177 lines
7.3 KiB
C++
177 lines
7.3 KiB
C++
#include "models.h"
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llm_build_bert::llm_build_bert(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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const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
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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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ggml_tensor * inp_pos = nullptr;
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if (model.arch != LLM_ARCH_JINA_BERT_V2) {
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inp_pos = build_inp_pos();
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}
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// construct input embeddings (token, type, position)
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inpL = build_inp_embd(model.tok_embd);
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// token types are hardcoded to zero ("Sentence A")
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if (model.type_embd) {
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ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
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inpL = ggml_add(ctx0, inpL, type_row0);
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}
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if (model.arch == LLM_ARCH_BERT) {
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inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL);
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}
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cb(inpL, "inp_embd", -1);
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// embed layer norm
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inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
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cb(inpL, "inp_norm", -1);
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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 * cur = inpL;
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{
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ggml_tensor * Qcur;
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ggml_tensor * Kcur;
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ggml_tensor * Vcur;
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// self-attention
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if (model.layers[il].wqkv) {
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cur = build_lora_mm(model.layers[il].wqkv, cur);
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cb(cur, "wqkv", il);
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if (model.layers[il].bqkv) {
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cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
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cb(cur, "bqkv", il);
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}
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Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head * sizeof(float), cur->nb[1],
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0 * sizeof(float) * (n_embd));
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Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
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cur->nb[1], 1 * sizeof(float) * (n_embd));
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Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
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cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa));
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} else {
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Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, cur), model.layers[il].bq);
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Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, cur), model.layers[il].bk);
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Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, cur), model.layers[il].bv);
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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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}
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if (model.layers[il].attn_q_norm) {
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Qcur = ggml_reshape_2d(ctx0, Qcur, n_embd_head * n_head, n_tokens);
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Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, model.layers[il].attn_q_norm_b, LLM_NORM, il);
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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}
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if (model.layers[il].attn_k_norm) {
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Kcur = ggml_reshape_2d(ctx0, Kcur, n_embd_head * n_head_kv, n_tokens);
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Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, model.layers[il].attn_k_norm_b, LLM_NORM, il);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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}
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// RoPE
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if (model.arch == LLM_ARCH_NOMIC_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE ||
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model.arch == LLM_ARCH_JINA_BERT_V3) {
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Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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}
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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cur = build_attn(inp_attn,
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model.layers[il].wo, model.layers[il].bo,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), 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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inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
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}
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// re-add the layer input
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cur = ggml_add(ctx0, cur, inpL);
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// attention layer norm
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cur = build_norm(cur, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, il);
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if (model.layers[il].attn_norm_2 != nullptr) {
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cur = ggml_add(ctx0, cur, inpL); // re-add the layer input
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cur = build_norm(cur, model.layers[il].attn_norm_2, model.layers[il].attn_norm_2_b, LLM_NORM, il);
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}
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ggml_tensor * ffn_inp = cur;
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cb(ffn_inp, "ffn_inp", il);
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// feed-forward network
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if (hparams.moe_every_n_layers > 0 && il % hparams.moe_every_n_layers == 1) {
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// MoE branch
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cur = build_moe_ffn(cur, model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, nullptr,
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model.layers[il].ffn_down_exps, nullptr, hparams.n_expert, hparams.n_expert_used,
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LLM_FFN_GELU, false, false, 0.0f, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
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cb(cur, "ffn_moe_out", il);
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} else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE ||
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model.arch == LLM_ARCH_JINA_BERT_V3) {
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cur = build_ffn(cur,
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model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
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NULL, NULL, NULL,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, NULL,
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LLM_FFN_GELU, LLM_FFN_SEQ, il);
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cb(cur, "ffn_out", il);
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} else if (model.arch == LLM_ARCH_JINA_BERT_V2) {
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cur = build_ffn(cur,
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model.layers[il].ffn_up, NULL, NULL,
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model.layers[il].ffn_gate, NULL, NULL,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, NULL,
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model.layers[il].ffn_gate ? LLM_FFN_GELU : LLM_FFN_GEGLU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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} else {
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cur = build_ffn(cur,
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model.layers[il].ffn_up, NULL, NULL,
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model.layers[il].ffn_gate, NULL, NULL,
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model.layers[il].ffn_down, NULL, NULL,
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NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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}
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// attentions bypass the intermediate layer
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cur = ggml_add(ctx0, cur, ffn_inp);
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// output layer norm
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cur = build_norm(cur, model.layers[il].layer_out_norm, model.layers[il].layer_out_norm_b, LLM_NORM, 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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res->t_embd = cur;
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ggml_build_forward_expand(gf, cur);
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}
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