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koboldcpp/src/models/gemma4.cpp
Concedo 9b0b36b5ef Merge commit '66001722aa' into concedo_experimental 
# Conflicts:
#	README.md
#	docs/ops.md
#	docs/ops/SYCL.csv
#	examples/sycl/start-svr.sh
#	ggml/src/ggml-hexagon/ggml-hexagon.cpp
#	ggml/src/ggml-hexagon/htp/CMakeLists.txt
#	ggml/src/ggml-hexagon/htp/htp-ctx.h
#	ggml/src/ggml-hexagon/htp/htp-ops.h
#	ggml/src/ggml-hexagon/htp/main.c
#	ggml/src/ggml-hexagon/htp/unary-ops.c
#	ggml/src/ggml-opencl/CMakeLists.txt
#	ggml/src/ggml-opencl/ggml-opencl.cpp
#	ggml/src/ggml-opencl/kernels/cvt.cl
#	ggml/src/ggml-sycl/gated_delta_net.hpp
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	ggml/src/ggml-sycl/pad.cpp
#	ggml/src/ggml-sycl/ssm_conv.cpp
#	tests/test-backend-ops.cpp
#	tests/test-reasoning-budget.cpp
#	tools/server/README.md
#	tools/server/webui/src/lib/constants/settings-config.ts
2026-05-11 15:40:10 +08:00

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#include "models.h"
void llama_model_gemma4::load_arch_hparams(llama_model_loader & ml) {
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
uint32_t n_kv_shared_layers = 0;
ml.get_key(LLM_KV_ATTENTION_SHARED_KV_LAYERS, n_kv_shared_layers, false);
hparams.n_layer_kv_from_start = hparams.n_layer - (int32_t)n_kv_shared_layers;
hparams.f_attention_scale = 1.0f; // Gemma4 uses self.scaling = 1.0 (no pre-attn scaling)
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EMBEDDING_LENGTH_PER_LAYER, hparams.n_embd_per_layer);
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_SWA, hparams.n_embd_head_k_swa);
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_SWA, hparams.n_embd_head_v_swa);
ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
switch (hparams.n_layer) {
case 30: type = LLM_TYPE_26B_A4B; break;
case 35: type = LLM_TYPE_E2B; break;
case 42: type = LLM_TYPE_E4B; break;
case 60: type = LLM_TYPE_31B; break;
default: type = LLM_TYPE_UNKNOWN;
}
}
void llama_model_gemma4::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
const uint32_t n_embd_per_layer = hparams.n_embd_per_layer;
const int64_t n_ff_exp = hparams.n_ff_exp;
if (n_embd_head_k != n_embd_head_v) {
throw std::runtime_error("Gemma 4 requires n_embd_head_k == n_embd_head_v");
}
if (hparams.n_embd_head_k_swa != hparams.n_embd_head_v_swa) {
throw std::runtime_error("Gemma 4 requires n_embd_head_k_swa == n_embd_head_v_swa");
}
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
// if output is NULL, init from the input tok embed
if (output == NULL) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
if (n_embd_per_layer > 0) {
per_layer_tok_embd = create_tensor(tn(LLM_TENSOR_PER_LAYER_TOKEN_EMBD, "weight"), {n_embd_per_layer * n_layer, n_vocab}, 0);
per_layer_model_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_MODEL_PROJ, "weight", 0), {n_embd, n_embd_per_layer * n_layer}, 0);
per_layer_proj_norm = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ_NORM, "weight", 0), {n_embd_per_layer}, 0);
}
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
int rope_freqs_flag = 0;
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
const int64_t n_head = hparams.n_head(i);
const int64_t n_embd_head = hparams.n_embd_head_k(i);
const int64_t n_embd_k = hparams.n_embd_k_gqa(i);
const int64_t n_embd_v = hparams.n_embd_v_gqa(i);
const int kv_flags = hparams.has_kv(i) ? 0 : TENSOR_NOT_REQUIRED;
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
// note: use_alternative_attention (v_proj is optional, if it's not present, use k_proj)
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k}, kv_flags);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v}, TENSOR_NOT_REQUIRED);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head * n_head, n_embd}, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head}, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head}, kv_flags);
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
layer.out_scale = create_tensor(tn(LLM_TENSOR_LAYER_OUT_SCALE, "weight", i), {1u}, TENSOR_NOT_REQUIRED);
