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#include "models.h"
llm_build_t5_dec::llm_build_t5_dec(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
//const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
ggml_tensor * embd_enc = build_inp_cross_embd();
ggml_tensor * pos_bucket_dec = build_inp_pos_bucket_dec();
const int64_t n_outputs_enc = embd_enc->ne[1];
auto * inp_attn_self = build_attn_inp_kv();
auto * inp_attn_cross = build_attn_inp_cross();
ggml_tensor * inp_out_ids = build_inp_out_ids();
const int64_t dec_n_layer = hparams.dec_n_layer;
for (int il = 0; il < dec_n_layer; ++il) {
ggml_tensor * inpSA = inpL;
// norm
cur = build_norm(inpL,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
// self-attention
{
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b;
ggml_tensor * kq_b = build_pos_bias(pos_bucket_dec, attn_rel_b);
cur = build_attn(inp_attn_self,
model.layers[il].wo, model.layers[il].bo,
Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);
cb(cur, "kqv_out", il);
}
cur = ggml_add(ctx0, cur, inpSA);
cb(cur, "cross_inp", il);
ggml_tensor * inpCA = cur;
// norm
cur = build_norm(cur,
model.layers[il].attn_norm_cross, NULL,
LLM_NORM_RMS, il);
cb(cur, "attn_norm_cross", il);
// cross-attention
{
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq_cross, cur);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk_cross, embd_enc);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(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_outputs_enc);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_outputs_enc);
cur = build_attn(inp_attn_cross,
model.layers[il].wo_cross, nullptr,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
cb(cur, "kqv_out", il);
//ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
//ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
//ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
//cb(kq, "kq", il);
//kq = ggml_soft_max_ext(ctx0, kq, KQ_mask_cross, 1.0f, hparams.f_max_alibi_bias);
//cb(kq, "kq_soft_max_ext", il);
//ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_outputs_enc)));
//cb(v, "v", il);
//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);
//ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
//cb(kqv_merged, "kqv_merged", il);
//cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
//cb(cur, "kqv_merged_cont", il);
//ggml_build_forward_expand(gf, cur);
//cur = build_lora_mm(model.layers[il].wo_cross, cur);
//cb(cur, "kqv_out", il);
}
if (il == dec_n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpCA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
{
cur = build_norm(ffn_inp,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
// T5 uses relu, flan-T5 uses gelu-gated
cur = build_ffn(cur,
model.layers[il].ffn_up, NULL, NULL,
model.layers[il].ffn_gate, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
NULL,
model.layers[il].ffn_gate ? LLM_FFN_GELU : LLM_FFN_RELU,
model.layers[il].ffn_gate ? LLM_FFN_PAR : LLM_FFN_SEQ,
il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cb(cur, "result_embd", -1);
cur = build_norm(cur,
model.output_norm, NULL,
LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
// lm_head
cur = build_lora_mm(model.output, cur);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
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