// Read network parameters
bool ReadParameters(std::istream& stream) {
if (!previous_layer_.ReadParameters(stream)) return false;
- stream.read(reinterpret_cast<char*>(biases_),
- kOutputDimensions * sizeof(BiasType));
- stream.read(reinterpret_cast<char*>(weights_),
- kOutputDimensions * kPaddedInputDimensions *
- sizeof(WeightType));
+ for (std::size_t i = 0; i < kOutputDimensions; ++i)
+ biases_[i] = read_little_endian<BiasType>(stream);
+ for (std::size_t i = 0; i < kOutputDimensions * kPaddedInputDimensions; ++i)
+ weights_[i] = read_little_endian<WeightType>(stream);
return !stream.fail();
}
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;
+ };
+
+ // This function takes
+ // sum0 = [xmm0a, xmm0b, xmm0c, xmm0d]
+ // sum1 = [xmm1a, xmm1b, xmm1c, xmm1d]
+ // sum2 = [xmm2a, xmm2b, xmm2c, xmm2d]
+ // sum3 = [xmm3a, xmm3b, xmm3c, xmm3d]
+ // and returns
+ // ret = [
+ // reduce_add_epi32(xmm0a), reduce_add_epi32(xmm1a), reduce_add_epi32(xmm2a), reduce_add_epi32(xmm3a),
+ // reduce_add_epi32(xmm0b), reduce_add_epi32(xmm1b), reduce_add_epi32(xmm2b), reduce_add_epi32(xmm3b),
+ // reduce_add_epi32(xmm0c), reduce_add_epi32(xmm1c), reduce_add_epi32(xmm2c), reduce_add_epi32(xmm3c),
+ // reduce_add_epi32(xmm0d), reduce_add_epi32(xmm1d), reduce_add_epi32(xmm2d), reduce_add_epi32(xmm3d)
+ // ]
+ [[maybe_unused]] auto m512_hadd128x16_interleave = [](
+ __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3) -> __m512i {
+
+ __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);
+
+ return _mm512_add_epi32(sum0123a, sum0123b);
+ };
+
+ [[maybe_unused]] auto m512_haddx4 = [m512_hadd128x16_interleave](
+ __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3, __m128i bias) -> __m128i {
+
+ __m512i sum = m512_hadd128x16_interleave(sum0, sum1, sum2, sum3);
+
+ __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_haddx8 = [m512_hadd128x16_interleave](
+ __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3,
+ __m512i sum4, __m512i sum5, __m512i sum6, __m512i sum7, __m256i bias) -> __m256i {
+
+ __m512i suma = m512_hadd128x16_interleave(sum0, sum1, sum2, sum3);
+ __m512i sumb = m512_hadd128x16_interleave(sum4, sum5, sum6, sum7);
+
+ __m512i indices0 = _mm512_setr_epi64(0, 1, 8, 9, 4, 5, 12, 13);
+ __m512i indices1 = _mm512_setr_epi64(2, 3, 10, 11, 6, 7, 14, 15);
+ __m512i x = _mm512_add_epi32(
+ _mm512_permutex2var_epi64(suma, indices0, sumb),
+ _mm512_permutex2var_epi64(suma, indices1, sumb));
+
+ __m256i sum256lo = _mm512_castsi512_si256(x);
+ __m256i sum256hi = _mm512_extracti64x4_epi64(x, 1);
+
+ return _mm256_add_epi32(_mm256_add_epi32(sum256lo, sum256hi), bias);
+ };
+
+ [[maybe_unused]] auto m512_hadd256x8 =[m512_hadd128x16_interleave](
+ __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3, __m256i bias) -> __m256i {
+
+ __m512i sum = m512_hadd128x16_interleave(sum0, sum1, sum2, sum3);
+
+ __m512i indices = _mm512_setr_epi32(
+ 0, 4, 8, 12, 2, 6, 10, 14,
+ 1, 5, 9, 13, 3, 7, 11, 15);
+ sum = _mm512_permutexvar_epi32(indices, sum);
+
+ __m256i sum256lo = _mm512_castsi512_si256(sum);
+ __m256i sum256hi = _mm512_extracti64x4_epi64(sum, 1);
+
