1#include "models.h"
2
3llm_build_rwkv6qwen2::llm_build_rwkv6qwen2(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv6_base(model, params) {
4 GGML_ASSERT(n_embd == hparams.n_embd_r());
5
6 ggml_tensor * cur;
7 ggml_tensor * inpL;
8
9 inpL = build_inp_embd(tok_embd: model.tok_embd);
10
11 auto * rs_inp = build_rs_inp();
12
13 const auto n_embd = hparams.n_embd;
14 const auto n_seq_tokens = ubatch.n_seq_tokens;
15 const auto n_seqs = ubatch.n_seqs;
16
17 ggml_tensor * inp_out_ids = build_inp_out_ids();
18
19 for (int il = 0; il < n_layer; ++il) {
20 const llama_layer * layer = &model.layers[il];
21 inpL = ggml_reshape_3d(ctx: ctx0, a: inpL, ne0: n_embd, ne1: n_seq_tokens, ne2: n_seqs);
22
23 ggml_tensor * token_shift = build_rwkv_token_shift_load(inp: rs_inp, ubatch, il);
24
25 ggml_tensor * att_norm = build_norm(cur: inpL, mw: layer->attn_norm, mb: layer->attn_norm_b, type: LLM_NORM_RMS, il);
26 cb(cur: att_norm, name: "attn_norm", il);
27
28 ggml_tensor * x_prev = ggml_concat(
29 ctx: ctx0,
30 a: token_shift,
31 b: ggml_view_3d(ctx: ctx0, a: att_norm, ne0: n_embd, ne1: n_seq_tokens - 1, ne2: n_seqs, nb1: att_norm->nb[1], nb2: att_norm->nb[2], offset: 0),
32 dim: 1
33 );
34
35 cur = build_rwkv6_time_mix(inp: rs_inp, cur: att_norm, x_prev, ubatch, il);
36
37 token_shift = ggml_view_3d(ctx: ctx0, a: att_norm, ne0: n_embd, ne1: 1, ne2: n_seqs, nb1: att_norm->nb[1], nb2: att_norm->nb[2], offset: (n_seq_tokens-1)*n_embd*ggml_element_size(tensor: att_norm));
38 ggml_build_forward_expand(cgraph: gf, tensor: build_rwkv_token_shift_store(token_shift, ubatch, il));
39
40 ggml_tensor * ffn_inp = ggml_add(ctx: ctx0, a: cur, b: inpL);
41 cb(cur: ffn_inp, name: "ffn_inp", il);
42
43 cur = ggml_reshape_2d(ctx: ctx0, a: cur, ne0: n_embd, ne1: n_tokens);
44 ffn_inp = ggml_reshape_2d(ctx: ctx0, a: ffn_inp, ne0: n_embd, ne1: n_tokens);
45
46 if (il == n_layer - 1 && inp_out_ids) {
47 cur = ggml_get_rows(ctx: ctx0, a: cur, b: inp_out_ids);
48 ffn_inp = ggml_get_rows(ctx: ctx0, a: ffn_inp, b: inp_out_ids);
49 }
50
51 // feed-forward network
52 cur = build_norm(cur: ffn_inp,
53 mw: model.layers[il].ffn_norm, NULL,
54 type: LLM_NORM_RMS, il);
55 cb(cur, name: "ffn_norm", il);
56
57 cur = build_ffn(cur,
58 up: model.layers[il].ffn_up, NULL, NULL,
59 gate: model.layers[il].ffn_gate, NULL, NULL,
60 down: model.layers[il].ffn_down, NULL, NULL,
61 NULL,
62 type_op: LLM_FFN_SILU, type_gate: LLM_FFN_PAR, il);
63 cb(cur, name: "ffn_out", il);
64
65 cur = ggml_add(ctx: ctx0, a: cur, b: ffn_inp);
66
67 cur = build_cvec(cur, il);
68 cb(cur, name: "l_out", il);
69
70 // input for next layer
71 inpL = cur;
72 }
73
74 cur = inpL;
75 cur = build_norm(cur, mw: model.output_norm, mb: model.output_norm_b, type: LLM_NORM_RMS, il: -1);
76
77 cb(cur, name: "result_norm", il: -1);
78 res->t_embd = cur;
79
80 cur = build_lora_mm(w: model.output, cur);
81
82 cb(cur, name: "result_output", il: -1);
83 res->t_logits = cur;
84
85 ggml_build_forward_expand(cgraph: gf, tensor: cur);
86}
87