1#include "models.h"
2
3llm_build_plamo::llm_build_plamo(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
4 const int64_t n_embd_head = hparams.n_embd_head_v;
5
6 GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
7 GGML_ASSERT(n_embd_head == hparams.n_rot);
8
9 ggml_tensor * cur;
10 ggml_tensor * inpL;
11
12 inpL = build_inp_embd(tok_embd: model.tok_embd);
13
14 // inp_pos - contains the positions
15 ggml_tensor * inp_pos = build_inp_pos();
16
17 auto * inp_attn = build_attn_inp_kv();
18
19 ggml_tensor * inp_out_ids = build_inp_out_ids();
20
21 for (int il = 0; il < n_layer; ++il) {
22 // norm
23 cur = build_norm(cur: inpL,
24 mw: model.layers[il].attn_norm, NULL,
25 type: LLM_NORM_RMS, il);
26 cb(cur, name: "attn_norm", il);
27
28 ggml_tensor * sa_inp = cur;
29
30 // self-attention
31 {
32 // compute Q and K and RoPE them
33 ggml_tensor * Qcur = build_lora_mm(w: model.layers[il].wq, cur);
34 cb(cur: Qcur, name: "Qcur", il);
35
36 ggml_tensor * Kcur = build_lora_mm(w: model.layers[il].wk, cur);
37 cb(cur: Kcur, name: "Kcur", il);
38
39 ggml_tensor * Vcur = build_lora_mm(w: model.layers[il].wv, cur);
40 cb(cur: Vcur, name: "Vcur", il);
41
42 Qcur = ggml_reshape_3d(ctx: ctx0, a: Qcur, ne0: n_embd_head, ne1: n_head, ne2: n_tokens);
43 Kcur = ggml_reshape_3d(ctx: ctx0, a: Kcur, ne0: n_embd_head, ne1: n_head_kv, ne2: n_tokens);
44 Vcur = ggml_reshape_3d(ctx: ctx0, a: Vcur, ne0: n_embd_head, ne1: n_head_kv, ne2: n_tokens);
45
46 Qcur = ggml_rope_ext(
47 ctx: ctx0, a: Qcur, b: inp_pos, c: nullptr,
48 n_dims: n_embd_head, mode: rope_type, n_ctx_orig, freq_base, freq_scale,
49 ext_factor, attn_factor, beta_fast, beta_slow
50 );
51
52 Kcur = ggml_rope_ext(
53 ctx: ctx0, a: Kcur, b: inp_pos, c: nullptr,
54 n_dims: n_embd_head, mode: rope_type, n_ctx_orig, freq_base, freq_scale,
55 ext_factor, attn_factor, beta_fast, beta_slow
56 );
57
58 cb(cur: Qcur, name: "Qcur", il);
59 cb(cur: Kcur, name: "Kcur", il);
60 cb(cur: Vcur, name: "Vcur", il);
61
62 cur = build_attn(inp: inp_attn,
63 wo: model.layers[il].wo, NULL,
64 q_cur: Qcur, k_cur: Kcur, v_cur: Vcur, kq_b: nullptr, sinks: nullptr, v_mla: nullptr, kq_scale: 1.0f/sqrtf(x: float(n_embd_head)), il);
65 }
66 if (il == n_layer - 1 && inp_out_ids) {
67 cur = ggml_get_rows(ctx: ctx0, a: cur, b: inp_out_ids);
68 sa_inp = ggml_get_rows(ctx: ctx0, a: sa_inp, b: inp_out_ids);
69 inpL = ggml_get_rows(ctx: ctx0, a: inpL, b: inp_out_ids);
70 }
71 ggml_tensor * sa_out = cur;
72
73 cur = sa_inp;
74
75 // feed-forward network
76 {
77 cur = build_ffn(cur,
78 up: model.layers[il].ffn_up, NULL, NULL,
79 gate: model.layers[il].ffn_gate, NULL, NULL,
80 down: model.layers[il].ffn_down, NULL, NULL,
81 NULL,
82 type_op: LLM_FFN_SILU, type_gate: LLM_FFN_PAR, il);
83 cb(cur, name: "ffn_out", il);
84 }
85 cur = ggml_add(ctx: ctx0, a: cur, b: sa_out);
86 cur = ggml_add(ctx: ctx0, a: cur, b: inpL);
87
88 cur = build_cvec(cur, il);
89 cb(cur, name: "l_out", il);
90
91 // input for next layer
92 inpL = cur;
93 }
94 cur = inpL;
95
96 cur = build_norm(cur,
97 mw: model.output_norm, NULL,
98 type: LLM_NORM_RMS, il: -1);
99
100 cb(cur, name: "result_norm", il: -1);
101 res->t_embd = cur;
102
103 // lm_head
104 cur = build_lora_mm(w: model.output, cur);
105
106 cb(cur, name: "result_output", il: -1);
107 res->t_logits = cur;
108
109 ggml_build_forward_expand(cgraph: gf, tensor: cur);
110}
111