+ return _mm256_add_epi32(_mm256_hadd_epi32(sum256lo, sum256hi), bias);
+ };
+
+ [[maybe_unused]] auto m512_hadd256x16 = [m512_hadd128x16_interleave](
+ __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3,
+ __m512i sum4, __m512i sum5, __m512i sum6, __m512i sum7, __m512i bias) -> __m512i {
+
+ __m512i suma = m512_hadd128x16_interleave(sum0, sum1, sum2, sum3);
+ __m512i sumb = m512_hadd128x16_interleave(sum4, sum5, sum6, sum7);
+
+ __m512i indices0 = _mm512_setr_epi64(0, 1, 8, 9, 4, 5, 12, 13);
+ __m512i indices1 = _mm512_setr_epi64(2, 3, 10, 11, 6, 7, 14, 15);
+ __m512i x = _mm512_add_epi32(
+ _mm512_permutex2var_epi64(suma, indices0, sumb),
+ _mm512_permutex2var_epi64(suma, indices1, sumb));
+
+ __m512i indices = _mm512_setr_epi32(0, 8, 1, 9, 2, 10, 3, 11, 4, 12, 5, 13, 6, 14, 7, 15);
+ return _mm512_add_epi32(_mm512_permutexvar_epi32(indices, x), bias);
+ };
+
+#if defined (USE_VNNI)
+ [[maybe_unused]] auto m512_add_dpbusd_epi32 = [=](__m512i& acc, __m512i a, __m512i b) {
+ acc = _mm512_dpbusd_epi32(acc, a, b);
+#else
+ [[maybe_unused]] auto m512_dpbusd_epi32 = [=](__m512i a, __m512i b) -> __m512i {
+ __m512i product0 = _mm512_maddubs_epi16(a, b);
+ return _mm512_madd_epi16(product0, kOnes512);
+#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);
+ };
+#if defined (USE_VNNI)
+ [[maybe_unused]] auto m256_add_dpbusd_epi32 = [=](__m256i& acc, __m256i a, __m256i b) {
+ acc = _mm256_dpbusd_epi32(acc, a, b);
+#else
+ [[maybe_unused]] auto m256_dpbusd_epi32 = [=](__m256i a, __m256i b) -> __m256i {
+ __m256i product0 = _mm256_maddubs_epi16(a, b);
+ return _mm256_madd_epi16(product0, kOnes256);
+#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_dpbusd_epi32 = [=](__m128i a, __m128i b) -> __m128i {
+ __m128i product0 = _mm_maddubs_epi16(a, b);
+ return _mm_madd_epi16(product0, kOnes128);
+ };
+
+#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 __m512i kOnes = _mm512_set1_epi16(1);
- const auto input_vector = reinterpret_cast<const __m512i*>(input);
+ // 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 % 16 == 0 && kNumChunks256 == 1)
+ {
+ for (IndexType i = 0; i < kOutputDimensions; i += 16)
+ {
+ const IndexType offset01a = (i + 0) * kPaddedInputDimensions;
+ const IndexType offset23a = (i + 2) * kPaddedInputDimensions;
+ const IndexType offset45a = (i + 4) * kPaddedInputDimensions;
+ const IndexType offset67a = (i + 6) * kPaddedInputDimensions;
+ const IndexType offset01b = (i + 8) * kPaddedInputDimensions;
+ const IndexType offset23b = (i + 10) * kPaddedInputDimensions;
+ const IndexType offset45b = (i + 12) * kPaddedInputDimensions;
+ const IndexType offset67b = (i + 14) * kPaddedInputDimensions;
+
+ const __m512i bias = *reinterpret_cast<const __m512i*>(&biases_[i]);
+ __m512i* outptr = reinterpret_cast<__m512i*>(&output[i]);
+
+ const auto row01a = *reinterpret_cast<const __m512i*>(&weights_[offset01a]);
+ const auto row23a = *reinterpret_cast<const __m512i*>(&weights_[offset23a]);
+ const auto row45a = *reinterpret_cast<const __m512i*>(&weights_[offset45a]);
+ const auto row67a = *reinterpret_cast<const __m512i*>(&weights_[offset67a]);
