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https://github.com/ggml-org/whisper.cpp.git
synced 2026-10-05 05:51:22 +02:00
talk-llama : sync llama.cpp
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@@ -30,17 +30,8 @@ llm_build_llada::llm_build_llada(const llama_model & model, const llm_graph_para
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// self-attention
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{
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// compute separate Q, K, V projections without bias, matching LLaDALlamaBlock
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ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
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n_embd_head, n_head, n_head_kv, il);
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Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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@@ -53,7 +44,7 @@ llm_build_llada::llm_build_llada(const llama_model & model, const llm_graph_para
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cb(Vcur, "Vcur", il);
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cur = build_attn(inp_attn,
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model.layers[il].wo, NULL,
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model.layers[il].wo, NULL, model.layers[il].wo_s,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
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}
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if (il == n_layer - 1 && inp_out_ids) {
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