biases_[i] = read_little_endian<BiasType>(stream);
for (std::size_t i = 0; i < kOutputDimensions * kPaddedInputDimensions; ++i)
weights_[i] = read_little_endian<WeightType>(stream);
+
+#if defined (USE_SSSE3)
+ // Determine if quadruplets of weight and input products can be summed using 16bits
+ // without saturation. We assume worst case combinations of 0 and 127 for all inputs.
+ if (!stream.fail())
+ {
+ auto can_saturate = [](const WeightType* w, int idx[4]) {
+ int pSum = 0, nSum = 0;
+ for (int p = 0; p < 4; ++p)
+ if (w[idx[p]] > 0)
+ pSum += w[idx[p]];
+ else
+ nSum += w[idx[p]];
+
+ return pSum > 258 || nSum < -258;
+ };
+
+ for (IndexType i = 0; i < kOutputDimensions; ++i)
+ {
+ canSaturate16[i] = false;
+ const WeightType* w = &weights_[i * kPaddedInputDimensions];
+#if defined (USE_AVX512)
+ for (IndexType j = 0; j < (kPaddedInputDimensions & ~127) && !canSaturate16[i]; j += 128)
+ for (int k = 0; k < 64 && !canSaturate16[i]; k += 2)
+ {
+ int spacing[4] = { 0, 1, 64, 65 };
+ canSaturate16[i] = can_saturate(&w[j + k], spacing);
+ }
+#elif defined (USE_AVX2)
+ for (IndexType j = 0; j < (kPaddedInputDimensions & ~63) && !canSaturate16[i]; j += 64)
+ for (int k = 0; k < 32 && !canSaturate16[i]; k += 2)
+ {
+ int spacing[4] = { 0, 1, 32, 33 };
+ canSaturate16[i] = can_saturate(&w[j + k], spacing);
+ }
+#elif defined (USE_SSSE3)
+ for (IndexType j = 0; j < (kPaddedInputDimensions & ~31) && !canSaturate16[i]; j += 32)
+ for (int k = 0; k < 16 && !canSaturate16[i]; k += 2)
+ {
+ int spacing[4] = { 0, 1, 16, 17 };
+ canSaturate16[i] = can_saturate(&w[j + k], spacing);
+ }
+#endif
+ }
+ }
+#endif
+
return !stream.fail();
}
#endif
};
+ [[maybe_unused]] auto m512_add_dpbusd_epi32x2 = [=](__m512i& acc, __m512i a0, __m512i b0, __m512i a1, __m512i b1) {
+#if defined (USE_VNNI)
+ acc = _mm512_dpbusd_epi32(acc, a0, b0);
+ acc = _mm512_dpbusd_epi32(acc, a1, b1);
+#else
+ __m512i product0 = _mm512_maddubs_epi16(a0, b0);
+ __m512i product1 = _mm512_maddubs_epi16(a1, b1);
+ product0 = _mm512_adds_epi16(product0, product1);
+ product0 = _mm512_madd_epi16(product0, kOnes512);
+ acc = _mm512_add_epi32(acc, product0);
+#endif
+ };
+
#endif
#if defined (USE_AVX2)
#endif
};
+ [[maybe_unused]] auto m256_add_dpbusd_epi32x2 = [=](__m256i& acc, __m256i a0, __m256i b0, __m256i a1, __m256i b1) {
+#if defined (USE_VNNI)
+ acc = _mm256_dpbusd_epi32(acc, a0, b0);
+ acc = _mm256_dpbusd_epi32(acc, a1, b1);
+#else
+ __m256i product0 = _mm256_maddubs_epi16(a0, b0);
+ __m256i product1 = _mm256_maddubs_epi16(a1, b1);
+ product0 = _mm256_adds_epi16(product0, product1);
+ product0 = _mm256_madd_epi16(product0, kOnes256);
+ acc = _mm256_add_epi32(acc, product0);
+#endif
+ };
+
#endif
#if defined (USE_SSSE3)
acc = _mm_add_epi32(acc, product0);
};
+ [[maybe_unused]] auto m128_add_dpbusd_epi32x2 = [=](__m128i& acc, __m128i a0, __m128i b0, __m128i a1, __m128i b1) {
+ __m128i product0 = _mm_maddubs_epi16(a0, b0);
+ __m128i product1 = _mm_maddubs_epi16(a1, b1);
+ product0 = _mm_adds_epi16(product0, product1);
+ product0 = _mm_madd_epi16(product0, kOnes128);
+ acc = _mm_add_epi32(acc, product0);
+ };
+
#endif
#if defined (USE_AVX512)
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)
+ int j = 0;
+ if (!canSaturate16x4[i / 4])
+ {
