mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-08-12 22:29:39 +04:00
218 lines
8.7 KiB
C++
218 lines
8.7 KiB
C++
#include "../llama-build-context.h"
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#include "../llama-model.h"
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#include "../llama-context.h"
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ggml_cgraph * llm_build_context::build_llama() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(n_tokens), false);
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// mutable variable, needed during the last layer of the computation to skip unused tokens
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int32_t n_tokens = this->n_tokens;
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const int64_t n_embd_head = hparams.n_embd_head_v(0);
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k(0));
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GGML_ASSERT(n_embd_head == hparams.n_rot);
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ggml_tensor * cur;
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ggml_tensor * inpL;
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ggml_tensor * inp_attn_scale = nullptr;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = build_inp_pos();
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if (model.arch == LLM_ARCH_LLAMA4) {
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inp_attn_scale = build_input_scale(n_tokens);
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}
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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//bool is_swa = hparams.n_swa > 0 && h_params.n_swa_pattern > 0 ?
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ggml_tensor * KQ_mask = build_inp_KQ_mask();
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ggml_tensor * KQ_mask_swa = nullptr;
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if (hparams.n_swa > 0 && hparams.n_swa_pattern > 0) {
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KQ_mask_swa = build_inp_KQ_mask_swa();
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}
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auto inp_out_ids = n_tokens > 1 ? build_inp_out_ids() : nullptr;
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//const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
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const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : 1.f;
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * inpSA = inpL;
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bool use_rope = model.arch == LLM_ARCH_LLAMA4 ? (il + 1) % hparams.n_no_rope_layer_step != 0 : true;
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auto this_KQ_mask = hparams.n_swa > 0 && hparams.n_swa_pattern > 0 && il % hparams.n_swa_pattern < (hparams.n_swa_pattern - 1) ?
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KQ_mask_swa : KQ_mask;
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int this_n_swa = this_KQ_mask == KQ_mask_swa ? hparams.n_swa : 0;
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// rope freq factors for llama3; may return nullptr for llama2 and other models
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//auto rope_factors = build_rope_factors(il);
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// self-attention
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if (use_rope) {
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cur = build_std_attention(gf, model.layers[il].attn_norm, inpL,
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inp_pos, il == n_layer - 1 && n_tokens > 1 ? inp_out_ids : nullptr, nullptr,
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this_KQ_mask, nullptr, nullptr, kq_scale, hparams.f_attention_scale, this_n_swa, il, true, false, true);
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}
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else {
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auto rope_factors = build_rope_factors(il);
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// norm
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cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
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cb(cur, "attn_norm", il);
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auto [Qcur, Kcur, Vcur] = llm_build_mul_mat_qkv(gf, cur,
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model.layers[il].wqkv, model.layers[il].bqkv,
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model.layers[il].wqk, model.layers[il].bqk,
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model.layers[il].wq, model.layers[il].bq,
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model.layers[il].wk, model.layers[il].bk,
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model.layers[il].wv, model.layers[il].bv,
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nullptr, nullptr, hparams.f_attention_scale, il);
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if (use_rope) {
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Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors,
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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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Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, rope_factors,
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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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} else if (inp_attn_scale) {
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Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);
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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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if (model.arch == LLM_ARCH_LLAMA4 && use_rope && hparams.use_kq_norm) {
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// Llama4TextL2Norm
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Qcur = ggml_rms_norm(ctx0, Qcur, hparams.f_norm_rms_eps);
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Kcur = ggml_rms_norm(ctx0, Kcur, hparams.f_norm_rms_eps);
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cb(Qcur, "Qcur_normed", il);
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cb(Kcur, "Kcur_normed", il);
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}
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cur = llm_build_kv(ctx0, lctx, kv_self, gf,
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model.layers[il].wo, model.layers[il].bo,
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Kcur, Vcur, Qcur, this_KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il, nullptr,
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this_n_swa);
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}
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if (il == n_layer - 1 && !use_rope && inp_out_ids) {
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// skip computing output for unused tokens
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auto inp_out_ids = build_inp_out_ids();
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n_tokens = n_outputs;
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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cb(cur, "last_attn", il);
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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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cb(inpSA, "last_ffn_inp", il);
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}
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// For Granite architecture
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if (hparams.f_residual_scale) {
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// Why is hparams.f_residual_scale not simply absorbed into model.layers[il].wv ?
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cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
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}
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ggml_tensor * ffn_inp;
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if (use_rope) {
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ffn_inp = cur;
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} else {
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ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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}
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// feed-forward network
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if (model.layers[il].ffn_gate_inp == nullptr) {
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// non-MoE
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cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, ffn_inp,
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model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
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model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il, gf, true);
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cb(cur, "ffn_out", il);
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} else if (model.arch == LLM_ARCH_LLAMA4) {
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// llama4 MoE
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ggml_tensor * ffn_inp_normed = llm_build_norm(ctx0, ffn_inp, hparams, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, cb, il);
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cb(cur, "ffn_norm", il);
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ggml_tensor * moe_out = llm_build_moe_ffn(ctx0, lctx, ffn_inp_normed,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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model.layers[il].ffn_gate_exps,
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model.layers[il].ffn_down_exps,
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nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SILU, false,
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false, 0.0,
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LLM_EXPERT_GATING_FUNC_SIGMOID,
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cb, il, gf, true, model.layers[il].ffn_up_gate_exps);
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// Shared experts
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ggml_tensor * shexp_out = llm_build_ffn(ctx0, lctx, nullptr, ffn_inp_normed,
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model.layers[il].ffn_up_shexp, NULL, NULL,
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model.layers[il].ffn_gate_shexp, NULL, NULL,
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model.layers[il].ffn_down_shexp, NULL, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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cb(shexp_out, "ffn_moe_shexp", il);
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cur = ggml_add(ctx0, moe_out, shexp_out);
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cb(cur, "ffn_moe_out_merged", il);
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} else {
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// MoE branch
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cur = llm_build_norm(ctx0, ffn_inp, hparams, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, cb, il);
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cb(cur, "ffn_norm", il);
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cur = llm_build_moe_ffn(ctx0, lctx, cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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model.layers[il].ffn_gate_exps,
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model.layers[il].ffn_down_exps,
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nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SILU, true,
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false, 0.0,
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LLM_EXPERT_GATING_FUNC_SOFTMAX,
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cb, il, gf, true);
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cb(cur, "ffn_moe_out", il);
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}
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// For Granite architecture
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if (hparams.f_residual_scale) {
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// Why is hparams.f_residual_scale not simply absorbed into model.layers[il].ffn_down_exps ?
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cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
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}
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//cur = ggml_add(ctx0, cur, ffn_inp);
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//cb(cur, "ffn_out", il);
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cur = lctx.cvec.apply_to(ctx0, cur, il);
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cb(cur, "l_out", 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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// lm_head
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cur = build_output(lctx, ctx0, cur, model.output, model.output_norm, cb);
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// For Granite architecture
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if (hparams.f_logit_scale) {
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// Why is hparams.f_logit_scale not simply absorbed into model.output ?
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cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
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}
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cb(cur, "result_output", -1);
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ggml_build_forward_expand(gf, cur);
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return gf;
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}
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