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https://github.com/ggml-org/llama.cpp.git
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model, mtmd: fix gguf conversion for audio/vision mmproj (#21309)
* fix gguf conversion for audio/vision mmproj * fix test
This commit is contained in:
Xuan-Son Nguyen
311
src/models/gemma4-iswa.cpp
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311
src/models/gemma4-iswa.cpp
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@@ -0,0 +1,311 @@
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#include "models.h"
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llm_build_gemma4_iswa::llm_build_gemma4_iswa(const llama_model & model, const llm_graph_params & params) :
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llm_graph_context(params),
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model(model),
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n_embd_per_layer(model.hparams.n_embd_per_layer) {
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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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// important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
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inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);
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cb(inpL, "inp_scaled", -1);
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// inp_pos - contains the positions
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ggml_tensor * inp_pos = build_inp_pos();
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// TODO: is causal == true correct? might need some changes
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auto * inp_attn = build_attn_inp_kv_iswa();
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// inp_per_layer shape: [n_embd_per_layer, n_tokens, n_layer]
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ggml_tensor * inp_per_layer = nullptr;
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if (model.tok_embd_per_layer) {
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inp_per_layer = project_per_layer_inputs(inpL, get_per_layer_inputs());
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}
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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_embd_head = hparams.n_embd_head_k(il);
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_v(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 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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const int n_rot_l = hparams.n_rot(il);
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// norm
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cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
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cb(cur, "attn_norm", il);
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ggml_tensor * freq_factors = nullptr;
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if (!hparams.is_swa(il)) {
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// full_attention layers use rope_freqs for proportional rope
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freq_factors = model.layers[il].rope_freqs;
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}
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// Q projection (shared for both non-KV and KV layers)
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// this is to mirror Gemma4Attention in pytorch code
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ggml_tensor * Qcur;
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{
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Qcur = build_lora_mm(model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
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cb(Qcur, "Qcur_normed", il);
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Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, freq_factors, n_rot_l, 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_pos", il);
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}
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// self-attention
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if (hparams.has_kv(il)) {
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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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ggml_tensor * Vcur = model.layers[il].wv
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? build_lora_mm(model.layers[il].wv, cur)
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: Kcur; // if v_proj is not present, use Kcur as Vcur
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cb(Vcur, "Vcur", il);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
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Vcur = ggml_rms_norm(ctx0, Vcur, hparams.f_norm_rms_eps);
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cb(Kcur, "Kcur_normed", il);
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cb(Vcur, "Vcur_normed", il);
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Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, freq_factors, n_rot_l, 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_pos", il);
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cur = build_attn(inp_attn, model.layers[il].wo,
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nullptr, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
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hparams.f_attention_scale, il);
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} else {
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// reuse KV cache of earlier layers
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cur = build_attn(inp_attn,
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model.layers[il].wo, nullptr,
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Qcur, nullptr, nullptr, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);
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}
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// TODO @ngxson : strip unused token right after the last KV layer to speed up prompt processing
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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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cur = build_norm(cur,
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model.layers[il].attn_post_norm, nullptr,
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LLM_NORM_RMS, il);
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cb(cur, "attn_post_norm", il);
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ggml_tensor * attn_out = ggml_add(ctx0, cur, inpL);
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cb(attn_out, "attn_out", il);
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// feed-forward network
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const bool is_moe_layer = model.layers[il].ffn_gate_inp != nullptr;
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if (is_moe_layer) {
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// MLP (shared exp)
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ggml_tensor * cur_mlp = build_norm(attn_out,
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model.layers[il].ffn_norm, nullptr,
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LLM_NORM_RMS, il);
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cb(cur_mlp, "ffn_norm_1", il);
