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talk-llama : sync llama.cpp
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@@ -1,12 +1,10 @@
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#include "models.h"
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llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
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const int64_t n_embd_head = hparams.n_embd_head_v;
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const int64_t n_embd_head = hparams.n_embd_head_v();
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const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
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ggml_tensor * cur;
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ggml_tensor * inpL;
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@@ -129,9 +127,17 @@ llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params
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// feed-forward network
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if (hparams.moe_every_n_layers > 0 && il % hparams.moe_every_n_layers == 1) {
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// MoE branch
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cur = build_moe_ffn(cur, model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, nullptr,
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model.layers[il].ffn_down_exps, nullptr, hparams.n_expert, hparams.n_expert_used,
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LLM_FFN_GELU, false, false, 0.0f, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
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cur = build_moe_ffn(cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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nullptr,
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model.layers[il].ffn_down_exps,
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nullptr,
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hparams.n_expert, hparams.n_expert_used,
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LLM_FFN_GELU, false,
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hparams.expert_weights_scale,
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LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
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il);
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cb(cur, "ffn_moe_out", il);
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} else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE ||
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model.arch == LLM_ARCH_JINA_BERT_V3) {
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