const TransformedFeatureType* transformed_features, char* buffer) const {
const auto input = previous_layer_.Propagate(
transformed_features, buffer + kSelfBufferSize);
+
+#if defined (USE_AVX512)
+
+ [[maybe_unused]] const __m512i kOnes512 = _mm512_set1_epi16(1);
+
+ [[maybe_unused]] auto m512_hadd = [](__m512i sum, int bias) -> int {
+ return _mm512_reduce_add_epi32(sum) + bias;
+ };
+
+ [[maybe_unused]] auto m512_haddx4 = [](__m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3, __m128i bias) -> __m128i {
+ __m512i sum01a = _mm512_unpacklo_epi32(sum0, sum1);
+ __m512i sum01b = _mm512_unpackhi_epi32(sum0, sum1);
+
+ __m512i sum23a = _mm512_unpacklo_epi32(sum2, sum3);
+ __m512i sum23b = _mm512_unpackhi_epi32(sum2, sum3);
+
+ __m512i sum01 = _mm512_add_epi32(sum01a, sum01b);
+ __m512i sum23 = _mm512_add_epi32(sum23a, sum23b);
+
+ __m512i sum0123a = _mm512_unpacklo_epi64(sum01, sum23);
+ __m512i sum0123b = _mm512_unpackhi_epi64(sum01, sum23);
+
+ __m512i sum = _mm512_add_epi32(sum0123a, sum0123b);
+
+ __m256i sum256lo = _mm512_castsi512_si256(sum);
+ __m256i sum256hi = _mm512_extracti64x4_epi64(sum, 1);
+
+ sum256lo = _mm256_add_epi32(sum256lo, sum256hi);
+
+ __m128i sum128lo = _mm256_castsi256_si128(sum256lo);
+ __m128i sum128hi = _mm256_extracti128_si256(sum256lo, 1);
+
+ return _mm_add_epi32(_mm_add_epi32(sum128lo, sum128hi), bias);
+ };
+
+ [[maybe_unused]] auto m512_add_dpbusd_epi32 = [=](__m512i& acc, __m512i a, __m512i b) {
+#if defined (USE_VNNI)
+ acc = _mm512_dpbusd_epi32(acc, a, b);
+#else
+ __m512i product0 = _mm512_maddubs_epi16(a, b);
+ product0 = _mm512_madd_epi16(product0, kOnes512);
+ acc = _mm512_add_epi32(acc, product0);
+#endif
+ };
+
+#endif
+#if defined (USE_AVX2)
+
+ [[maybe_unused]] const __m256i kOnes256 = _mm256_set1_epi16(1);
+
+ [[maybe_unused]] auto m256_hadd = [](__m256i sum, int bias) -> int {
+ __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1));
+ sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_BADC));
+ sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_CDAB));
+ return _mm_cvtsi128_si32(sum128) + bias;
+ };
+
+ [[maybe_unused]] auto m256_haddx4 = [](__m256i sum0, __m256i sum1, __m256i sum2, __m256i sum3, __m128i bias) -> __m128i {
+ sum0 = _mm256_hadd_epi32(sum0, sum1);
+ sum2 = _mm256_hadd_epi32(sum2, sum3);
+
+ sum0 = _mm256_hadd_epi32(sum0, sum2);
+
+ __m128i sum128lo = _mm256_castsi256_si128(sum0);
+ __m128i sum128hi = _mm256_extracti128_si256(sum0, 1);
+
+ return _mm_add_epi32(_mm_add_epi32(sum128lo, sum128hi), bias);
+ };
+
+ [[maybe_unused]] auto m256_add_dpbusd_epi32 = [=](__m256i& acc, __m256i a, __m256i b) {
+#if defined (USE_VNNI)
+ acc = _mm256_dpbusd_epi32(acc, a, b);
+#else
+ __m256i product0 = _mm256_maddubs_epi16(a, b);
+ product0 = _mm256_madd_epi16(product0, kOnes256);
+ acc = _mm256_add_epi32(acc, product0);
+#endif
+ };
+
+#endif
+
+#if defined (USE_SSSE3)
+
+ [[maybe_unused]] const __m128i kOnes128 = _mm_set1_epi16(1);
+
+ [[maybe_unused]] auto m128_hadd = [](__m128i sum, int bias) -> int {
+ sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC
+ sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0xB1)); //_MM_PERM_CDAB
+ return _mm_cvtsi128_si32(sum) + bias;
+ };
+
+ [[maybe_unused]] auto m128_haddx4 = [](__m128i sum0, __m128i sum1, __m128i sum2, __m128i sum3, __m128i bias) -> __m128i {
+ sum0 = _mm_hadd_epi32(sum0, sum1);
+ sum2 = _mm_hadd_epi32(sum2, sum3);
+
+ sum0 = _mm_hadd_epi32(sum0, sum2);
+
+ return _mm_add_epi32(sum0, bias);
