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Fix Strict Aliasing Violation

Browse Source 
Even though this failed with non regression bounds on fishtest, merging
it as is to improve the current situation.

Failed Non Regression:
https://tests.stockfishchess.org/tests/view/6988814db0f3ca5200aafb50
LLR: -2.94 (-2.94,2.94) <-1.75,0.25>
Total: 185792 W: 48109 L: 48565 D: 89118
Ptnml(0-2): 553, 21228, 49811, 20730, 574

Previously we had code similar to this
```
alignas(64) std::uint8_t foo[1024];
const auto* int_ptr = reinterpret_cast<const std::int32_t*>(foo);
```

Which is a strict aliasing violation once we dereference int_ptr, which
invokes UB and causes newer gcc version to emit incorrect code.

fixes https://github.com/official-stockfish/Stockfish/issues/6598
fixes https://github.com/official-stockfish/Stockfish/issues/4932

This violation was introduced in PR #3287 and merged in commit 23c385e.
Due to aggressive compiler optimizations, users may need to include
CXXFLAGS=-fno-strict-aliasing as a build argument to ensure a correctly
functioning binary.

Also thanks to @anematode for writing parts of this and realizing this
strict aliasing violation.

closes https://github.com/official-stockfish/Stockfish/pull/6599

Bench: 2668754

Co-Authored-By: Timothy Herchen <timothy.herchen@gmail.com>
This commit is contained in:
Disservin
2026-02-08 15:20:42 +01:00
co-authored by Timothy Herchen
parent fac506bdf3
commit bfeb36eb3d
5 changed files with 53 additions and 31 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/memory.h
+12 -1
View File
@@ -20,12 +20,12 @@
#define MEMORY_H_INCLUDED
#include <algorithm>
#include <cstddef>
#include <cstdint>
#include <memory>
#include <new>
#include <type_traits>
#include <utility>
#include <cstring>
#include "types.h"
@@ -317,6 +317,17 @@ auto windows_try_with_large_page_priviliges([[maybe_unused]] FuncYesT&& fyes, Fu
#endif
template<typename T, typename ByteT>
T load_as(const ByteT* buffer) {
static_assert(std::is_trivially_copyable<T>::value, "Type must be trivially copyable");
static_assert(sizeof(ByteT) == 1);
T value;
std::memcpy(&value, buffer, sizeof(T));
return value;
}
} // namespace Stockfish
#endif // #ifndef MEMORY_H_INCLUDED
src/nnue/layers/affine_transform.h
+5 -4
View File
@@ -24,6 +24,7 @@
#include <cstdint>
#include <iostream>
#include "../../memory.h"
#include "../nnue_common.h"
#include "../simd.h"
@@ -95,7 +96,7 @@ static void affine_transform_non_ssse3(std::int32_t* output,
#elif defined(USE_NEON)
int32x4_t sum = {biases[i]};
const auto row = reinterpret_cast<const int8x8_t*>(&weights[offset]);
const auto row = reinterpret_cast<const SIMD::vec_i8x8_t*>(&weights[offset]);
for (IndexType j = 0; j < NumChunks; ++j)
{
int16x8_t product = vmull_s8(inputVector[j * 2], row[j * 2]);
@@ -224,7 +225,6 @@ class AffineTransform {
constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / 4;
constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
const auto input32 = reinterpret_cast<const std::int32_t*>(input);
const vec_t* biasvec = reinterpret_cast<const vec_t*>(biases);
vec_t acc[NumRegs];
for (IndexType k = 0; k < NumRegs; ++k)
@@ -232,8 +232,9 @@ class AffineTransform {
for (IndexType i = 0; i < NumChunks; ++i)
{
const vec_t in0 = vec_set_32(input32[i]);
const auto col0 =
const vec_t in0 =
vec_set_32(load_as<std::int32_t>(input + i * sizeof(std::int32_t)));
const auto col0 =
reinterpret_cast<const vec_t*>(&weights[i * OutputDimensions * 4]);
for (IndexType k = 0; k < NumRegs; ++k)
src/nnue/layers/affine_transform_sparse_input.h
+21 -17
View File
@@ -27,6 +27,7 @@
#include <iostream>
#include "../../bitboard.h"
#include "../../memory.h"
#include "../simd.h"
#include "../nnue_common.h"
@@ -77,15 +78,17 @@ alignas(CacheLineSize) static constexpr struct OffsetIndices {
#define RESTRICT
#endif
// Find indices of nonzero numbers in an int32_t array
// Find indices of nonzero 32-bit values in a packed byte buffer.
// The input pointer addresses a sequence of 32-bit blocks stored in a
// std::uint8_t array.
template<const IndexType InputDimensions>
void find_nnz(const std::int32_t* RESTRICT input,
void find_nnz(const std::uint8_t* RESTRICT input,