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cur_mlp = build_ffn(cur_mlp,
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model.layers[il].ffn_up, nullptr, nullptr,
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model.layers[il].ffn_gate, nullptr, nullptr,
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model.layers[il].ffn_down, nullptr, nullptr,
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nullptr,
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LLM_FFN_GELU, LLM_FFN_PAR, il);
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cur_mlp = build_norm(cur_mlp,
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model.layers[il].ffn_post_norm_1, nullptr,
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LLM_NORM_RMS, il);
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cb(cur_mlp, "ffn_mlp", il);
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// Expert FFN
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ggml_tensor * cur_moe = build_norm(attn_out,
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model.layers[il].ffn_pre_norm_2, nullptr,
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LLM_NORM_RMS, il);
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cb(cur_moe, "ffn_norm_2", il);
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// custom MoE logits calculation (router operates on attn_out, not cur)
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ggml_tensor * tmp = ggml_rms_norm(ctx0, attn_out, hparams.f_norm_rms_eps);
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tmp = ggml_scale(ctx0, tmp, 1.0f / sqrtf((float) n_embd));
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tmp = ggml_mul(ctx0, tmp, model.layers[il].ffn_gate_inp_s);
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ggml_tensor * logits = build_lora_mm(model.layers[il].ffn_gate_inp, tmp); // [n_expert, n_tokens]
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cb(logits, "ffn_moe_logits", il);
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cur_moe = build_moe_ffn(cur_moe,
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nullptr, // gate_inp
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nullptr, // up_exps
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nullptr, // gate_exps
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model.layers[il].ffn_down_exps,
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nullptr, // exp_probs_b (not used for gemma4)
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n_expert, n_expert_used,
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LLM_FFN_GELU, true,
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1.0f,
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LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
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il, logits,
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model.layers[il].ffn_gate_up_exps,
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nullptr, // up_exps_s
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nullptr, // gate_exps_s
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model.layers[il].ffn_down_exps_s);
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cur_moe = build_norm(cur_moe,
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model.layers[il].ffn_post_norm_2, nullptr,
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LLM_NORM_RMS, il);
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cb(cur_moe, "ffn_moe", il);
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cur = ggml_add(ctx0, cur_mlp, cur_moe);
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cb(cur, "ffn_moe_combined", il);
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} else {
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cur = build_norm(attn_out,
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model.layers[il].ffn_norm, nullptr,
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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, nullptr, nullptr,
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model.layers[il].ffn_gate, nullptr, nullptr,
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model.layers[il].ffn_down, nullptr, nullptr,
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nullptr,
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LLM_FFN_GELU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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}
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cur = build_norm(cur,
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model.layers[il].ffn_post_norm, nullptr,
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LLM_NORM_RMS, -1);
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cb(cur, "ffn_post_norm", il);
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// residual connection
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cur = ggml_add(ctx0, cur, attn_out);
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// per-layer embedding
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if (inp_per_layer) {
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ggml_tensor * pe_in = cur;
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cb(cur, "pe_in", il);
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cur = build_lora_mm(model.layers[il].per_layer_inp_gate, cur); // [n_embd_per_layer, n_tokens]
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cur = ggml_gelu(ctx0, cur);
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ggml_tensor * inp_this_layer = view_2d_slice(inp_per_layer, il); // [n_embd_per_layer, n_tokens]
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// TODO @ngxson : improve this
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if (il == n_layer - 1 && inp_out_ids) {
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inp_this_layer = ggml_get_rows(ctx0, inp_this_layer, inp_out_ids);
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}
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cur = ggml_mul(ctx0, cur, inp_this_layer);
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cur = build_lora_mm(model.layers[il].per_layer_proj, cur); // [n_embd, n_tokens]
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cur = build_norm(cur, model.layers[il].per_layer_post_norm, nullptr, LLM_NORM_RMS, il);
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cb(cur, "per_layer_embd_out", il);
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// residual connection
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cur = ggml_add(ctx0, pe_in, cur);
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}
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// layer_scalar
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if (model.layers[il].out_scale) {
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cur = ggml_mul(ctx0, cur, model.layers[il].out_scale);
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cb(cur, "out_scaled", il);
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}
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cur = build_cvec(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_norm(cur,
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model.output_norm, nullptr,
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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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// lm_head
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cur = build_lora_mm(model.output, cur);
