97 lines
3.2 KiB
C++
97 lines
3.2 KiB
C++
#include "models.h"
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llm_build_t5_enc::llm_build_t5_enc(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
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const int64_t n_embd_head = hparams.n_embd_head_v;
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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ggml_tensor * cur;
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ggml_tensor * inpL;
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inpL = build_inp_embd(model.tok_embd);
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ggml_tensor * pos_bucket_enc = build_inp_pos_bucket_enc();
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auto * inp_attn = build_attn_inp_no_cache();
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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for (int il = 0; il < n_layer; ++il) {
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ggml_tensor * inpSA = inpL;
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// norm
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cur = build_norm(inpL,
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model.layers[il].attn_norm_enc, NULL,
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LLM_NORM_RMS, il);
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cb(cur, "attn_norm", il);
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// self-attention
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{
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ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq_enc, cur);
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cb(Qcur, "Qcur", il);
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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk_enc, cur);
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cb(Kcur, "Kcur", il);
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ggml_tensor * Vcur = build_lora_mm(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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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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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;
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ggml_tensor * kq_b = build_pos_bias(pos_bucket_enc, attn_rel_b);
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cur = build_attn(inp_attn,
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model.layers[il].wo_enc, nullptr,
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Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);
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cb(cur, "kqv_out", il);
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}
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if (il == n_layer - 1 && inp_out_ids) {
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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}
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ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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// feed-forward network
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{
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cur = build_norm(ffn_inp,
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model.layers[il].ffn_norm_enc, NULL,
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LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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// T5 uses relu, flan-T5 uses gelu-gated
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cur = build_ffn(cur,
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model.layers[il].ffn_up_enc, NULL, NULL,
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model.layers[il].ffn_gate_enc, NULL, NULL,
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model.layers[il].ffn_down_enc, NULL, NULL,
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NULL,
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model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
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model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ,
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il);
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cb(cur, "ffn_out", il);
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}
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cur = ggml_add(ctx0, cur, ffn_inp);
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cb(cur, "ffn_out", il);
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cur = build_cvec(cur, il);
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cb(cur, "l_out", il);
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// input for next layer
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inpL = cur;
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}
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cur = inpL;
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cb(cur, "result_embd", -1);
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cur = build_norm(cur,
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model.output_norm_enc, NULL,
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LLM_NORM_RMS, -1);
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cb(cur, "result_norm", -1);
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res->t_embd = cur;
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ggml_build_forward_expand(gf, cur);
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}
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