vbatts/llama.cpp
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llama : change naming to prefer "_enc" suffix

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Georgi Gerganov 2024-07-04 11:24:13 +03:00
parent d40c9a1d3a
commit 03ab5dd67c
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GPG key ID: 449E073F9DC10735
1 changed files with 103 additions and 98 deletions
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src/llama.cpp
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@ -2265,6 +2265,7 @@ struct llama_cparams {
void * cb_eval_user_data;
};
// TODO: separate into "llama_layer_enc" and "llama_layer_dec"
struct llama_layer {
// normalization
struct ggml_tensor * attn_norm;
@ -2282,8 +2283,8 @@ struct llama_layer {
struct ggml_tensor * attn_sub_norm;
struct ggml_tensor * attn_post_norm;
struct ggml_tensor * ffn_sub_norm;
struct ggml_tensor * cross_attn_norm;
struct ggml_tensor * enc_attn_norm;
struct ggml_tensor * attn_norm_cross;
struct ggml_tensor * attn_norm_enc;
// attention
struct ggml_tensor * wq;
@ -2295,14 +2296,14 @@ struct llama_layer {
struct ggml_tensor * wq_b;
struct ggml_tensor * wkv_a_mqa;
struct ggml_tensor * wkv_b;
struct ggml_tensor * cross_wq;
struct ggml_tensor * cross_wk;
struct ggml_tensor * cross_wv;
struct ggml_tensor * cross_wo;
struct ggml_tensor * enc_wq;
struct ggml_tensor * enc_wk;
struct ggml_tensor * enc_wv;
struct ggml_tensor * enc_wo;
struct ggml_tensor * wq_cross;
struct ggml_tensor * wk_cross;
struct ggml_tensor * wv_cross;
struct ggml_tensor * wo_cross;
struct ggml_tensor * wq_enc;
struct ggml_tensor * wk_enc;
struct ggml_tensor * wv_enc;
struct ggml_tensor * wo_enc;
// attention bias
struct ggml_tensor * bq;
@ -2312,9 +2313,9 @@ struct llama_layer {
struct ggml_tensor * bqkv;
// relative position bias
struct ggml_tensor * rel_attn_b;
struct ggml_tensor * enc_rel_attn_b;
struct ggml_tensor * cross_rel_attn_b;
struct ggml_tensor * attn_rel_b;
struct ggml_tensor * attn_rel_b_enc;
struct ggml_tensor * attn_rel_b_cross;
// normalization
struct ggml_tensor * ffn_norm;
@ -2323,15 +2324,15 @@ struct llama_layer {
struct ggml_tensor * layer_out_norm;
struct ggml_tensor * layer_out_norm_b;
struct ggml_tensor * ffn_norm_exps;
struct ggml_tensor * enc_ffn_norm;
struct ggml_tensor * ffn_norm_enc;
// ff
struct ggml_tensor * ffn_gate; // w1
struct ggml_tensor * ffn_down; // w2
struct ggml_tensor * ffn_up; // w3
struct ggml_tensor * enc_ffn_gate;
struct ggml_tensor * enc_ffn_down;
struct ggml_tensor * enc_ffn_up;
struct ggml_tensor * ffn_gate_enc;
struct ggml_tensor * ffn_down_enc;
struct ggml_tensor * ffn_up_enc;
// ff MoE
struct ggml_tensor * ffn_gate_inp;
@ -2565,7 +2566,7 @@ struct llama_model {
struct ggml_tensor * output_norm_b;
struct ggml_tensor * output;
struct ggml_tensor * output_b;
struct ggml_tensor * enc_output_norm;
struct ggml_tensor * output_norm_enc;
std::vector<llama_layer> layers;
@ -2721,8 +2722,8 @@ struct llama_context {
struct ggml_tensor * inp_s_mask; // F32 [1, n_kv]
struct ggml_tensor * inp_s_seq; // I32 [n_kv, n_batch]
struct ggml_tensor * inp_pos_bucket; // I32 [n_batch|n_kv, n_batch]
struct ggml_tensor * inp_enc_output; // F32 [n_embd, n_enc_outputs]
struct ggml_tensor * inp_cross_KQ_mask; // F32 [n_enc_outputs, n_batch]
struct ggml_tensor * inp_embd_enc; // F32 [n_embd, n_outputs_enc]
struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
// control vectors
struct llama_control_vector cvec;
@ -7080,8 +7081,9 @@ static bool llm_load_tensors(
// output
{
model.enc_output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm_enc = ml.create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
// if output is NULL, init from the input tok embed
if (model.output == NULL) {
@ -7095,35 +7097,35 @@ static bool llm_load_tensors(
auto & layer = model.layers[i];
layer.enc_attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd});
layer.enc_rel_attn_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {hparams.n_head, hparams.n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.attn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd});
layer.attn_rel_b_enc = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {hparams.n_head, hparams.n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.enc_wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
layer.enc_wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
layer.enc_wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
layer.enc_wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
layer.wq_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
layer.wk_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
