCK_SIZE;
result_type h1 = m_seed;
constexpr uint32_t c1 = 0xcc9e2d51;
constexpr uint32_t c2 = 0x1b873593;
constexpr uint32_t c3 = 0xe6546b64;
constexpr uint32_t rot_c1 = 15;
constexpr uint32_t rot_c2 = 13;

// Process all four-byte chunks.
for (cudf::size_type i = 0; i < nblocks; i++) {
uint32_t k1 = getblock32(data, i * BLOCK_SIZE);
k1 *= c1;
k1 = rotl32(k1, 15);
k1 = rotl32(k1, rot_c1);
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@bdice bdice Jan 27, 2022 •
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Future PR: We might define common functions and magic values between MurmurHash3_32 and SparkMurmurHash3_32 like rotl32 and fmix32 and getblock32 in a common base class, and only override the Spark-specific bits in a derived class. CRTP might be an even better choice, like I did for the SHA-family functions (draft #9215) - just needs a bit of analysis to decide which way to go.

k1 *= c2;
h1 ^= k1;
h1 = rotl32(h1, 13);
h1 = h1 * 5 + 0xe6546b64;
h1 = rotl32(h1, rot_c2);
h1 = h1 * 5 + c3;
}
//----------
// tail
uint8_t const* tail = reinterpret_cast<uint8_t const*>(data + nblocks * 4);
uint32_t k1 = 0;
switch (len & 3) {
case 3: k1 ^= tail[2] << 16;
case 2: k1 ^= tail[1] << 8;

// Process remaining bytes that do not fill a four-byte chunk.
uint32_t k1 = 0;
switch (len % 4) {
case 3: k1 ^= std::to_integer<uint8_t>(data[tail_offset + 2]) << 16;
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case 2: k1 ^= std::to_integer<uint8_t>(data[tail_offset + 1]) << 8;
case 1:
k1 ^= tail[0];
k1 ^= std::to_integer<uint8_t>(data[tail_offset]);
k1 *= c1;
k1 = rotl32(k1, 15);
k1 = rotl32(k1, rot_c1);
k1 *= c2;
h1 ^= k1;
};
//----------
// finalization

// Finalize hash.
h1 ^= len;
h1 = fmix32(h1);
return h1;
Expand All @@ -207,49 +224,9 @@ template <>
hash_value_type __device__ inline MurmurHash3_32<cudf::string_view>::operator()(
cudf::string_view const& key) const
{
auto const len = key.size_bytes();
uint8_t const* data = reinterpret_cast<uint8_t const*>(key.data());
int const nblocks = len / 4;
result_type h1 = m_seed;
constexpr uint32_t c1 = 0xcc9e2d51;
constexpr uint32_t c2 = 0x1b873593;
auto getblock32 = [] __device__(uint32_t const* p, int i) -> uint32_t {
// Individual byte reads for unaligned accesses (very likely)
auto q = (uint8_t const*)(p + i);
return q[0] | (q[1] << 8) | (q[2] << 16) | (q[3] << 24);
};

//----------
// body
uint32_t const* const blocks = reinterpret_cast<uint32_t const*>(data + nblocks * 4);
for (int i = -nblocks; i; i++) {
uint32_t k1 = getblock32(blocks, i);
k1 *= c1;
k1 = rotl32(k1, 15);
k1 *= c2;
h1 ^= k1;
h1 = rotl32(h1, 13);
h1 = h1 * 5 + 0xe6546b64;
}
//----------
// tail
uint8_t const* tail = reinterpret_cast<uint8_t const*>(data + nblocks * 4);
uint32_t k1 = 0;
switch (len & 3) {
case 3: k1 ^= tail[2] << 16;
case 2: k1 ^= tail[1] << 8;
case 1:
k1 ^= tail[0];
k1 *= c1;
k1 = rotl32(k1, 15);
k1 *= c2;
h1 ^= k1;
};
//----------
// finalization
h1 ^= len;
h1 = fmix32(h1);
return h1;
auto const data = reinterpret_cast<std::byte const*>(key.data());
auto const len = key.size_bytes();
return this->compute_bytes(data, len);
}

template <>
Expand Down Expand Up @@ -303,8 +280,7 @@ hash_value_type __device__ inline MurmurHash3_32<cudf::struct_view>::operator()(

template <typename Key>
struct SparkMurmurHash3_32 {
using argument_type = Key;
using result_type = hash_value_type;
using result_type = hash_value_type;

SparkMurmurHash3_32() = default;
constexpr SparkMurmurHash3_32(uint32_t seed) : m_seed(seed) {}
Expand All @@ -330,58 +306,67 @@ struct SparkMurmurHash3_32 {
template <typename T, std::enable_if_t<std::is_floating_point<T>::value>* = nullptr>
hash_value_type __device__ inline compute_floating_point(T const& key) const
{
if (isnan(key)) {
if (std::isnan(key)) {
T nan = std::numeric_limits<T>::quiet_NaN();
return compute(nan);
} else {
return compute(key);
}
}

template <typename TKey>
result_type __device__ inline compute(TKey const& key) const
template <typename T>
result_type __device__ inline compute(T const& key) const
{
return compute_bytes(reinterpret_cast<std::byte const*>(&key), sizeof(T));
}