+ for (; j < (int)kNumChunks512 - 1; j += 2)
+ {
+ const __m512i in0 = input_vector512[j];
+ const __m512i in1 = input_vector512[j + 1];
+
+ m512_add_dpbusd_epi32x2(sum0, in0, row0[j], in1, row0[j + 1]);
+ m512_add_dpbusd_epi32x2(sum1, in0, row1[j], in1, row1[j + 1]);
+ m512_add_dpbusd_epi32x2(sum2, in0, row2[j], in1, row2[j + 1]);
+ m512_add_dpbusd_epi32x2(sum3, in0, row3[j], in1, row3[j + 1]);
+ }
+ }
+ for (; j < (int)kNumChunks512; ++j)
{
const __m512i in = input_vector512[j];
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)
+ int j = 0;
+ if (!canSaturate16x4[i / 4])
{
- const __m256i in = input_vector[j];
+ for (; j < (int)kNumChunks - 1; j += 2)
+ {
+ const __m256i in0 = input_vector[j];
+ const __m256i in1 = input_vector[j + 1];
+
+ m256_add_dpbusd_epi32x2(sum0, in0, row0[j], in1, row0[j + 1]);
+ m256_add_dpbusd_epi32x2(sum1, in0, row1[j], in1, row1[j + 1]);
+ m256_add_dpbusd_epi32x2(sum2, in0, row2[j], in1, row2[j + 1]);
+ m256_add_dpbusd_epi32x2(sum3, in0, row3[j], in1, row3[j + 1]);
+ }
+ }
+ for (; j < (int)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]);
+ 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);
for (IndexType j = 0; j < kNumChunks; ++j)
{
- const __m256i in = input_vector[j];
+ const __m256i in = input_vector[j];
m256_add_dpbusd_epi32(sum0, in, row0[j]);
}
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)
+ int j = 0;
+ if (!canSaturate16x4[i / 4])
+ {
+ for (; j < (int)kNumChunks - 1; j += 2)
+ {
+ const __m128i in0 = input_vector[j];
+ const __m128i in1 = input_vector[j + 1];
+
+ m128_add_dpbusd_epi32x2(sum0, in0, row0[j], in1, row0[j + 1]);
+ m128_add_dpbusd_epi32x2(sum1, in0, row1[j], in1, row1[j + 1]);
+ m128_add_dpbusd_epi32x2(sum2, in0, row2[j], in1, row2[j + 1]);
+ m128_add_dpbusd_epi32x2(sum3, in0, row3[j], in1, row3[j + 1]);
+ }
+ }
+ for (; j < (int)kNumChunks; ++j)
{
- const __m128i in = input_vector[j];
+ 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]);
+ 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);
const auto row0 = reinterpret_cast<const __m128i*>(&weights_[0]);
- for (int j = 0; j < (int)kNumChunks; j += 1)
+ for (int j = 0; j < (int)kNumChunks; ++j)
{
const __m128i in = input_vector[j];
for (IndexType j = 0; j < kNumChunks; ++j) {
__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_row_lo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
+ __m128i extended_row_hi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8);
__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);
for (IndexType j = 0; j < kNumChunks; ++j) {
__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_row_lo = _mm_srai_pi16(_mm_unpacklo_pi8(row_j, row_j), 8);
+ __m64 extended_row_hi = _mm_srai_pi16(_mm_unpackhi_pi8(row_j, row_j), 8);
__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);
PreviousLayer previous_layer_;
alignas(kCacheLineSize) BiasType biases_[kOutputDimensions];
- alignas(kCacheLineSize)
- WeightType weights_[kOutputDimensions * kPaddedInputDimensions];
+ alignas(kCacheLineSize) WeightType weights_[kOutputDimensions * kPaddedInputDimensions];
+ union {
+ uint32_t canSaturate16x4[(kOutputDimensions + 3) / 4];
+ bool canSaturate16[kOutputDimensions];
+ };
};
} // namespace Eval::NNUE::Layers
projects / stockfish / blobdiff