layer.wv_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
layer.wo_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
layer.enc_ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd});
layer.enc_ffn_gate = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.enc_ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.enc_ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd});
layer.ffn_gate_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_down_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff});
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_ATTN_NORM, "weight", i), {n_embd});
layer.rel_attn_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {hparams.n_head, hparams.n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.attn_rel_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {hparams.n_head, hparams.n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
layer.cross_attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_cross = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM, "weight", i), {n_embd});
// this tensor seems to be unused in HF transformers implementation
layer.cross_rel_attn_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {hparams.n_head, hparams.n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.attn_rel_b_cross = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {hparams.n_head, hparams.n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.cross_wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
layer.cross_wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
layer.cross_wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
layer.cross_wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
layer.wq_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
layer.wk_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
layer.wv_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
layer.wo_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd});
layer.ffn_gate = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
@ -7922,7 +7924,7 @@ struct llm_build_context {
const int32_t n_tokens;
const int32_t n_kv; // size of KV cache to consider (n_kv <= kv_self.size)
const int32_t n_outputs;
const int32_t n_enc_outputs;
const int32_t n_outputs_enc;
const int32_t kv_head; // index of where we store new KV data in the cache
const int32_t n_ctx_orig;
@ -7972,7 +7974,7 @@ struct llm_build_context {
n_tokens (batch.n_tokens),
n_kv (worst_case ? kv_self.size : kv_self.n),
n_outputs (worst_case ? n_tokens : lctx.n_outputs),
n_enc_outputs (worst_case ? n_tokens : lctx.encoder_output.size() / hparams.n_embd),
n_outputs_enc (worst_case ? n_tokens : lctx.encoder_output.size() / hparams.n_embd),
kv_head (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head),
n_ctx_orig (cparams.n_ctx_orig_yarn),
flash_attn (cparams.flash_attn),
@ -8005,8 +8007,8 @@ struct llm_build_context {
lctx.inp_s_mask = nullptr;
lctx.inp_s_seq = nullptr;
lctx.inp_pos_bucket = nullptr;
lctx.inp_enc_output = nullptr;
lctx.inp_cross_KQ_mask = nullptr;
lctx.inp_embd_enc = nullptr;
lctx.inp_KQ_mask_cross = nullptr;
}
void free() {
@ -8259,7 +8261,7 @@ struct llm_build_context {
return gf;
}
struct ggml_tensor * llm_build_inp_rel_pos_bucket(bool causal) {
struct ggml_tensor * llm_build_pos_bucket(bool causal) {
if (causal) {
lctx.inp_pos_bucket = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_tokens);
} else {
@ -8272,11 +8274,11 @@ struct llm_build_context {
return lctx.inp_pos_bucket;
}
struct ggml_tensor * llm_build_rel_pos_bias(struct ggml_tensor * pos_bucket, struct ggml_tensor * rel_attn_b) {
struct ggml_tensor * llm_build_pos_bias(struct ggml_tensor * pos_bucket, struct ggml_tensor * attn_rel_b) {
struct ggml_tensor * pos_bucket_1d = ggml_view_1d(ctx0, pos_bucket, pos_bucket->ne[0] * pos_bucket->ne[1], 0);
cb(pos_bucket_1d, "pos_bucket_1d", -1);
struct ggml_tensor * pos_bias = ggml_get_rows(ctx0, rel_attn_b, pos_bucket_1d);
struct ggml_tensor * pos_bias = ggml_get_rows(ctx0, attn_rel_b, pos_bucket_1d);
cb(pos_bias, "pos_bias", -1);
pos_bias = ggml_view_3d(ctx0, pos_bias, pos_bias->ne[0], lctx.inp_pos_bucket->ne[0], lctx.inp_pos_bucket->ne[1], ggml_element_size(pos_bias) * pos_bias->ne[0], ggml_element_size(pos_bias) * pos_bias->ne[0] * lctx.inp_pos_bucket->ne[0], 0);
@ -8291,19 +8293,19 @@ struct llm_build_context {
return pos_bias;
}
struct ggml_tensor * llm_build_inp_enc_output() {
struct ggml_tensor * llm_build_inp_embd_enc() {
const int64_t n_embd = hparams.n_embd;
lctx.inp_enc_output = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_enc_outputs);