+ const auto row01b = *reinterpret_cast<const __m512i*>(&weights_[offset01b]);
+ const auto row23b = *reinterpret_cast<const __m512i*>(&weights_[offset23b]);
+ const auto row45b = *reinterpret_cast<const __m512i*>(&weights_[offset45b]);
+ const auto row67b = *reinterpret_cast<const __m512i*>(&weights_[offset67b]);
+
+ const __m256i in256 = input_vector256[0];
+ const __m512i in = _mm512_inserti64x4(_mm512_castsi256_si512(in256), in256, 1);
+
+#if defined (USE_VNNI)
+ __m512i sum01a = _mm512_setzero_si512();
+ __m512i sum23a = _mm512_setzero_si512();
+ __m512i sum45a = _mm512_setzero_si512();
+ __m512i sum67a = _mm512_setzero_si512();
+ __m512i sum01b = _mm512_setzero_si512();
+ __m512i sum23b = _mm512_setzero_si512();
+ __m512i sum45b = _mm512_setzero_si512();
+ __m512i sum67b = _mm512_setzero_si512();
+
+ m512_add_dpbusd_epi32(sum01a, in, row01a);
+ m512_add_dpbusd_epi32(sum23a, in, row23a);
+ m512_add_dpbusd_epi32(sum45a, in, row45a);
+ m512_add_dpbusd_epi32(sum67a, in, row67a);
+ m512_add_dpbusd_epi32(sum01b, in, row01b);
+ m512_add_dpbusd_epi32(sum23b, in, row23b);
+ m512_add_dpbusd_epi32(sum45b, in, row45b);
+ m512_add_dpbusd_epi32(sum67b, in, row67b);
+#else
+ __m512i sum01a = m512_dpbusd_epi32(in, row01a);
+ __m512i sum23a = m512_dpbusd_epi32(in, row23a);
+ __m512i sum45a = m512_dpbusd_epi32(in, row45a);
+ __m512i sum67a = m512_dpbusd_epi32(in, row67a);
+ __m512i sum01b = m512_dpbusd_epi32(in, row01b);
+ __m512i sum23b = m512_dpbusd_epi32(in, row23b);
+ __m512i sum45b = m512_dpbusd_epi32(in, row45b);
+ __m512i sum67b = m512_dpbusd_epi32(in, row67b);
+#endif
+
+ *outptr = m512_hadd256x16(
+ sum01a, sum23a, sum45a, sum67a,
+ sum01b, sum23b, sum45b, sum67b, bias);
+ }
+ }
+ else 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)
+ {
+ 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]);
+
+#if defined (USE_VNNI)
+ __m512i sum0 = _mm512_setzero_si512();
+ __m512i sum1 = _mm512_setzero_si512();
+ __m512i sum2 = _mm512_setzero_si512();
+ __m512i sum3 = _mm512_setzero_si512();
+ const IndexType kStart = 0;
+#else
+ __m512i sum0 = m512_dpbusd_epi32(input_vector512[0], row0[0]);
+ __m512i sum1 = m512_dpbusd_epi32(input_vector512[0], row1[0]);
+ __m512i sum2 = m512_dpbusd_epi32(input_vector512[0], row2[0]);
+ __m512i sum3 = m512_dpbusd_epi32(input_vector512[0], row3[0]);
+ const IndexType kStart = 1;
+#endif
+
+ for (IndexType j = kStart; j < kNumChunks512; ++j)
+ {
+ const __m512i in = input_vector512[j];
+
+#if defined (USE_VNNI)
+ 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]);
+#else
+ sum0 = _mm512_add_epi32(sum0, m512_dpbusd_epi32(in, row0[j]));
+ sum1 = _mm512_add_epi32(sum1, m512_dpbusd_epi32(in, row1[j]));
+ sum2 = _mm512_add_epi32(sum2, m512_dpbusd_epi32(in, row2[j]));
+ sum3 = _mm512_add_epi32(sum3, m512_dpbusd_epi32(in, row3[j]));
+#endif
+ }
+
+ *outptr = m512_haddx4(sum0, sum1, sum2, sum3, bias);
+ }
+ else
+ {
+ 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]);
+
+#if defined (USE_VNNI)
+ __m256i sum0 = _mm256_setzero_si256();
+ __m256i sum1 = _mm256_setzero_si256();
+ __m256i sum2 = _mm256_setzero_si256();
+ __m256i sum3 = _mm256_setzero_si256();
+ const IndexType kStart = 0;
