209 lines
8.6 KiB
C++
209 lines
8.6 KiB
C++
#include "models.h"
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void llama_model_muse_glimmer::load_arch_hparams(llama_model_loader & ml) {
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
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ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
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ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
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hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
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ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
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hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
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uint32_t swa_period = 4;
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if (ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false)) {
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hparams.set_swa_pattern(swa_period);
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} else {
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ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
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}
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switch (hparams.n_layer()) {
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case 52: type = LLM_TYPE_30B; break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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}
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void llama_model_muse_glimmer::load_arch_tensors(llama_model_loader &) {
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LLAMA_LOAD_LOCALS;
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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// Pre/post-attention norms (Muse Glimmer's `weight + 1` applied at conversion time).
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
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layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
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// Q/K/V/O projections.
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create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
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// QK-norm. Weights are synthesized at conversion time to absorb `qk_scale_factor`.
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layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
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layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
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// Attention output gate: sigmoid(gate) * attn_out before o_proj (same as afmoe).
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layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
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// Pre/post-FFN norms (FFN_PRE_NORM is aliased to LLM_TENSOR_FFN_NORM).
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
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layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
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// Dense FFN (unlike afmoe, no MoE branches).
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
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}
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}
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llama_model_muse_glimmer::graph::graph(const llama_model & model, const llm_graph_params & params)
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: 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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// Different to f_norm_rms_eps for post-attn / post-FFN norms
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const float post_norm_eps = 1e-8f;
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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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inpL = build_norm(inpL, nullptr, nullptr, LLM_NORM_RMS, -1);
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cb(inpL, "embd_norm", -1);
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ggml_tensor * inp_pos = build_inp_pos();
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auto * inp_attn = build_attn_inp_kv_iswa();
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
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for (int il = 0; il < n_layer; ++il) {
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// expose per-layer residual for speculative drafts (see LLM_KV_TARGET_LAYERS).
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res->t_layer_inp[il] = inpL;
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const float freq_base_l = model.get_rope_freq_base (cparams, il);
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const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
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ggml_tensor * inpSA = inpL;
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// RoPE runs on the SWA layers, NoPE on full ones.
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const bool use_rope = hparams.is_swa(il);
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// pre-attention norm (weight+1 folded at conversion time)
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cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "attn_norm", il);
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// self-attention: attention output gate around SDPA (afmoe.cpp:147-191)
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{
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ggml_tensor * attn_inp = cur; // save input for gate computation
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auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
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n_embd_head, n_head, n_head_kv, il);
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// gate = wqkv_gate @ attn_inp (from pre-attn hidden state)
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ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);
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cb(gate, "attn_gate_proj", il);
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// QK-norm. attn_q_norm weight was synthesized at conversion to broadcast
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// qk_scale_factor across head_dim; attn_k_norm is identity (ones).
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Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
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Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
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cb(Qcur, "Qcur_normed", il);
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cb(Kcur, "Kcur_normed", il);
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if (use_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_l,
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ext_factor, attn_factor, beta_fast, beta_slow);
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cb(Qcur, "Qcur_rope", il);
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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_l,
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ext_factor, attn_factor, beta_fast, beta_slow);
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cb(Kcur, "Kcur_rope", il);
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}
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// SDPA. wo is deferred; the gate goes between attn_out and o_proj.
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cur = build_attn(inp_attn,
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NULL, NULL, NULL,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
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cb(cur, "attn_out", il);
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gate = ggml_sigmoid(ctx0, gate);
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cb(gate, "attn_gate_sig", il);
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cur = ggml_mul(ctx0, cur, gate);
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cb(cur, "attn_gated", il);
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cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);
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cb(cur, "attn_o_proj", il);
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}
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cur = ggml_rms_norm(ctx0, cur, post_norm_eps);
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cur = ggml_mul(ctx0, cur, model.layers[il].attn_post_norm);
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cb(cur, "attn_post_norm", 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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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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}
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ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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// pre-FFN norm
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cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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// SwiGLU dense FFN
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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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cur = ggml_rms_norm(ctx0, cur, post_norm_eps);
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cur = ggml_mul(ctx0, cur, model.layers[il].ffn_post_norm);
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cb(cur, "ffn_post_norm", il);
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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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// final norm
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cur = build_norm(cur, model.output_norm, NULL, 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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// lm_head, followed by output multiplier
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cur = build_lora_mm(model.output, cur, model.output_s);
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cur = ggml_scale(ctx0, cur, hparams.f_logit_scale);
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// Final logit tanh softcap (from gemma3.cpp).
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if (hparams.f_final_logit_softcapping) {
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cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
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cur = ggml_tanh(ctx0, cur);
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cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);
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}
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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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std::unique_ptr<llm_graph_context> llama_model_muse_glimmer::build_arch_graph(const llm_graph_params & params) const {
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return std::make_unique<graph>(*this, params);
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}
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