ggml_set_input(lctx.inp_enc_output);
cb(lctx.inp_enc_output, "enc_output", -1);
return lctx.inp_enc_output;
lctx.inp_embd_enc = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_outputs_enc);
ggml_set_input(lctx.inp_embd_enc);
cb(lctx.inp_embd_enc, "embd_enc", -1);
return lctx.inp_embd_enc;
}
struct ggml_tensor * llm_build_inp_cross_KQ_mask() {
lctx.inp_cross_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_enc_outputs, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
ggml_set_input(lctx.inp_cross_KQ_mask);
cb(lctx.inp_cross_KQ_mask, "enc_mask", -1);
return lctx.inp_cross_KQ_mask;
struct ggml_tensor * llm_build_inp_KQ_mask_cross() {
lctx.inp_KQ_mask_cross = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_outputs_enc, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
ggml_set_input(lctx.inp_KQ_mask_cross);
cb(lctx.inp_KQ_mask_cross, "KQ_mask_cross", -1);
return lctx.inp_KQ_mask_cross;
}
struct ggml_cgraph * build_llama() {
@ -12629,29 +12631,29 @@ struct llm_build_context {
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
if (lctx.is_encoding) {
struct ggml_tensor * enc_pos_buckets = llm_build_inp_rel_pos_bucket(false);
struct ggml_tensor * pos_bucket_enc = llm_build_pos_bucket(false);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * enc_KQ_mask = build_inp_KQ_mask(false);
struct ggml_tensor * KQ_mask_enc = build_inp_KQ_mask(false);
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].enc_attn_norm, NULL,
model.layers[il].attn_norm_enc, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
// self-attention
{
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].enc_wq, cur);
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq_enc, cur);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].enc_wk, cur);
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk_enc, cur);
cb(Kcur, "Kcur", il);
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].enc_wv, cur);
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv_enc, cur);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
@ -12663,12 +12665,12 @@ struct llm_build_context {
struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
cb(kq, "kq", il);
struct ggml_tensor * rel_attn_b = model.layers[il].enc_rel_attn_b ? model.layers[il].enc_rel_attn_b : model.layers[0].enc_rel_attn_b;
struct ggml_tensor * pos_bias = llm_build_rel_pos_bias(enc_pos_buckets, rel_attn_b);
struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc;
struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_enc, attn_rel_b);
struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias);
cb(kq_b, "kq_b", il);
kq = ggml_soft_max_ext(ctx0, kq_b, enc_KQ_mask, 1.0f, hparams.f_max_alibi_bias);
kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_enc, 1.0f, hparams.f_max_alibi_bias);
cb(kq, "kq_soft_max_ext", il);
struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens)));
@ -12685,7 +12687,7 @@ struct llm_build_context {
ggml_build_forward_expand(gf, cur);
cur = ggml_mul_mat(ctx0, model.layers[il].enc_wo, cur);
cur = ggml_mul_mat(ctx0, model.layers[il].wo_enc, cur);
cb(cur, "kqv_out", il);
}
@ -12703,18 +12705,18 @@ struct llm_build_context {
// feed-forward network
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].enc_ffn_norm, NULL,
model.layers[il].ffn_norm_enc, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
// T5 uses relu, flan-T5 uses gelu-gated
cur = llm_build_ffn(ctx0, cur,
model.layers[il].enc_ffn_up, NULL, NULL,
model.layers[il].enc_ffn_gate, NULL, NULL,
model.layers[il].enc_ffn_down, NULL, NULL,
model.layers[il].ffn_up_enc, NULL, NULL,
model.layers[il].ffn_gate_enc, NULL, NULL,
model.layers[il].ffn_down_enc, NULL, NULL,
NULL,
model.layers[il].enc_ffn_gate ? LLM_FFN_GELU : LLM_FFN_RELU,
model.layers[il].enc_ffn_gate ? LLM_FFN_PAR : LLM_FFN_SEQ,
model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ,
cb, il);
cb(cur, "ffn_out", il);
}
@ -12736,15 +12738,15 @@ struct llm_build_context {
cb(cur, "result_embd", -1);
cur = llm_build_norm(ctx0, cur, hparams,
model.enc_output_norm, NULL,
model.output_norm_enc, NULL,
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
} else {
struct ggml_tensor * enc_output = llm_build_inp_enc_output();
struct ggml_tensor * dec_pos_buckets = llm_build_inp_rel_pos_bucket(true);
struct ggml_tensor * embd_enc = llm_build_inp_embd_enc();
struct ggml_tensor * pos_buckets_dec = llm_build_pos_bucket(true);
struct ggml_tensor * dec_KQ_mask = build_inp_KQ_mask();
struct ggml_tensor * cross_KQ_mask = llm_build_inp_cross_KQ_mask();
struct ggml_tensor * KQ_mask_dec = build_inp_KQ_mask();