+ };
+
+ [[maybe_unused]] auto m128_add_dpbusd_epi32 = [=](__m128i& acc, __m128i a, __m128i b) {
+ __m128i product0 = _mm_maddubs_epi16(a, b);
+ product0 = _mm_madd_epi16(product0, kOnes128);
+ acc = _mm_add_epi32(acc, product0);
+ };
+
+#endif
+
+#if defined (USE_AVX512)
+
+ constexpr IndexType kNumChunks512 = kPaddedInputDimensions / (kSimdWidth * 2);
+ constexpr IndexType kNumChunks256 = kPaddedInputDimensions / kSimdWidth;
+
const auto output = reinterpret_cast<OutputType*>(buffer);
- #if defined(USE_AVX512)
- constexpr IndexType kNumChunks = kPaddedInputDimensions / (kSimdWidth * 2);
- const auto input_vector = reinterpret_cast<const __m512i*>(input);
- #if !defined(USE_VNNI)
- const __m512i kOnes = _mm512_set1_epi16(1);
- #endif
+ // Since to saturate a zmm register it takes 64 bytes we
+ // cannot use AVX512 for the smaller affine transforms.
+ // Instead we fallback to a AVX2 implementation if the
+ // kInputDimensions isn't a multiple of 64.
+ // Note that this means that for example for
+ // kInputDimensions of 96 we fallback to AVX2 even though
+ // the first 64 elements could be processed with AVX512.
+ // This is caused by mixing the __m256 and __m512 variables
+ // required to better handle that case and it would
+ // require handling more cases statically not to lose performance.
+ // This should be revisited if such input dimensions are to be considered.
+ [[maybe_unused]] const auto input_vector512 = reinterpret_cast<const __m512i*>(input);
+ [[maybe_unused]] const auto input_vector256 = reinterpret_cast<const __m256i*>(input);
+
+ // kOutputDimensions is either 1 or a multiple of kSimdWidth
+ // because then it is also an input dimension.
+ if constexpr (kOutputDimensions % 4 == 0)
+ {
+ for (IndexType i = 0; i < kOutputDimensions; i += 4)
+ {
+ const IndexType offset0 = (i + 0) * kPaddedInputDimensions;
+ const IndexType offset1 = (i + 1) * kPaddedInputDimensions;
+ const IndexType offset2 = (i + 2) * kPaddedInputDimensions;
+ const IndexType offset3 = (i + 3) * kPaddedInputDimensions;
+
+ const __m128i bias = *reinterpret_cast<const __m128i*>(&biases_[i]);
+ __m128i* outptr = reinterpret_cast<__m128i*>(&output[i]);
+
+ if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) == 0)
+ {
+ __m512i sum0 = _mm512_setzero_si512();
+ __m512i sum1 = _mm512_setzero_si512();
+ __m512i sum2 = _mm512_setzero_si512();
+ __m512i sum3 = _mm512_setzero_si512();
+
+ const auto row0 = reinterpret_cast<const __m512i*>(&weights_[offset0]);
+ const auto row1 = reinterpret_cast<const __m512i*>(&weights_[offset1]);
+ const auto row2 = reinterpret_cast<const __m512i*>(&weights_[offset2]);
+ const auto row3 = reinterpret_cast<const __m512i*>(&weights_[offset3]);
+
+ for (IndexType j = 0; j < kNumChunks512; ++j)
+ {
+ const __m512i in = input_vector512[j];
+
+ m512_add_dpbusd_epi32(sum0, in, row0[j]);
+ m512_add_dpbusd_epi32(sum1, in, row1[j]);
+ m512_add_dpbusd_epi32(sum2, in, row2[j]);
+ m512_add_dpbusd_epi32(sum3, in, row3[j]);
+ }
+
+ *outptr = m512_haddx4(sum0, sum1, sum2, sum3, bias);
+ }
+ else
+ {
+ __m256i sum0 = _mm256_setzero_si256();
+ __m256i sum1 = _mm256_setzero_si256();
+ __m256i sum2 = _mm256_setzero_si256();
+ __m256i sum3 = _mm256_setzero_si256();
+
+ const auto row0 = reinterpret_cast<const __m256i*>(&weights_[offset0]);
+ const auto row1 = reinterpret_cast<const __m256i*>(&weights_[offset1]);
+ const auto row2 = reinterpret_cast<const __m256i*>(&weights_[offset2]);
