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Support attention_bias on LLaMA architecture #4283

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merged 3 commits into from
Dec 1, 2023
Merged

Support attention_bias on LLaMA architecture #4283

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c48679a
Support attention_bias on LLaMA architecture
CausalLM Dec 1, 2023
e192572
check existence of qkvo bias while loading llama models
CausalLM Dec 1, 2023
b1efaed
Update llama.cpp
CausalLM Dec 1, 2023
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29 changes: 25 additions & 4 deletions llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1248,6 +1248,9 @@ struct llama_layer {
struct ggml_tensor * wqkv;

// attention bias
struct ggml_tensor * bq;
struct ggml_tensor * bk;
struct ggml_tensor * bv;
struct ggml_tensor * bo;
struct ggml_tensor * bqkv;

Expand Down Expand Up @@ -2781,6 +2784,11 @@ static void llm_load_tensors(
layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);

layer.bq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, backend);
layer.bk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, backend);
layer.bv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, backend);
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend);
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I think you need to crease these tensors only for the models that have them, otherwise this breaks llama models.

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Correct

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fixed, tested on llama2, cuda & cpu


layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);

Expand All @@ -2791,8 +2799,9 @@ static void llm_load_tensors(
if (backend == GGML_BACKEND_GPU) {
vram_weights +=
ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.bq) +
ggml_nbytes(layer.bk) + ggml_nbytes(layer.bv) + ggml_nbytes(layer.bo) +
ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
}
}
} break;
Expand Down Expand Up @@ -3891,13 +3900,25 @@ struct llm_build_context {
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
if (model.layers[il].bq) {
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
cb(Qcur, "Qcur", il);
}

struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
if (model.layers[il].bk) {
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
cb(Kcur, "Kcur", il);
}

struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);

if (model.layers[il].bv) {
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
cb(Vcur, "Vcur", il);
}

Qcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
Expand All @@ -3915,7 +3936,7 @@ struct llm_build_context {
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);

cur = llm_build_kqv(ctx0, hparams, kv_self,
model.layers[il].wo, NULL,
model.layers[il].wo, model.layers[il].bo,
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
cb(cur, "kqv_out", il);
}
Expand Down