+#else
+ __m256i sum0 = m256_dpbusd_epi32(input_vector256[0], row0[0]);
+ __m256i sum1 = m256_dpbusd_epi32(input_vector256[0], row1[0]);
+ __m256i sum2 = m256_dpbusd_epi32(input_vector256[0], row2[0]);
+ __m256i sum3 = m256_dpbusd_epi32(input_vector256[0], row3[0]);
+ const IndexType kStart = 1;
+#endif
+
+ for (IndexType j = kStart; j < kNumChunks256; ++j)
+ {
+ const __m256i in = input_vector256[j];
+
+#if defined (USE_VNNI)
+ 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]);
+#else
+ sum0 = _mm256_add_epi32(sum0, m256_dpbusd_epi32(in, row0[j]));
+ sum1 = _mm256_add_epi32(sum1, m256_dpbusd_epi32(in, row1[j]));
+ sum2 = _mm256_add_epi32(sum2, m256_dpbusd_epi32(in, row2[j]));
+ sum3 = _mm256_add_epi32(sum3, m256_dpbusd_epi32(in, row3[j]));
+#endif
+ }
+
+ *outptr = m256_haddx4(sum0, sum1, sum2, sum3, bias);
+ }
+ }
+ }
+ else if constexpr (kOutputDimensions == 1)
+ {
+ if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) == 0)
+ {
+ const auto row0 = reinterpret_cast<const __m512i*>(&weights_[0]);
+
+#if defined (USE_VNNI)
+ __m512i sum0 = _mm512_setzero_si512();
+ const IndexType kStart = 0;
+#else
+ __m512i sum0 = m512_dpbusd_epi32(input_vector512[0], row0[0]);
+ const IndexType kStart = 1;
+#endif
+
+ for (IndexType j = kStart; j < kNumChunks512; ++j)
+ {
+ const __m512i in = input_vector512[j];
+
+#if defined (USE_VNNI)
+ m512_add_dpbusd_epi32(sum0, in, row0[j]);
+#else
+ sum0 = _mm512_add_epi32(sum0, m512_dpbusd_epi32(in, row0[j]));
+#endif
+ }
+
+ output[0] = m512_hadd(sum0, biases_[0]);
+ }
+ else
+ {
+ const auto row0 = reinterpret_cast<const __m256i*>(&weights_[0]);
+
+#if defined (USE_VNNI)
+ __m256i sum0 = _mm256_setzero_si256();
+ const IndexType kStart = 0;
+#else
+ __m256i sum0 = m256_dpbusd_epi32(input_vector256[0], row0[0]);
+ const IndexType kStart = 1;
+#endif
+
+ for (IndexType j = kStart; j < kNumChunks256; ++j)
+ {
+ const __m256i in = input_vector256[j];
+
+#if defined (USE_VNNI)
+ m256_add_dpbusd_epi32(sum0, in, row0[j]);
+#else
+ sum0 = _mm256_add_epi32(sum0, m256_dpbusd_epi32(in, row0[j]));
+#endif
+ }
+
+ 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 __m256i kOnes = _mm256_set1_epi16(1);
+
+ const auto output = reinterpret_cast<OutputType*>(buffer);
const auto input_vector = reinterpret_cast<const __m256i*>(input);
- #elif defined(USE_SSSE3)
+ // 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]);
+
+ 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]);
+
+#if defined (USE_VNNI)
+ __m256i sum0 = _mm256_setzero_si256();
+ __m256i sum1 = _mm256_setzero_si256();
+ __m256i sum2 = _mm256_setzero_si256();
+ __m256i sum3 = _mm256_setzero_si256();
+ const IndexType kStart = 0;
+#else
+ __m256i sum0 = m256_dpbusd_epi32(input_vector[0], row0[0]);
+ __m256i sum1 = m256_dpbusd_epi32(input_vector[0], row1[0]);
+ __m256i sum2 = m256_dpbusd_epi32(input_vector[0], row2[0]);
+ __m256i sum3 = m256_dpbusd_epi32(input_vector[0], row3[0]);
+ const IndexType kStart = 1;
+#endif
+
+ for (IndexType j = kStart; j < kNumChunks; ++j)
+ {
+ const __m256i in = input_vector[j];
+
+#if defined (USE_VNNI)
+ 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]);