struct ggml_tensor * KQ_mask_cross = llm_build_inp_KQ_mask_cross();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
@ -12791,12 +12793,12 @@ struct llm_build_context {
struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
cb(kq, "kq", il);
struct ggml_tensor * rel_attn_b = model.layers[il].rel_attn_b ? model.layers[il].rel_attn_b : model.layers[0].rel_attn_b;
struct ggml_tensor * pos_bias = llm_build_rel_pos_bias(dec_pos_buckets, rel_attn_b);
struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b;
struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_buckets_dec, attn_rel_b);
struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias);
cb(kq_b, "kq_b", il);
kq = ggml_soft_max_ext(ctx0, kq_b, dec_KQ_mask, 1.0f, hparams.f_max_alibi_bias);
kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_dec, 1.0f, hparams.f_max_alibi_bias);
cb(kq, "kq_soft_max_ext", il);
struct ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
@ -12821,23 +12823,23 @@ struct llm_build_context {
// norm
cur = llm_build_norm(ctx0, cur, hparams,
model.layers[il].cross_attn_norm, NULL,
model.layers[il].attn_norm_cross, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "cross_attn_norm", il);
cb(cur, "attn_norm_cross", il);
// cross-attention
{
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].cross_wq, cur);
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq_cross, cur);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].cross_wk, enc_output);
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk_cross, embd_enc);
cb(Kcur, "Kcur", il);
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].cross_wv, enc_output);
struct ggml_tensor * Vcur = ggml_mul_mat(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);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_enc_outputs);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_outputs_enc);
struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
@ -12845,13 +12847,13 @@ struct llm_build_context {
struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
cb(kq, "kq", il);
kq = ggml_soft_max_ext(ctx0, kq, cross_KQ_mask, 1.0f, hparams.f_max_alibi_bias);
kq = ggml_soft_max_ext(ctx0, kq, KQ_mask_cross, 1.0f, hparams.f_max_alibi_bias);
cb(kq, "kq_soft_max_ext", il);
struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_enc_outputs)));
struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_outputs_enc)));
cb(v, "v", il);
struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_enc_outputs, n_embd_head, n_head_kv), kq);
struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_outputs_enc, n_embd_head, n_head_kv), kq);
cb(kqv, "kqv", il);
struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
@ -12862,7 +12864,7 @@ struct llm_build_context {
ggml_build_forward_expand(gf, cur);
cur = ggml_mul_mat(ctx0, model.layers[il].cross_wo, cur);
cur = ggml_mul_mat(ctx0, model.layers[il].wo_cross, cur);
cb(cur, "kqv_out", il);
}
@ -12891,8 +12893,8 @@ struct llm_build_context {
model.layers[il].ffn_gate, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
NULL,
model.layers[il].enc_ffn_gate ? LLM_FFN_GELU : LLM_FFN_RELU,
model.layers[il].enc_ffn_gate ? LLM_FFN_PAR : LLM_FFN_SEQ,
model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ,
cb, il);
cb(cur, "ffn_out", il);
}
@ -13630,17 +13632,20 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
}
}
if (!lctx.is_encoding && lctx.inp_enc_output) {
ggml_backend_tensor_set(lctx.inp_enc_output, lctx.encoder_output.data(), 0, lctx.encoder_output.size() * ggml_element_size(lctx.inp_enc_output));
if (!lctx.is_encoding && lctx.inp_embd_enc) {
assert(lctx.inp_embd_enc->type == GGML_TYPE_F32);
assert(ggml_nelements(lctx.inp_embd_enc) == lctx.encoder_output.size());
ggml_backend_tensor_set(lctx.inp_embd_enc, lctx.encoder_output.data(), 0, ggml_nbytes(lctx.inp_embd_enc));
}
if (!lctx.is_encoding && lctx.inp_cross_KQ_mask) {
if (!lctx.is_encoding && lctx.inp_KQ_mask_cross) {
const int64_t n_encoder_output = lctx.encoder_output.size() / hparams.n_embd;
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cross_KQ_mask->buffer));
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_cross->buffer));
float * data = (float *) lctx.inp_cross_KQ_mask->data;
float * data = (float *) lctx.inp_KQ_mask_cross->data;
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
@ -14127,7 +14132,7 @@ static int llama_encode_internal(
lctx.output_ids[i] = i;
}
lctx.inp_enc_output = NULL;
lctx.inp_embd_enc = NULL;
for (uint32_t cur_token = 0; cur_token < n_tokens_all; cur_token += n_ubatch) {
const uint32_t n_tokens = std::min(n_ubatch, n_tokens_all - cur_token);