mirror of
https://github.com/ggml-org/whisper.cpp.git
synced 2026-10-04 13:31:18 +02:00
talk-llama : sync llama.cpp
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@@ -9,7 +9,7 @@ llm_build_gemma_embedding::llm_build_gemma_embedding(const llama_model & model,
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inpL = build_inp_embd(model.tok_embd);
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// important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
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// important: do not normalize weights for raw embeddings input (i.e. encoded image embeddings)
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inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);
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cb(inpL, "inp_scaled", -1);
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@@ -31,18 +31,8 @@ llm_build_gemma_embedding::llm_build_gemma_embedding(const llama_model & model,
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// self-attention
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{
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// compute Q and K and RoPE them
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ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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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 = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
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cb(Qcur, "Qcur_normed", il);
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@@ -65,7 +55,7 @@ llm_build_gemma_embedding::llm_build_gemma_embedding(const llama_model & model,
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cur =
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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, il);
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}
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