+ const auto row3 = reinterpret_cast<const __m256i*>(&weights_[offset3]);
+
+ for (IndexType j = 0; j < kNumChunks256; ++j)
+ {
+ const __m256i in = input_vector256[j];
+
+ m256_add_dpbusd_epi32(sum0, in, row0[j]);
+ m256_add_dpbusd_epi32(sum1, in, row1[j]);
+ m256_add_dpbusd_epi32(sum2, in, row2[j]);
+ m256_add_dpbusd_epi32(sum3, in, row3[j]);
+ }
+
+ *outptr = m256_haddx4(sum0, sum1, sum2, sum3, bias);
+ }
+ }
+ }
+ else if constexpr (kOutputDimensions == 1)
+ {
+ if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) == 0)
+ {
+ __m512i sum0 = _mm512_setzero_si512();
+
+ const auto row0 = reinterpret_cast<const __m512i*>(&weights_[0]);
+
+ for (IndexType j = 0; j < kNumChunks512; ++j)
+ {
+ const __m512i in = input_vector512[j];
+
+ m512_add_dpbusd_epi32(sum0, in, row0[j]);
+ }
+
+ output[0] = m512_hadd(sum0, biases_[0]);
+ }
+ else
+ {
+ __m256i sum0 = _mm256_setzero_si256();
+
+ const auto row0 = reinterpret_cast<const __m256i*>(&weights_[0]);
+
+ for (IndexType j = 0; j < kNumChunks256; ++j)
+ {
+ const __m256i in = input_vector256[j];
+
+ m256_add_dpbusd_epi32(sum0, in, row0[j]);
+ }
+
+ output[0] = m256_hadd(sum0, biases_[0]);
+ }
+ }
+ else
+ {
+ // This case can never happen because kOutputDimensions
+ // is always 1 or a multiple of kSimdWidth.
+ assert(false);
+ }
+
+#elif defined (USE_AVX2)
- #elif defined(USE_AVX2)
constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
+
+ const auto output = reinterpret_cast<OutputType*>(buffer);
const auto input_vector = reinterpret_cast<const __m256i*>(input);
- #if !defined(USE_VNNI)
- const __m256i kOnes = _mm256_set1_epi16(1);
- #endif
- #elif defined(USE_SSE2)
+ // kOutputDimensions is either 1 or a multiple of kSimdWidth
+ // because then it is also an input dimension.
+ if constexpr (kOutputDimensions % 4 == 0)
+ {
+ for (IndexType i = 0; i < kOutputDimensions; i += 4)
+ {
+ const IndexType offset0 = (i + 0) * kPaddedInputDimensions;
+ const IndexType offset1 = (i + 1) * kPaddedInputDimensions;
+ const IndexType offset2 = (i + 2) * kPaddedInputDimensions;
+ const IndexType offset3 = (i + 3) * kPaddedInputDimensions;
+
+ const __m128i bias = *reinterpret_cast<const __m128i*>(&biases_[i]);
+ __m128i* outptr = reinterpret_cast<__m128i*>(&output[i]);
+
+ __m256i sum0 = _mm256_setzero_si256();
+ __m256i sum1 = _mm256_setzero_si256();
+ __m256i sum2 = _mm256_setzero_si256();
+ __m256i sum3 = _mm256_setzero_si256();
+
+ const auto row0 = reinterpret_cast<const __m256i*>(&weights_[offset0]);
+ const auto row1 = reinterpret_cast<const __m256i*>(&weights_[offset1]);
+ const auto row2 = reinterpret_cast<const __m256i*>(&weights_[offset2]);
+ const auto row3 = reinterpret_cast<const __m256i*>(&weights_[offset3]);
+
+ for (IndexType j = 0; j < kNumChunks; ++j)
+ {
+ const __m256i in = input_vector[j];
+
+ m256_add_dpbusd_epi32(sum0, in, row0[j]);
+ m256_add_dpbusd_epi32(sum1, in, row1[j]);
+ m256_add_dpbusd_epi32(sum2, in, row2[j]);
+ m256_add_dpbusd_epi32(sum3, in, row3[j]);
+ }
+
+ *outptr = m256_haddx4(sum0, sum1, sum2, sum3, bias);
+ }
+ }
+ else if constexpr (kOutputDimensions == 1)
+ {
+ __m256i sum0 = _mm256_setzero_si256();
+
+ const auto row0 = reinterpret_cast<const __m256i*>(&weights_[0]);
+
+ for (IndexType j = 0; j < kNumChunks; ++j)
+ {
+ const __m256i in = input_vector[j];
+
+ m256_add_dpbusd_epi32(sum0, in, row0[j]);
+ }
+
+ output[0] = m256_hadd(sum0, biases_[0]);
+ }
+ else
+ {
+ // This case can never happen because kOutputDimensions
+ // is always 1 or a multiple of kSimdWidth.
