1#include "models.h"
2
3llm_build_minicpm3::llm_build_minicpm3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
4 //TODO: if the model varies, these parameters need to be read from the model
5 const int64_t n_embd_base = 256;
6 const float scale_embd = 12.0f;
7 const float scale_depth = 1.4f;
8 const float kq_scale = 1.0f / sqrtf(float(hparams.n_embd_head_k));
9
10 const uint32_t n_embd_head_qk_rope = hparams.n_rot;
11 const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
12
13 const uint32_t kv_lora_rank = hparams.n_lora_kv;
14
15 ggml_tensor * cur;
16 ggml_tensor * inpL;
17
18 inpL = build_inp_embd(model.tok_embd);
19
20 // scale the input embeddings
21 inpL = ggml_scale(ctx0, inpL, scale_embd);
22 cb(inpL, "inp_scaled", -1);
23
24 // inp_pos - contains the positions
25 ggml_tensor * inp_pos = build_inp_pos();
26
27 auto * inp_attn = build_attn_inp_kv();
28
29 ggml_tensor * inp_out_ids = build_inp_out_ids();
30
31 for (int il = 0; il < n_layer; ++il) {
32 ggml_tensor * inpSA = inpL;
33
34 ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
35
36 // norm
37 cur = build_norm(inpL,
38 model.layers[il].attn_norm, NULL,
39 LLM_NORM_RMS, il);
40 cb(cur, "attn_norm", il);
41
42 // self_attention
43 {
44 ggml_tensor * q = NULL;
45 // {n_embd, q_lora_rank} * {n_embd, n_tokens} -> {q_lora_rank, n_tokens}
46 q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
47 cb(q, "q", il);
48
49 q = build_norm(q,
50 model.layers[il].attn_q_a_norm, NULL,
51 LLM_NORM_RMS, il);
52 cb(q, "q", il);
53
54 // {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens}
55 q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
56 cb(q, "q", il);
57
58 // split into {n_head * n_embd_head_qk_nope, n_tokens}
59 ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
60 ggml_row_size(q->type, hparams.n_embd_head_k),
61 ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
62 0);
63 cb(q_nope, "q_nope", il);
64
65 // and {n_head * n_embd_head_qk_rope, n_tokens}
66 ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
67 ggml_row_size(q->type, hparams.n_embd_head_k),
68 ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
69 ggml_row_size(q->type, n_embd_head_qk_nope));
70 cb(q_pe, "q_pe", il);
71
72 // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}
73 ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
74 cb(kv_pe_compresseed, "kv_pe_compresseed", il);
75
76 // split into {kv_lora_rank, n_tokens}
77 ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens,
78 kv_pe_compresseed->nb[1],
79 0);
80 cb(kv_compressed, "kv_compressed", il);
81
82 // and {n_embd_head_qk_rope, n_tokens}
83 ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens,
84 kv_pe_compresseed->nb[1],
85 kv_pe_compresseed->nb[1],
86 ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));
87 cb(k_pe, "k_pe", il);
88
89 kv_compressed = build_norm(kv_compressed,
90 model.layers[il].attn_kv_a_norm, NULL,
91 LLM_NORM_RMS, il);
92 cb(kv_compressed, "kv_compressed", il);
93
94 // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens}
95 ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed);
96 cb(kv, "kv", il);
97
98 // split into {n_head * n_embd_head_qk_nope, n_tokens}
99 ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
100 ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
101 ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
102 0);
103 cb(k_nope, "k_nope", il);
104
105 // and {n_head * n_embd_head_v, n_tokens}
106 ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens,
107 ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)),
108 ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head),
109 ggml_row_size(kv->type, (n_embd_head_qk_nope)));
110 cb(v_states, "v_states", il);
111
112 v_states = ggml_cont(ctx0, v_states);
113 cb(v_states, "v_states", il);
114
115 q_pe = ggml_rope_ext(
116 ctx0, q_pe, inp_pos, rope_factors,
117 n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
118 ext_factor, attn_factor, beta_fast, beta_slow
119 );
120 cb(q_pe, "q_pe", il);
121
122 // shared RoPE key
123 k_pe = ggml_rope_ext(
124 ctx0, k_pe, inp_pos, rope_factors,
125 n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
126 ext_factor, attn_factor, beta_fast, beta_slow
127 );
128 cb(k_pe, "k_pe", il);
129
130 ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);
131 cb(q_states, "q_states", il);
132
133 ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
134 cb(k_states, "k_states", il);
135
136 cur = build_attn(inp_attn,
137 model.layers[il].wo, NULL,
138 q_states, k_states, v_states, nullptr, nullptr, nullptr, kq_scale, il);
139 }
140 if (il == n_layer - 1 && inp_out_ids) {
141 cur = ggml_get_rows(ctx0, cur, inp_out_ids);
142 inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
143 }
144 // scale_res - scale the hidden states for residual connection
145 const float scale_res = scale_depth/sqrtf(float(n_layer)); // TODO: is this correct?
146 cur = ggml_scale(ctx0, cur, scale_res);
147 cb(cur, "hidden_scaled", il);
148
149 ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
150 cb(ffn_inp, "ffn_inp", il);
151
152 // feed-forward network
153 {
154 cur = build_norm(ffn_inp,
155 model.layers[il].ffn_norm, NULL,
156 LLM_NORM_RMS, il);
157 cb(cur, "ffn_norm", il);
158
159 cur = build_ffn(cur,
160 model.layers[il].ffn_up, NULL, NULL,
161 model.layers[il].ffn_gate, NULL, NULL,
162 model.layers[il].ffn_down, NULL, NULL,
163 NULL,
164 LLM_FFN_SILU, LLM_FFN_PAR, il);
165 cb(cur, "ffn_out", il);
166 }
167 // scale the hidden states for residual connection
168 cur = ggml_scale(ctx0, cur, scale_res);
169 cb(cur, "hidden_scaled_ffn", il);
170
171 cur = ggml_add(ctx0, cur, ffn_inp);
172
173 cur = build_cvec(cur, il);
174 cb(cur, "l_out", il);
175
176 // input for next layer
177 inpL = cur;
178 }
179 cur = inpL;
180
181 cur = build_norm(cur,
182 model.output_norm, NULL,
183 LLM_NORM_RMS, -1);
184
185 cb(cur, "result_norm", -1);
186 res->t_embd = cur;
187
188 // lm_head scaling
189 const float scale_lmhead = float(n_embd_base)/float(n_embd);
190 cur = ggml_scale(ctx0, cur, scale_lmhead);
191 cb(cur, "lmhead_scaling", -1);
192
193 // lm_head
194 cur = build_lora_mm(model.output, cur);
195
196 cb(cur, "result_output", -1);
197 res->t_logits = cur;
198
199 ggml_build_forward_expand(gf, cur);
200}