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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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// get 2D slice view from a 3D tensor, the idx corresponds to the 3rd dim
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ggml_tensor * llm_build_gemma4_iswa::view_2d_slice(ggml_tensor * x, int idx) {
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GGML_ASSERT(idx < (int) x->ne[2]);
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return ggml_view_2d(ctx0, x, x->ne[0], x->ne[1], ggml_row_size(x->type, x->ne[0]),
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idx * x->ne[0] * x->ne[1] * ggml_element_size(x));
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}
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// equivalent to get_per_layer_inputs() in python code
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// output shape: [n_embd_per_layer, n_layer, n_tokens]
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ggml_tensor * llm_build_gemma4_iswa::get_per_layer_inputs() {
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auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
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ggml_tensor * inp_per_layer;
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if (ubatch.token) {
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inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
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ggml_set_input(inp->tokens);
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res->t_inp_tokens = inp->tokens;
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inp_per_layer = ggml_get_rows(ctx0, model.tok_embd_per_layer, inp->tokens);
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inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_per_layer, n_layer, n_tokens);
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inp_per_layer = ggml_scale(ctx0, inp_per_layer, sqrtf((float) n_embd_per_layer));
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cb(inp_per_layer, "inp_per_layer_selected", -1);
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res->add_input(std::move(inp));
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} else {
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// Vision embedding path: use padding token (ID=0) embedding
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// TODO: verify if this is the correct behavior in transformers implementation
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const int64_t embd_size = model.tok_embd_per_layer->ne[0]; // n_embd_per_layer * n_layer
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// Extract and dequantize padding token embedding (row 0)
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ggml_tensor * padding = ggml_view_1d(ctx0, model.tok_embd_per_layer, embd_size, 0);
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inp_per_layer = ggml_cast(ctx0, padding, GGML_TYPE_F32);
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// Reshape to [n_embd_per_layer, n_layer, 1]
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inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_per_layer, n_layer, 1);
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cb(inp_per_layer, "inp_per_layer_vision", -1);
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}
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return inp_per_layer;
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}
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// equivalent to project_per_layer_inputs() in python code
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// this calculates the per-layer inputs, so the final tensor shape will have n_layer as the last dim
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// inputs_embeds shape: [n_embd, n_tokens]
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// inp_per_layer shape: [n_embd_per_layer, n_layer, n_tokens] (from get_per_layer_inputs)
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// output shape: [n_embd_per_layer, n_tokens, n_layer]
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ggml_tensor * llm_build_gemma4_iswa::project_per_layer_inputs(ggml_tensor * inputs_embeds, ggml_tensor * inp_per_layer) {
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const float per_layer_projection_scale = 1.0f / sqrtf((float) n_embd);
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const float per_layer_input_scale = 1.0f / sqrtf(2.0f);
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ggml_tensor * per_layer_proj = ggml_mul_mat(ctx0, model.per_layer_model_proj, inputs_embeds);
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per_layer_proj = ggml_scale(ctx0, per_layer_proj, per_layer_projection_scale);
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per_layer_proj = ggml_reshape_3d(ctx0, per_layer_proj, n_embd_per_layer, n_layer, n_tokens);
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per_layer_proj = build_norm(per_layer_proj, model.per_layer_proj_norm, nullptr, LLM_NORM_RMS,
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-1); // [n_embd_per_layer, n_layer, n_tokens]
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cb(per_layer_proj, "per_layer_proj", -1);
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inp_per_layer = ggml_add(ctx0, per_layer_proj, inp_per_layer);
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inp_per_layer = ggml_scale(ctx0, inp_per_layer, per_layer_input_scale);
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cb(inp_per_layer, "inp_per_layer", -1);
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// permute to shape: [n_embd_per_layer, n_tokens, n_layer]
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inp_per_layer = ggml_cont(ctx0, ggml_permute(ctx0, inp_per_layer, 0, 2, 1, 3));
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return inp_per_layer;
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}
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@@ -266,6 +266,17 @@ struct llm_build_gemma3n_iswa : public llm_graph_context {
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ggml_tensor * altup_correct(ggml_tensor * predictions, ggml_tensor * activated, int il);
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};
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struct llm_build_gemma4_iswa : public llm_graph_context {
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const llama_model & model;
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const int64_t n_embd_per_layer;
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llm_build_gemma4_iswa(const llama_model & model, const llm_graph_params & params);
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ggml_tensor * view_2d_slice(ggml_tensor * x, int idx);
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ggml_tensor * get_per_layer_inputs();
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ggml_tensor * project_per_layer_inputs(ggml_tensor * inputs_embeds, ggml_tensor * inp_per_layer);
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};
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struct llm_build_gemma_embedding : public llm_graph_context {
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llm_build_gemma_embedding(const llama_model & model, const llm_graph_params & params);
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};
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