mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-06-28 04:30:15 -05:00
384 lines
14 KiB
C++
384 lines
14 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_qwen3() {
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ggml_cgraph * gf = new_graph_custom();
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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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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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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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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
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ggml_tensor * rope_cache = nullptr;
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if (model.split_mode != LLAMA_SPLIT_MODE_GRAPH && cparams.rope_cache &&
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(rope_type == LLAMA_ROPE_TYPE_NEOX || rope_type == LLAMA_ROPE_TYPE_NORM)) {
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rope_cache = ggml_rope_cache(ctx0, inp_pos, nullptr, n_embd_head, 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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auto inp_out_ids = n_tokens > 1 ? build_inp_out_ids() : nullptr;
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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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if (!rope_cache) {
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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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KQ_mask, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), 0.0f, 0, il, true, false, true);
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} else {
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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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// self-attention
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{
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auto [Qcur, Kcur, Vcur] = llm_build_mul_mat_qkv(gf, cur,
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model.layers[il].wqkv, nullptr,
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model.layers[il].wqk, nullptr,
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model.layers[il].wq, nullptr,
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model.layers[il].wk, nullptr,
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model.layers[il].wv, nullptr,
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model.layers[il].attn_q_norm, model.layers[il].attn_k_norm, 0, il);
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if (rope_cache) {
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Qcur = ggml_rope_fast(ctx0, Qcur, rope_cache);
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Kcur = ggml_rope_fast(ctx0, Kcur, rope_cache);
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} else {
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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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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, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
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cur = ggml_add(ctx0, cur, inpSA);
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cb(cur, "attn_with_inp", il);
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}
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}
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if (il == n_layer - 1 && rope_cache && inp_out_ids) {
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// skip computing output for unused tokens
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struct ggml_tensor * inp_out_ids = build_inp_out_ids();
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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}
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// feed-forward network
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cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, 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, cb, il, gf, true);
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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 = build_output(lctx, ctx0, inpL, model.output, model.output_norm, cb);
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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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ggml_cgraph * llm_build_context::build_qwen3moe() {
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ggml_cgraph * gf = new_graph_custom();
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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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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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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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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
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ggml_tensor * inp_out_ids = nullptr; //build_inp_out_ids();
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for (int il = 0; il < n_layer; ++il) {
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if (il == n_layer - 1 && n_tokens > 1) {
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inp_out_ids = build_inp_out_ids();
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}
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cur = build_std_attention(gf, model.layers[il].attn_norm, inpL, inp_pos, inp_out_ids, nullptr,
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KQ_mask, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), 0.0f, 0, il, true, false, true);
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auto ffn_inp = cur;
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cur = llm_build_std_moe_ffn(ctx0, lctx, model.layers[il].ffn_norm, ffn_inp,
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model.layers[il].ffn_gate_inp, nullptr,
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model.layers[il].ffn_up_exps, nullptr,
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model.layers[il].ffn_gate_exps, nullptr,
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model.layers[il].ffn_down_exps, nullptr,
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model.layers[il].ffn_exp_probs_b,
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nullptr, nullptr, // we don't have shared expert biases?
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nullptr, nullptr,
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nullptr, nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SILU, true, false, 0.0f,
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LLM_EXPERT_GATING_FUNC_SOFTMAX,
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LLM_FFN_SILU, cb, il, gf, true,
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model.layers[il].ffn_up_gate_exps);
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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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cur = build_output(lctx, ctx0, cur, model.output, model.output_norm, cb);
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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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ggml_cgraph * llm_build_context::build_qwen3vl() {
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ggml_cgraph * gf = new_graph_custom();
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const int64_t n_embd_full = hparams.n_embd; // main embd + deepstack embds
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const size_t n_deepstack_layers = hparams.n_deepstack_layers;
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const int64_t n_embd = n_embd_full / (n_deepstack_layers + 1);
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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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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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int sections[4];
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std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
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std::vector<struct ggml_tensor *> deepstack_features;
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if (batch.embd) {
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deepstack_features.resize(n_deepstack_layers, nullptr);
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// Image input: split main embd and deepstack embds
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struct ggml_tensor * inpL_main = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], 0);
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for (size_t i = 0; i < n_deepstack_layers; i++) {
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deepstack_features[i] = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], (i + 1) * n_embd * sizeof(float));
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}
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inpL = inpL_main;
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}
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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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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
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auto inp_out_ids = n_tokens > 1 ? build_inp_out_ids() : nullptr;
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for (int il = 0; il < n_layer; ++il) {
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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 ? inp_out_ids : nullptr, nullptr, KQ_mask,
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nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), 0.0f, 0, il, true, false, true, false, true);
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// feed-forward network
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cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, 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, cb, il, gf, true, false,
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batch.embd && (size_t)il < n_deepstack_layers ? deepstack_features[il] : nullptr);
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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 = build_output(lctx, ctx0, inpL, model.output, model.output_norm, cb);
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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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ggml_cgraph * llm_build_context::build_qwen3vlmoe() {
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ggml_cgraph * gf = new_graph_custom();
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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_full = hparams.n_embd; // main embd + deepstack embds
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const size_t n_deepstack_layers = hparams.n_deepstack_layers;
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const int64_t n_embd = n_embd_full / (n_deepstack_layers + 1);
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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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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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int sections[4];
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std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
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std::vector<struct ggml_tensor *> deepstack_features(n_deepstack_layers, nullptr);
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if (batch.embd) {
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// Image input: split main embd and deepstack embds
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struct ggml_tensor * inpL_main = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], 0);
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for (size_t i = 0; i < n_deepstack_layers; i++) {
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deepstack_features[i] = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], (i + 1) * n_embd * sizeof(float));
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}
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inpL = inpL_main;
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}
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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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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
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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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// norm
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cur = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "attn_norm", il);
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// self_attention
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{
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auto [Qcur, Kcur, Vcur] = llm_build_mul_mat_qkv(gf, cur,
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model.layers[il].wq, nullptr,
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model.layers[il].wk, nullptr,
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model.layers[il].wv, nullptr,
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0, il);
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, cb, il);
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cb(Qcur, "Qcur_normed", il);
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Qcur = ggml_rope_multi(
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ctx0, Qcur, inp_pos, nullptr,
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n_rot, sections, 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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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, cb, il);
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cb(Kcur, "Kcur_normed", il);
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Kcur = ggml_rope_multi(
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ctx0, Kcur, inp_pos, nullptr,
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n_rot, sections, 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(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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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, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
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}
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if (il == n_layer - 1) {
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// skip computing output for unused tokens
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struct ggml_tensor * 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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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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}
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struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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// MoE branch
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cur = llm_build_norm(ctx0, ffn_inp, hparams,
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model.layers[il].ffn_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "ffn_norm", il);
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cur =
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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, false, model.layers[il].ffn_up_gate_exps);
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cb(cur, "ffn_moe_out", il);
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cur = ggml_add(ctx0, cur, ffn_inp);
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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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if (batch.embd && (size_t)il < n_deepstack_layers) {
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cur = ggml_add(ctx0, cur, deepstack_features[il]);
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cb(cur, "deepstack_out", il);
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}
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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 = llm_build_norm(ctx0, cur, hparams,
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model.output_norm, NULL,
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LLM_NORM_RMS, cb, -1);
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cb(cur, "result_norm", -1);
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// lm_head
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cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
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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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