std::uint16_t* RESTRICT out,
IndexType& count_out) {
#if defined(USE_AVX512ICL)
constexpr IndexType SimdWidthIn = 16; // 512 bits / 32 bits
constexpr IndexType SimdWidthIn = 64; // 512 bits
constexpr IndexType SimdWidthOut = 32; // 512 bits / 16 bits
constexpr IndexType NumChunks = InputDimensions / SimdWidthOut;
const __m512i increment = _mm512_set1_epi16(SimdWidthOut);
@@ -122,7 +125,7 @@ void find_nnz(const std::int32_t* RESTRICT input,
IndexType count = 0;
for (IndexType i = 0; i < NumChunks; ++i)
{
const __m512i inputV = _mm512_load_si512(input + i * SimdWidth);
const __m512i inputV = _mm512_load_si512(input + i * SimdWidth * sizeof(std::uint32_t));
// Get a bitmask and gather non zero indices
const __mmask16 nnzMask = _mm512_test_epi32_mask(inputV, inputV);
@@ -293,10 +296,8 @@ class AffineTransformSparseInput {
std::uint16_t nnz[NumChunks];
IndexType count;
const auto input32 = reinterpret_cast<const std::int32_t*>(input);
// Find indices of nonzero 32-bit blocks
find_nnz<NumChunks>(input32, nnz, count);
find_nnz<NumChunks>(input, nnz, count);
const outvec_t* biasvec = reinterpret_cast<const outvec_t*>(biases);
outvec_t acc[NumRegs];
@@ -314,13 +315,16 @@ class AffineTransformSparseInput {
while (start < end - 2)
{
const std::ptrdiff_t i0 = *start++;
const std::ptrdiff_t i1 = *start++;
const std::ptrdiff_t i2 = *start++;
const invec_t in0 = vec_set_32(input32[i0]);
const invec_t in1 = vec_set_32(input32[i1]);
const invec_t in2 = vec_set_32(input32[i2]);
const auto col0 =
const std::ptrdiff_t i0 = *start++;
const std::ptrdiff_t i1 = *start++;
const std::ptrdiff_t i2 = *start++;
const invec_t in0 =
vec_set_32(load_as<std::int32_t>(input + i0 * sizeof(std::int32_t)));
const invec_t in1 =
vec_set_32(load_as<std::int32_t>(input + i1 * sizeof(std::int32_t)));
const invec_t in2 =
vec_set_32(load_as<std::int32_t>(input + i2 * sizeof(std::int32_t)));
const auto col0 =
reinterpret_cast<const invec_t*>(&weights_cp[i0 * OutputDimensions * ChunkSize]);
const auto col1 =
reinterpret_cast<const invec_t*>(&weights_cp[i1 * OutputDimensions * ChunkSize]);
@@ -338,9 +342,9 @@ class AffineTransformSparseInput {
#endif
while (start < end)
{
const std::ptrdiff_t i = *start++;
const invec_t in = vec_set_32(input32[i]);
const auto col =
const std::ptrdiff_t i = *start++;
const invec_t in = vec_set_32(load_as<std::int32_t>(input + i * sizeof(std::int32_t)));
const auto col =
reinterpret_cast<const invec_t*>(&weights_cp[i * OutputDimensions * ChunkSize]);
for (IndexType k = 0; k < NumAccums; ++k)
vec_add_dpbusd_32(acc[k], in, col[k]);
src/nnue/layers/clipped_relu.h
+4 -4
View File
@@ -141,10 +141,10 @@ class ClippedReLU {
constexpr IndexType Start = NumChunks * SimdWidth;
#elif defined(USE_NEON)
constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
const int8x8_t Zero = {0};
const auto in = reinterpret_cast<const int32x4_t*>(input);
const auto out = reinterpret_cast<int8x8_t*>(output);
constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
const SIMD::vec_i8x8_t Zero = {0};
const auto in = reinterpret_cast<const SIMD::vec_i32x4_t*>(input);
const auto out = reinterpret_cast<SIMD::vec_i8x8_t*>(output);
for (IndexType i = 0; i < NumChunks; ++i)
{
int16x8_t shifted;
src/nnue/simd.h
+11 -5
View File
@@ -181,11 +181,17 @@ inline __m128i vec_convert_8_16(uint64_t x) {
#define MaxChunkSize 16
#elif USE_NEON
using vec_t = int16x8_t;
using vec_i8_t = int8x16_t;
using psqt_vec_t = int32x4_t;
using vec128_t = uint16x8_t;
using vec_uint_t = uint32x4_t;
using vec_i8x8_t __attribute__((may_alias)) = int8x8_t;
using vec_i16x8_t __attribute__((may_alias)) = int16x8_t;
using vec_i8x16_t __attribute__((may_alias)) = int8x16_t;
using vec_u16x8_t __attribute__((may_alias)) = uint16x8_t;
using vec_i32x4_t __attribute__((may_alias)) = int32x4_t;
using vec_t __attribute__((may_alias)) = int16x8_t;
using vec_i8_t __attribute__((may_alias)) = int8x16_t;
using psqt_vec_t __attribute__((may_alias)) = int32x4_t;
using vec128_t __attribute__((may_alias)) = uint16x8_t;
using vec_uint_t __attribute__((may_alias)) = uint32x4_t;
#define vec_load(a) (*(a))
#define vec_store(a, b) *(a) = (b)
#define vec_add_16(a, b) vaddq_s16(a, b)