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llama : add support for lora adapters in T5 model (#8938)
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
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@ -13167,13 +13167,13 @@ struct llm_build_context {
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// self-attention
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{
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struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq_enc, cur);
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struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq_enc, cur);
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cb(Qcur, "Qcur", il);
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struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk_enc, cur);
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struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk_enc, cur);
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cb(Kcur, "Kcur", il);
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struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv_enc, cur);
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struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv_enc, 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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@ -13207,7 +13207,7 @@ struct llm_build_context {
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ggml_build_forward_expand(gf, cur);
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cur = ggml_mul_mat(ctx0, model.layers[il].wo_enc, cur);
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cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo_enc, cur);
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cb(cur, "kqv_out", il);
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}
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@ -13281,13 +13281,13 @@ struct llm_build_context {
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// self-attention
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{
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struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
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struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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llm_build_kv_store(ctx0, hparams, cparams, kv_self, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
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@ -13334,7 +13334,7 @@ struct llm_build_context {
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ggml_build_forward_expand(gf, cur);
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cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
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cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
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cb(cur, "kqv_out", il);
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}
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@ -13351,13 +13351,13 @@ struct llm_build_context {
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// cross-attention
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{
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struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq_cross, cur);
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struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq_cross, cur);
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cb(Qcur, "Qcur", il);
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struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk_cross, embd_enc);
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struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk_cross, embd_enc);
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cb(Kcur, "Kcur", il);
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struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv_cross, embd_enc);
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struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv_cross, embd_enc);
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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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@ -13386,7 +13386,7 @@ struct llm_build_context {
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ggml_build_forward_expand(gf, cur);
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cur = ggml_mul_mat(ctx0, model.layers[il].wo_cross, cur);
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cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo_cross, cur);
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cb(cur, "kqv_out", il);
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}
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@ -13443,7 +13443,7 @@ struct llm_build_context {
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cb(cur, "result_norm", -1);
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// lm_head
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cur = ggml_mul_mat(ctx0, model.output, cur);
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cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
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cb(cur, "result_output", -1);
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}
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