[[nodiscard]] __device__ inline uint32_t getblock32(std::byte const* data,
cudf::size_type offset) const
{
return compute_bytes(reinterpret_cast<std::byte const*>(&key), sizeof(TKey));
// Individual byte reads for unaligned accesses (very likely for strings)
auto q = reinterpret_cast<uint8_t const*>(data + offset);
return q[0] | (q[1] << 8) | (q[2] << 16) | (q[3] << 24);
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}

result_type __device__ compute_bytes(std::byte const* const data, cudf::size_type const len) const
result_type __device__ compute_bytes(std::byte const* data, cudf::size_type const len) const
{
constexpr cudf::size_type block_size = sizeof(uint32_t) / sizeof(std::byte);
cudf::size_type const nblocks = len / block_size;
uint32_t h1 = m_seed;
constexpr cudf::size_type BLOCK_SIZE = 4;
cudf::size_type const nblocks = len / BLOCK_SIZE;
result_type h1 = m_seed;
constexpr uint32_t c1 = 0xcc9e2d51;
constexpr uint32_t c2 = 0x1b873593;
constexpr uint32_t c3 = 0xe6546b64;
constexpr uint32_t rot_c1 = 15;
constexpr uint32_t rot_c2 = 13;

//----------
// Process all four-byte chunks
uint32_t const* const blocks = reinterpret_cast<uint32_t const*>(data);
// Process all four-byte chunks.
for (cudf::size_type i = 0; i < nblocks; i++) {
uint32_t k1 = blocks[i];
uint32_t k1 = getblock32(data, i * BLOCK_SIZE);
k1 *= c1;
k1 = rotl32(k1, 15);
k1 = rotl32(k1, rot_c1);
k1 *= c2;
h1 ^= k1;
h1 = rotl32(h1, 13);
h1 = h1 * 5 + 0xe6546b64;
h1 = rotl32(h1, rot_c2);
h1 = h1 * 5 + c3;
}
//----------

// Process remaining bytes that do not fill a four-byte chunk using Spark's approach
// (does not conform to normal MurmurHash3)
// (does not conform to normal MurmurHash3).
for (cudf::size_type i = nblocks * 4; i < len; i++) {
// We require a two-step cast to get the k1 value from the byte. First,
// we must cast to a signed int8_t. Then, the sign bit is preserved when
// casting to uint32_t under 2's complement. Java preserves the
// signedness when casting byte-to-int, but C++ does not.
uint32_t k1 = static_cast<uint32_t>(std::to_integer<int8_t>(data[i]));
k1 *= c1;
k1 = rotl32(k1, 15);
k1 = rotl32(k1, rot_c1);
k1 *= c2;
h1 ^= k1;
h1 = rotl32(h1, 13);
h1 = h1 * 5 + 0xe6546b64;
h1 = rotl32(h1, rot_c2);
h1 = h1 * 5 + c3;
}
//----------
// finalization

// Finalize hash.
h1 ^= len;
h1 = fmix32(h1);
return h1;
Expand Down Expand Up @@ -501,46 +486,9 @@ template <>
hash_value_type __device__ inline SparkMurmurHash3_32<cudf::string_view>::operator()(
cudf::string_view const& key) const
{
auto const len = key.size_bytes();
int8_t const* data = reinterpret_cast<int8_t const*>(key.data());
int const nblocks = len / 4;
result_type h1 = m_seed;
constexpr uint32_t c1 = 0xcc9e2d51;
constexpr uint32_t c2 = 0x1b873593;
auto getblock32 = [] __device__(uint32_t const* p, int i) -> uint32_t {
// Individual byte reads for unaligned accesses (very likely)
auto q = (const uint8_t*)(p + i);
return q[0] | (q[1] << 8) | (q[2] << 16) | (q[3] << 24);
};

//----------
// body
uint32_t const* const blocks = reinterpret_cast<uint32_t const*>(data + nblocks * 4);
for (int i = -nblocks; i; i++) {
uint32_t k1 = getblock32(blocks, i);
k1 *= c1;
k1 = rotl32(k1, 15);
k1 *= c2;
h1 ^= k1;
h1 = rotl32(h1, 13);
h1 = h1 * 5 + 0xe6546b64;
}
//----------
// Spark's byte by byte tail processing
for (int i = nblocks * 4; i < len; i++) {
uint32_t k1 = data[i];
k1 *= c1;
k1 = rotl32(k1, 15);
k1 *= c2;
h1 ^= k1;
h1 = rotl32(h1, 13);
h1 = h1 * 5 + 0xe6546b64;
}
//----------
// finalization
h1 ^= len;
h1 = fmix32(h1);
return h1;
auto const data = reinterpret_cast<std::byte const*>(key.data());
auto const len = key.size_bytes();
return this->compute_bytes(data, len);
}

template <>
Expand Down