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Weights file format

The NN weights file is in Google Protocol Buffers1 format.

The schema definition is located in the lczero-common repository.

NN format description

The network format description is contained in weights.format().network_format() submessage.

  • It’s safe to assume that NetworkStructure 0, 1 or 2 will never be passed to the backend. I.e.
    • if weights.format().network_format().network() == NETWORK_CLASSICAL_WITH_HEADFORMAT (3), residual blocks don’t have SE layers.
    • if weights.format().network_format().network() == NETWORK_SE_WITH_HEADFORMAT (4), residual blocks has SE layers.
    • Otherwise the backend should throw an exception.

weights.format().network_format().policy(), weights.format().network_format().value() and weights.format().network_format().moves_left() must be checked, and in the case of the unsupported value, throw an exception.

weights.format().input() should be copied to backend’s NetworkCapabilities.

Weights encoding

Number of FILTERS is LayerAdapter(weights.weights().input().biases()).size() / 112. Number of BLOCKS is weights.weights().residual_size().

NN parameters encoding

Internally, the parameters are encoded as 16-bit quantized floating point numbers, in Weights::Layer protobuf submessage.

To use them as an array of floats, use LayerAdapter class from utils/weights_adapter.h.

For example here is how to access biases of the input convlayer:

const auto& input = weights.weights().input();
LayerAdapter biases(input.biases());
// Now use biases as if it was a vector<float>, e.g.
std::cout << biases.size();
for (auto x: biases) { /* do something */ }

Tensors are stored linearized to a vector. Noone remembers anymore which transposition is used for it, but most likely it’s HWC (e.g. the conv kernel is 3×3×FILTERS). When you figure that out, feel free to update this document.

Convolution layers

Convolution layers are encoded in Weights::ConvBlock for example, input convolution is weights.weights().input() of this type:

  message ConvBlock {
    optional Layer weights = 1;
    optional Layer biases = 2;
    optional Layer bn_means = 3;
    optional Layer bn_stddivs = 4;
    optional Layer bn_gammas = 5;
    optional Layer bn_betas = 6;

weights contains weights, and biases contains biases. All bn_ are not used anymore, always zero (they were used for batch normalization but now it’s fused).

Residual blocks

In weights.weights().residual() repeated submessage field, this is pretty self-describing.

  message Residual {
    optional ConvBlock conv1 = 1;
    optional ConvBlock conv2 = 2;
    optional SEunit se = 3;

SEunit is also quite clear:

  message SEunit {
    optional Layer w1 = 1;
    optional Layer b1 = 2;
    optional Layer w2 = 3;
    optional Layer b2 = 4;

SE_CHANNELS can be compute as LayerAdapter(se_unit.b1()).size();

Policy head

For POLICY_CLASSICAL, POLICY_CONV_SIZE is computed as LayerAdapter(weights.policy().biases()).size(). Also for POLICY_CLASSICAL, the policy1 field is unused.

  // Policy head
  // Extra convolution for AZ-style policy head
  optional ConvBlock policy1 = 11;
  optional ConvBlock policy = 3;
  optional Layer ip_pol_w = 4;
  optional Layer ip_pol_b = 5;

Value head

Probably clear..

Moves left head

  // Moves left head
  optional ConvBlock moves_left = 12;
  optional Layer ip1_mov_w = 13;
  optional Layer ip1_mov_b = 14;
  optional Layer ip2_mov_w = 15;
  optional Layer ip2_mov_b = 16;

MLH_CHANNELS = LayerAdapter(weights.moves_left().biases()).size()

FC_SIZE = LayerAdapter(weights.ip1_mov_b()).size()

LegacyWeights wrapper

There is a LegacyWeights struct which was used to contain weights before Protocol Buffer weights format was introduced. This struct was planned to be deprecated and later removed.

However, it seems that it adds considerable amount of convenience (as working with std::vector<float> is more familiar) so it’s probably here to say.

  1. https://developers.google.com/protocol-buffers ↩︎

Last Updated: 2020-06-04