mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-07 09:57:00 +00:00
125 lines
4.0 KiB
C++
125 lines
4.0 KiB
C++
#include "models.h"
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llm_build_openelm::llm_build_openelm(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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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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inpL = build_inp_embd(model.tok_embd);
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// inp_pos - contains the positions
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ggml_tensor * inp_pos = build_inp_pos();
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auto * inp_attn = build_attn_inp_kv();
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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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const int64_t n_head = hparams.n_head(il);
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const int64_t n_head_kv = hparams.n_head_kv(il);
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const int64_t n_head_qkv = 2*n_head_kv + n_head;
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cur = inpL;
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ggml_tensor * residual = cur;
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// 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_RMS, il);
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cb(cur, "attn_norm", il);
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// self-attention
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{
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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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cur = ggml_reshape_3d(ctx0, cur, n_embd_head_k, n_head_qkv, n_tokens);
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ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, cur->nb[1], cur->nb[2], 0);
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cb(Qcur, "Qcur", il);
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ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*n_head);
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cb(Kcur, "Kcur", il);
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ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*(n_head+n_head_kv)));
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cb(Vcur, "Vcur", il);
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Qcur = build_norm(Qcur,
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model.layers[il].attn_q_norm, NULL,
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LLM_NORM_RMS, il);
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cb(Qcur, "Qcur", il);
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Kcur = build_norm(Kcur,
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model.layers[il].attn_k_norm, NULL,
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LLM_NORM_RMS, il);
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cb(Kcur, "Kcur", il);
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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, NULL,
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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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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, NULL,
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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(Qcur, "Vcur", il);
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cur = build_attn(inp_attn,
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model.layers[il].wo, NULL,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
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}
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if (il == n_layer - 1 && inp_out_ids) {
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residual = ggml_get_rows(ctx0, residual, inp_out_ids);
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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}
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ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
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cb(ffn_inp, "ffn_inp", il);
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// feed-forward network
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{
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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_RMS, 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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model.layers[il].ffn_gate, NULL, NULL,
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model.layers[il].ffn_down, NULL, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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}
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cur = ggml_add(ctx0, cur, ffn_inp);
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cur = build_cvec(cur, il);
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cb(cur, "l_out", il);
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inpL = cur;
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}
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cur = inpL;
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// norm
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cur = build_norm(cur,
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model.output_norm, NULL,
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LLM_NORM_RMS, -1);
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cb(cur, "result_norm", -1);
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res->t_embd = cur;
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cur = build_lora_mm(model.output, cur);
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cb(cur, "result_output", -1);
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res->t_logits = cur;
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ggml_build_forward_expand(gf, cur);
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}
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