+ assert(false);
+ }
+
+#elif defined (USE_SSSE3)
+
constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
- #ifndef USE_SSSE3
+
+ auto output = reinterpret_cast<OutputType*>(buffer);
+ const auto input_vector = reinterpret_cast<const __m128i*>(input);
+
+ // kOutputDimensions is either 1 or a multiple of kSimdWidth
+ // because then it is also an input dimension.
+ if constexpr (kOutputDimensions % 4 == 0)
+ {
+ for (IndexType i = 0; i < kOutputDimensions; i += 4)
+ {
+ const IndexType offset0 = (i + 0) * kPaddedInputDimensions;
+ const IndexType offset1 = (i + 1) * kPaddedInputDimensions;
+ const IndexType offset2 = (i + 2) * kPaddedInputDimensions;
+ const IndexType offset3 = (i + 3) * kPaddedInputDimensions;
+
+ const __m128i bias = *reinterpret_cast<const __m128i*>(&biases_[i]);
+ __m128i* outptr = reinterpret_cast<__m128i*>(&output[i]);
+
+ __m128i sum0 = _mm_setzero_si128();
+ __m128i sum1 = _mm_setzero_si128();
+ __m128i sum2 = _mm_setzero_si128();
+ __m128i sum3 = _mm_setzero_si128();
+
+ const auto row0 = reinterpret_cast<const __m128i*>(&weights_[offset0]);
+ const auto row1 = reinterpret_cast<const __m128i*>(&weights_[offset1]);
+ const auto row2 = reinterpret_cast<const __m128i*>(&weights_[offset2]);
+ const auto row3 = reinterpret_cast<const __m128i*>(&weights_[offset3]);
+
+ for (int j = 0; j < (int)kNumChunks; j += 1)
+ {
+ const __m128i in = input_vector[j];
+
+ m128_add_dpbusd_epi32(sum0, in, row0[j]);
+ m128_add_dpbusd_epi32(sum1, in, row1[j]);
+ m128_add_dpbusd_epi32(sum2, in, row2[j]);
+ m128_add_dpbusd_epi32(sum3, in, row3[j]);
+ }
+
+ *outptr = m128_haddx4(sum0, sum1, sum2, sum3, bias);
+ }
+ }
+ else if constexpr (kOutputDimensions == 1)
+ {
+ __m128i sum0 = _mm_setzero_si128();
+
+ const auto row0 = reinterpret_cast<const __m128i*>(&weights_[0]);
+
+ for (int j = 0; j < (int)kNumChunks; j += 1)
+ {
+ const __m128i in = input_vector[j];
+
+ m128_add_dpbusd_epi32(sum0, in, row0[j]);
+ }
+
+ output[0] = m128_hadd(sum0, biases_[0]);
+ }
+ else
+ {
+ // This case can never happen because kOutputDimensions
+ // is always 1 or a multiple of kSimdWidth.
+ assert(false);
+ }
+
+#else
+
+// Use old implementation for the other architectures.
+
+ auto output = reinterpret_cast<OutputType*>(buffer);
+
+#if defined(USE_SSE2)
+ constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
+#ifndef USE_SSSE3
const __m128i kZeros = _mm_setzero_si128();
- #else
+#else
const __m128i kOnes = _mm_set1_epi16(1);
- #endif
+#endif
const auto input_vector = reinterpret_cast<const __m128i*>(input);
- #elif defined(USE_MMX)
+#elif defined(USE_MMX)
constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
const __m64 kZeros = _mm_setzero_si64();
const auto input_vector = reinterpret_cast<const __m64*>(input);
- #elif defined(USE_NEON)
+#elif defined(USE_NEON)
constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
const auto input_vector = reinterpret_cast<const int8x8_t*>(input);
- #endif
+#endif
for (IndexType i = 0; i < kOutputDimensions; ++i) {
const IndexType offset = i * kPaddedInputDimensions;
- #if defined(USE_AVX512)
- __m512i sum = _mm512_setzero_si512();
- const auto row = reinterpret_cast<const __m512i*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- #if defined(USE_VNNI)
- sum = _mm512_dpbusd_epi32(sum, _mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
- #else
- __m512i product = _mm512_maddubs_epi16(_mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
- product = _mm512_madd_epi16(product, kOnes);
- sum = _mm512_add_epi32(sum, product);
- #endif
- }
-
- // Note: Changing kMaxSimdWidth from 32 to 64 breaks loading existing networks.