+#else
+ sum0 = _mm256_add_epi32(sum0, m256_dpbusd_epi32(in, row0[j]));
+ sum1 = _mm256_add_epi32(sum1, m256_dpbusd_epi32(in, row1[j]));
+ sum2 = _mm256_add_epi32(sum2, m256_dpbusd_epi32(in, row2[j]));
+ sum3 = _mm256_add_epi32(sum3, m256_dpbusd_epi32(in, row3[j]));
+#endif
+ }
+
+ *outptr = m256_haddx4(sum0, sum1, sum2, sum3, bias);
+ }
+ }
+ else if constexpr (kOutputDimensions == 1)
+ {
+ const auto row0 = reinterpret_cast<const __m256i*>(&weights_[0]);
+
+#if defined (USE_VNNI)
+ __m256i sum0 = _mm256_setzero_si256();
+ const IndexType kStart = 0;
+#else
+ __m256i sum0 = m256_dpbusd_epi32(input_vector[0], row0[0]);
+ const IndexType kStart = 1;
+#endif
+
+ for (IndexType j = kStart; j < kNumChunks; ++j)
+ {
+ const __m256i in = input_vector[j];
+
+#if defined (USE_VNNI)
+ m256_add_dpbusd_epi32(sum0, in, row0[j]);
+#else
+ sum0 = _mm256_add_epi32(sum0, m256_dpbusd_epi32(in, row0[j]));
+#endif
+ }
+
+ 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;
+
+ 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]);
+
+ 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]);
+
+ __m128i sum0 = m128_dpbusd_epi32(input_vector[0], row0[0]);
+ __m128i sum1 = m128_dpbusd_epi32(input_vector[0], row1[0]);
+ __m128i sum2 = m128_dpbusd_epi32(input_vector[0], row2[0]);
+ __m128i sum3 = m128_dpbusd_epi32(input_vector[0], row3[0]);
+
+ for (int j = 1; j < (int)kNumChunks; ++j)
+ {
+ const __m128i in = input_vector[j];
+
+ sum0 = _mm_add_epi32(sum0, m128_dpbusd_epi32(in, row0[j]));
+ sum1 = _mm_add_epi32(sum1, m128_dpbusd_epi32(in, row1[j]));
+ sum2 = _mm_add_epi32(sum2, m128_dpbusd_epi32(in, row2[j]));
+ sum3 = _mm_add_epi32(sum3, m128_dpbusd_epi32(in, row3[j]));
+ }
+
+ *outptr = m128_haddx4(sum0, sum1, sum2, sum3, bias);
+ }
+ }
+ else if constexpr (kOutputDimensions == 1)
+ {
+ const auto row0 = reinterpret_cast<const __m128i*>(&weights_[0]);
+
+ __m128i sum0 = m128_dpbusd_epi32(input_vector[0], row0[0]);
+
+ for (int j = 1; j < (int)kNumChunks; ++j)
+ sum0 = _mm_add_epi32(sum0, m128_dpbusd_epi32(input_vector[j], 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
const __m128i kOnes = _mm_set1_epi16(1);
+#endif
const auto input_vector = reinterpret_cast<const __m128i*>(input);
- #elif defined(USE_NEON)
+#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)
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]);
+#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]);
for (IndexType j = 0; j < kNumChunks; ++j) {
- __m512i product = _mm512_maddubs_epi16(
- _mm512_load_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
- product = _mm512_madd_epi16(product, kOnes);
- sum = _mm512_add_epi32(sum, product);
- }
- output[i] = _mm512_reduce_add_epi32(sum) + biases_[i];
-
- // 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 iv_256 = reinterpret_cast<const __m256i*>(input);
- const auto row_256 = reinterpret_cast<const __m256i*>(&weights_[offset]);
- int j = kNumChunks * 2;
-
- __m256i sum256 = _mm256_maddubs_epi16(
- _mm256_load_si256(&iv_256[j]), _mm256_load_si256(&row_256[j]));
- sum256 = _mm256_madd_epi16(sum256, _mm256_set1_epi16(1));
