127 lines
4.1 KiB
C++
127 lines
4.1 KiB
C++
#include "models.h"
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template <bool iswa>
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llm_build_modern_bert<iswa>::llm_build_modern_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 = build_inp_pos();
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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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cb(inpL, "inp_embd", -1);
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// embed layer norm
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inpL = build_norm(inpL, model.tok_norm, nullptr, LLM_NORM, -1);
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cb(inpL, "inp_norm", -1);
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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auto * inp_attn = build_attn_inp_no_cache();
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for (int il = 0; il < n_layer; ++il) {
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float freq_base_l = 0.0f;
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if constexpr (iswa) {
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freq_base_l = model.get_rope_freq_base(cparams, il);
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} else {
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freq_base_l = freq_base;
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}
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cur = inpL;
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// attention layer norm
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if (model.layers[il].attn_norm) {
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cur = build_norm(inpL,
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model.layers[il].attn_norm, NULL,
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LLM_NORM, il);
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cb(cur, "attn_norm", il);
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}
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// self attention
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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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const size_t type_size = ggml_type_size(cur->type);
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ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head*type_size, cur->nb[1], 0*type_size*(n_embd));
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ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*type_size, cur->nb[1], 1*type_size*(n_embd));
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ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*type_size, cur->nb[1], 1*type_size*(n_embd + n_embd_gqa));
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// RoPE
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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, freq_base_l, 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, nullptr,
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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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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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ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
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cb(ffn_inp, "ffn_inp", il);
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// attention layer norm
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cur = build_norm(ffn_inp,
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model.layers[il].ffn_norm, NULL,
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LLM_NORM, il);
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cb(cur, "ffn_norm", il);
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cur = build_ffn(cur,
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model.layers[il].ffn_up, NULL, NULL,
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NULL, NULL, NULL,
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model.layers[il].ffn_down, NULL, NULL,
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NULL,
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LLM_FFN_GEGLU, LLM_FFN_SEQ, il);
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// attentions bypass the intermediate layer
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cur = ggml_add(ctx0, cur, ffn_inp);
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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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cur = build_norm(cur,
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model.output_norm, NULL,
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LLM_NORM, -1);
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cb(cur, "final_norm_out", -1);
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if (hparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
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// extracting cls token
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cur = ggml_view_1d(ctx0, cur, hparams.n_embd, 0);
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cb(cur, "cls_pooled_embd", -1);
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}
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cb(cur, "res_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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// Explicit template instantiations
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template struct llm_build_modern_bert<false>;
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template struct llm_build_modern_bert<true>;
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