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Exporting the currently loaded network file

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This PR adds an ability to export any currently loaded network.
The export_net command now takes an optional filename parameter.
If the loaded net is not the embedded net the filename parameter is required.

Two changes were required to support this:

* the "architecture" string, which is really just a some kind of description in the net, is now saved into netDescription on load and correctly saved on export.
* the AffineTransform scrambles weights for some architectures and sparsifies them, such that retrieving the index is hard. This is solved by having a temporary scrambled<->unscrambled index lookup table when loading the network, and the actual index is saved for each individual weight that makes it to canSaturate16. This increases the size of the canSaturate16 entries by 6 bytes.

closes https://github.com/official-stockfish/Stockfish/pull/3456

No functional change
This commit is contained in:
Tomasz Sobczyk
2021-05-07 12:24:12 +02:00
committed by Joost VandeVondele
parent d777ea79ff
commit 58054fd0fa
10 changed files with 159 additions and 27 deletions
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src/nnue/layers/affine_transform.h
+41 -6
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@@ -69,15 +69,19 @@ namespace Stockfish::Eval::NNUE::Layers {
if (!previousLayer.read_parameters(stream)) return false;
for (std::size_t i = 0; i < OutputDimensions; ++i)
biases[i] = read_little_endian<BiasType>(stream);
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
#if !defined (USE_SSSE3)
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
weights[i] = read_little_endian<WeightType>(stream);
#else
weights[
std::unique_ptr<uint32_t[]> indexMap = std::make_unique<uint32_t[]>(OutputDimensions * PaddedInputDimensions);
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) {
const uint32_t scrambledIdx =
(i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
i / PaddedInputDimensions * 4 +
i % 4
] = read_little_endian<WeightType>(stream);
i % 4;
weights[scrambledIdx] = read_little_endian<WeightType>(stream);
indexMap[scrambledIdx] = i;
}
// Determine if eights of weight and input products can be summed using 16bits
// without saturation. We assume worst case combinations of 0 and 127 for all inputs.
@@ -109,7 +113,8 @@ namespace Stockfish::Eval::NNUE::Layers {
IndexType idx = maxK / 2 * OutputDimensions * 4 + maxK % 2;
sum[sign == -1] -= w[idx];
canSaturate16.add(j, i + maxK / 2 * 4 + maxK % 2 + x * 2, w[idx]);
const uint32_t scrambledIdx = idx + i * OutputDimensions + j * 4 + x * 2;
canSaturate16.add(j, i + maxK / 2 * 4 + maxK % 2 + x * 2, w[idx], indexMap[scrambledIdx]);
w[idx] = 0;
}
}
@@ -125,6 +130,34 @@ namespace Stockfish::Eval::NNUE::Layers {
return !stream.fail();
}
// Write network parameters
bool write_parameters(std::ostream& stream) const {
if (!previousLayer.write_parameters(stream)) return false;
for (std::size_t i = 0; i < OutputDimensions; ++i)
write_little_endian<BiasType>(stream, biases[i]);
#if !defined (USE_SSSE3)
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
write_little_endian<WeightType>(stream, weights[i]);
#else
std::unique_ptr<WeightType[]> unscrambledWeights = std::make_unique<WeightType[]>(OutputDimensions * PaddedInputDimensions);
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) {
unscrambledWeights[i] =
weights[
(i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
i / PaddedInputDimensions * 4 +
i % 4
];
}
for (int i = 0; i < canSaturate16.count; ++i)
unscrambledWeights[canSaturate16.ids[i].wIdx] = canSaturate16.ids[i].w;
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
write_little_endian<WeightType>(stream, unscrambledWeights[i]);
#endif
return !stream.fail();
}
// Forward propagation
const OutputType* propagate(
const TransformedFeatureType* transformedFeatures, char* buffer) const {
@@ -444,12 +477,14 @@ namespace Stockfish::Eval::NNUE::Layers {
struct CanSaturate {
int count;
struct Entry {
uint32_t wIdx;
uint16_t out;
uint16_t in;
int8_t w;
} ids[PaddedInputDimensions * OutputDimensions * 3 / 4];
void add(int i, int j, int8_t w) {
void add(int i, int j, int8_t w, uint32_t wIdx) {
ids[count].wIdx = wIdx;
ids[count].out = i;
ids[count].in = j;
ids[count].w = w;