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#include "model.h"
using namespace std;
namespace amunmt {
namespace CPU {
namespace dl4mt {
Weights::Embeddings::Embeddings(const NpzConverter& model, const std::string &key)
: E_(model[key])
{}
Weights::Embeddings::Embeddings(const NpzConverter& model, const std::vector<std::pair<std::string, bool>> keys)
: E_(model.getFirstOfMany(keys))
{}
Weights::GRU::GRU(const NpzConverter& model, const std::vector<std::string> &keys)
: W_(model[keys.at(0)]),
B_(model(keys.at(1), true)),
U_(model[keys.at(2)]),
Wx_(model[keys.at(3)]),
Bx1_(model(keys.at(4), true)),
Bx2_(Bx1_.rows(), Bx1_.columns()),
Ux_(model[keys.at(5)]),
Gamma_1_(model[keys.at(6)]),
Gamma_2_(model[keys.at(7)])
{
const_cast<mblas::Matrix&>(Bx2_) = 0.0f;
}
//////////////////////////////////////////////////////////////////////////////
Weights::DecInit::DecInit(const NpzConverter& model)
: Wi_(model["ff_state_W"]),
Bi_(model("ff_state_b", true)),
Gamma_(model["ff_state_gamma"])
{}
Weights::DecGRU2::DecGRU2(const NpzConverter& model)
: W_(model["decoder_Wc"]),
B_(model("decoder_b_nl", true)),
U_(model["decoder_U_nl"]),
Wx_(model["decoder_Wcx"]),
Bx2_(model("decoder_bx_nl", true)),
Bx1_(Bx2_.rows(), Bx2_.columns()),
Ux_(model["decoder_Ux_nl"]),
Gamma_1_(model["decoder_cell2_gamma1"]),
Gamma_2_(model["decoder_cell2_gamma2"])
{
const_cast<mblas::Matrix&>(Bx1_) = 0.0f;
}
Weights::DecAttention::DecAttention(const NpzConverter& model)
: V_(model("decoder_U_att", true)),
W_(model["decoder_W_comb_att"]),
B_(model("decoder_b_att", true)),
U_(model["decoder_Wc_att"]),
C_(model["decoder_c_tt"]), // scalar?
Gamma_1_(model["decoder_att_gamma1"]),
Gamma_2_(model["decoder_att_gamma2"])
{}
Weights::DecSoftmax::DecSoftmax(const NpzConverter& model)
: W1_(model["ff_logit_lstm_W"]),
B1_(model("ff_logit_lstm_b", true)),
W2_(model["ff_logit_prev_W"]),
B2_(model("ff_logit_prev_b", true)),
W3_(model["ff_logit_ctx_W"]),
B3_(model("ff_logit_ctx_b", true)),
W4_(model.getFirstOfMany({std::pair<std::string, bool>(std::string("ff_logit_W"), false),
std::make_pair(std::string("Wemb_dec"), true)})),
B4_(model("ff_logit_b", true)),
Gamma_0_(model["ff_logit_l1_gamma0"]),
Gamma_1_(model["ff_logit_l1_gamma1"]),
Gamma_2_(model["ff_logit_l1_gamma2"])
{}
//////////////////////////////////////////////////////////////////////////////
Weights::Weights(const NpzConverter& model, size_t)
: encEmbeddings_(model, "Wemb"),
encForwardGRU_(model, {"encoder_W", "encoder_b", "encoder_U", "encoder_Wx", "encoder_bx",
"encoder_Ux", "encoder_gamma1", "encoder_gamma2"}),
encBackwardGRU_(model, {"encoder_r_W", "encoder_r_b", "encoder_r_U", "encoder_r_Wx",
"encoder_r_bx", "encoder_r_Ux", "encoder_r_gamma1", "encoder_r_gamma2"}),
decEmbeddings_(model, std::vector<std::pair<std::string, bool>>({std::make_pair(std::string("Wemb_dec"), false),
std::make_pair(std::string("Wemb"), false)})),
decInit_(model),
decGru1_(model, {"decoder_W", "decoder_b", "decoder_U", "decoder_Wx", "decoder_bx", "decoder_Ux",
"decoder_cell1_gamma1", "decoder_cell1_gamma2"}),
decGru2_(model),
decAttention_(model),
decSoftmax_(model)
{}
} // namespace dl4mt
} // namespace cpu
} // namespace amunmt
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