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llama : add support for lora adapters in T5 model (#8938)

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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
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fairydreaming 2024-08-09 18:53:09 +02:00 committed by GitHub
parent 272e3bd95e
commit 6afd1a99dc
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1 changed files with 13 additions and 13 deletions
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src/llama.cpp
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@ -13167,13 +13167,13 @@ struct llm_build_context {
// self-attention
{
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq_enc, cur);
struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq_enc, cur);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk_enc, cur);
struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk_enc, cur);
cb(Kcur, "Kcur", il);
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv_enc, cur);
struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv_enc, cur);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
@ -13207,7 +13207,7 @@ struct llm_build_context {
ggml_build_forward_expand(gf, cur);
cur = ggml_mul_mat(ctx0, model.layers[il].wo_enc, cur);
cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo_enc, cur);
cb(cur, "kqv_out", il);
}
@ -13281,13 +13281,13 @@ struct llm_build_context {
// self-attention
{
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
llm_build_kv_store(ctx0, hparams, cparams, kv_self, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
@ -13334,7 +13334,7 @@ struct llm_build_context {
ggml_build_forward_expand(gf, cur);
cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
cb(cur, "kqv_out", il);
}
@ -13351,13 +13351,13 @@ struct llm_build_context {
// cross-attention
{
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq_cross, cur);
struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq_cross, cur);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk_cross, embd_enc);
struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk_cross, embd_enc);
cb(Kcur, "Kcur", il);
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv_cross, embd_enc);
struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv_cross, embd_enc);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
@ -13386,7 +13386,7 @@ struct llm_build_context {
ggml_build_forward_expand(gf, cur);
cur = ggml_mul_mat(ctx0, model.layers[il].wo_cross, cur);
cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo_cross, cur);
cb(cur, "kqv_out", il);
}
@ -13443,7 +13443,7 @@ struct llm_build_context {
cb(cur, "result_norm", -1);
// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
cb(cur, "result_output", -1);
}