- sum256 = _mm256_hadd_epi32(sum256, sum256);
- sum256 = _mm256_hadd_epi32(sum256, sum256);
- const __m128i lo = _mm256_extracti128_si256(sum256, 0);
- const __m128i hi = _mm256_extracti128_si256(sum256, 1);
- output[i] += _mm_cvtsi128_si32(lo) + _mm_cvtsi128_si32(hi);
+ __m128i row_j = _mm_load_si128(&row[j]);
+ __m128i input_j = _mm_load_si128(&input_vector[j]);
+ __m128i row_signs = _mm_cmpgt_epi8(kZeros, row_j);
+ __m128i extended_row_lo = _mm_unpacklo_epi8(row_j, row_signs);
+ __m128i extended_row_hi = _mm_unpackhi_epi8(row_j, row_signs);
+ __m128i extended_input_lo = _mm_unpacklo_epi8(input_j, kZeros);
+ __m128i extended_input_hi = _mm_unpackhi_epi8(input_j, kZeros);
+ __m128i product_lo = _mm_madd_epi16(extended_row_lo, extended_input_lo);
+ __m128i product_hi = _mm_madd_epi16(extended_row_hi, extended_input_hi);
+ sum_lo = _mm_add_epi32(sum_lo, product_lo);
+ sum_hi = _mm_add_epi32(sum_hi, product_hi);
}
+ __m128i sum = _mm_add_epi32(sum_lo, sum_hi);
+ __m128i sum_high_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
+ sum = _mm_add_epi32(sum, sum_high_64);
+ __m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
+ sum = _mm_add_epi32(sum, sum_second_32);
+ output[i] = _mm_cvtsi128_si32(sum);
- #elif defined(USE_AVX2)
- __m256i sum = _mm256_setzero_si256();
- const auto row = reinterpret_cast<const __m256i*>(&weights_[offset]);
+#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]);
for (IndexType j = 0; j < kNumChunks; ++j) {
- __m256i product = _mm256_maddubs_epi16(
- _mm256_load_si256(&input_vector[j]), _mm256_load_si256(&row[j]));
- product = _mm256_madd_epi16(product, kOnes);
- sum = _mm256_add_epi32(sum, product);
+ __m64 row_j = row[j];
+ __m64 input_j = input_vector[j];
+ __m64 row_signs = _mm_cmpgt_pi8(kZeros, row_j);
+ __m64 extended_row_lo = _mm_unpacklo_pi8(row_j, row_signs);
+ __m64 extended_row_hi = _mm_unpackhi_pi8(row_j, row_signs);
+ __m64 extended_input_lo = _mm_unpacklo_pi8(input_j, kZeros);
+ __m64 extended_input_hi = _mm_unpackhi_pi8(input_j, kZeros);
+ __m64 product_lo = _mm_madd_pi16(extended_row_lo, extended_input_lo);
+ __m64 product_hi = _mm_madd_pi16(extended_row_hi, extended_input_hi);
+ sum_lo = _mm_add_pi32(sum_lo, product_lo);
+ sum_hi = _mm_add_pi32(sum_hi, product_hi);
}
- sum = _mm256_hadd_epi32(sum, sum);
- sum = _mm256_hadd_epi32(sum, sum);
- const __m128i lo = _mm256_extracti128_si256(sum, 0);
- const __m128i hi = _mm256_extracti128_si256(sum, 1);
- output[i] = _mm_cvtsi128_si32(lo) + _mm_cvtsi128_si32(hi) + biases_[i];
-
- #elif defined(USE_SSSE3)
- __m128i sum = _mm_cvtsi32_si128(biases_[i]);
- const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- __m128i product = _mm_maddubs_epi16(
- _mm_load_si128(&input_vector[j]), _mm_load_si128(&row[j]));
- product = _mm_madd_epi16(product, kOnes);
- sum = _mm_add_epi32(sum, product);
- }
- sum = _mm_hadd_epi32(sum, sum);
- sum = _mm_hadd_epi32(sum, sum);
- output[i] = _mm_cvtsi128_si32(sum);
+ __m64 sum = _mm_add_pi32(sum_lo, sum_hi);
+ 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)
+ _mm_empty();
+#endif
+
+#endif
+
return output;
}
projects / stockfish / blobdiff