- // As a result kPaddedInputDimensions may not be an even multiple of 64(512bit)
- // and we have to do one more 256bit chunk.
- if (kPaddedInputDimensions != kNumChunks * kSimdWidth * 2)
- {
- const auto iv256 = reinterpret_cast<const __m256i*>(&input_vector[kNumChunks]);
- const auto row256 = reinterpret_cast<const __m256i*>(&row[kNumChunks]);
- #if defined(USE_VNNI)
- __m256i product256 = _mm256_dpbusd_epi32(
- _mm512_castsi512_si256(sum), _mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
- sum = _mm512_inserti32x8(sum, product256, 0);
- #else
- __m256i product256 = _mm256_maddubs_epi16(_mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
- sum = _mm512_add_epi32(sum, _mm512_cvtepi16_epi32(product256));
- #endif
- }
- output[i] = _mm512_reduce_add_epi32(sum) + biases_[i];
-
- #elif defined(USE_AVX2)
- __m256i sum = _mm256_setzero_si256();
- const auto row = reinterpret_cast<const __m256i*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- #if defined(USE_VNNI)
- sum = _mm256_dpbusd_epi32(sum, _mm256_loadA_si256(&input_vector[j]), _mm256_load_si256(&row[j]));
- #else
- __m256i product = _mm256_maddubs_epi16(_mm256_loadA_si256(&input_vector[j]), _mm256_load_si256(&row[j]));
- product = _mm256_madd_epi16(product, kOnes);
- sum = _mm256_add_epi32(sum, product);
- #endif
- }
- __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1));
- sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_BADC));
- sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_CDAB));
- output[i] = _mm_cvtsi128_si32(sum128) + biases_[i];
-
- #elif defined(USE_SSSE3)
- __m128i sum = _mm_setzero_si128();
- const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
- for (int j = 0; j < (int)kNumChunks - 1; j += 2) {
- __m128i product0 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j]), _mm_load_si128(&row[j]));
- product0 = _mm_madd_epi16(product0, kOnes);
- sum = _mm_add_epi32(sum, product0);
- __m128i product1 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j+1]), _mm_load_si128(&row[j+1]));
- product1 = _mm_madd_epi16(product1, kOnes);
- sum = _mm_add_epi32(sum, product1);
- }
- if (kNumChunks & 0x1) {
- __m128i product = _mm_maddubs_epi16(_mm_load_si128(&input_vector[kNumChunks-1]), _mm_load_si128(&row[kNumChunks-1]));
- product = _mm_madd_epi16(product, kOnes);
- sum = _mm_add_epi32(sum, product);
- }
- sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC
- sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0xB1)); //_MM_PERM_CDAB
- output[i] = _mm_cvtsi128_si32(sum) + biases_[i];
-
- #elif defined(USE_SSE2)
+#if defined(USE_SSE2)
__m128i sum_lo = _mm_cvtsi32_si128(biases_[i]);
__m128i sum_hi = kZeros;
const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
sum = _mm_add_epi32(sum, sum_second_32);
output[i] = _mm_cvtsi128_si32(sum);
- #elif defined(USE_MMX)
+#elif defined(USE_MMX)
__m64 sum_lo = _mm_cvtsi32_si64(biases_[i]);
__m64 sum_hi = kZeros;
const auto row = reinterpret_cast<const __m64*>(&weights_[offset]);
sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
output[i] = _mm_cvtsi64_si32(sum);
- #elif defined(USE_NEON)
+#elif defined(USE_NEON)
int32x4_t sum = {biases_[i]};
const auto row = reinterpret_cast<const int8x8_t*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
}
output[i] = sum[0] + sum[1] + sum[2] + sum[3];
- #else
+#else
OutputType sum = biases_[i];
for (IndexType j = 0; j < kInputDimensions; ++j) {
sum += weights_[offset + j] * input[j];
}
output[i] = sum;
- #endif
+#endif
}
- #if defined(USE_MMX)
+#if defined(USE_MMX)
_mm_empty();
- #endif
+#endif
+
+#endif
+
return output;
}
projects / stockfish / blobdiff