if (!hparams.is_swa(i)) {
// full_attention layers use rope_freqs for proportional rope
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_embd_head/2}, rope_freqs_flag);
rope_freqs_flag = TENSOR_DUPLICATED;
}
// handle use_double_wide_mlp
int64_t n_ff_cur = hparams.n_ff(i);
// for expert layers, we use normal FFN as shared expert (same as python code)
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff_cur}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff_cur}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff_cur, n_embd}, 0);
layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
// MoE router
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
bool has_expert = layer.ffn_gate_inp != nullptr;
// norm
if (has_expert) {
layer.ffn_gate_inp_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "scale", i), {n_embd}, 0);
layer.ffn_pre_norm_2 = create_tensor(tn(LLM_TENSOR_FFN_PRE_NORM_2, "weight", i), {n_embd}, 0);
layer.ffn_post_norm_1 = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM_1, "weight", i), {n_embd}, 0);
layer.ffn_post_norm_2 = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM_2, "weight", i), {n_embd}, 0);
// MoE FFN
layer.ffn_gate_up_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_UP_EXPS, "weight", i), {n_embd, n_ff_exp * 2, n_expert}, TENSOR_NOT_REQUIRED);
if (layer.ffn_gate_up_exps == nullptr) {
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
}
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
// per-expert scale will be loaded as down_exps_s at the end of the current switch case
}
// per-layer embeddings
if (n_embd_per_layer > 0) {
layer.per_layer_inp_gate = create_tensor(tn(LLM_TENSOR_PER_LAYER_INP_GATE, "weight", i), {n_embd, n_embd_per_layer}, 0);
layer.per_layer_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ, "weight", i), {n_embd_per_layer, n_embd}, 0);
layer.per_layer_post_norm = create_tensor(tn(LLM_TENSOR_PER_LAYER_POST_NORM, "weight", i), {n_embd}, 0);
}
}
}
std::unique_ptr<llm_graph_context> llama_model_gemma4::build_arch_graph(const llm_graph_params & params) const {
return std::make_unique<graph>(*this, params);
}
// get 2D slice view from a 3D tensor, the idx corresponds to the 3rd dim
// static ggml_tensor * ggml_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, int idx) {
// GGML_ASSERT(idx < (int) x->ne[2]);
// return ggml_view_2d(ctx0, x, x->ne[0], x->ne[1], ggml_row_size(x->type, x->ne[0]),
// idx * x->ne[0] * x->ne[1] * ggml_element_size(x));
// }
llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_params & params) :
llm_graph_context(params),
model(model),
n_embd_per_layer(model.hparams.n_embd_per_layer) {
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
// important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);
cb(inpL, "inp_scaled", -1);
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
// TODO: is causal == true correct? might need some changes
auto * inp_attn = build_attn_inp_kv_iswa();
ggml_tensor * inp_out_ids = build_inp_out_ids();
ggml_tensor * inp_per_layer = nullptr;
if (model.per_layer_tok_embd) {
inp_per_layer = build_inp_per_layer();
ggml_build_forward_expand(gf, inp_per_layer);
// inp_per_layer shape: [n_embd_per_layer, n_tokens, n_layer]
inp_per_layer = project_per_layer_inputs(inpL, inp_per_layer);
}
for (int il = 0; il < n_layer; ++il) {
const int64_t n_embd_head = hparams.n_embd_head_k(il);
GGML_ASSERT(n_embd_head == hparams.n_embd_head_v(il));
const int64_t n_head = hparams.n_head(il);
const int64_t n_head_kv = hparams.n_head_kv(il);
const float freq_base_l = model.get_rope_freq_base(cparams, il);
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
const int n_rot_l = hparams.n_rot(il);
// norm
cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
ggml_tensor * freq_factors = nullptr;
if (!hparams.is_swa(il)) {
// full_attention layers use rope_freqs for proportional rope
freq_factors = model.layers[il].rope_freqs;
}
// Q projection (shared for both non-KV and KV layers)
// this is to mirror Gemma4Attention in pytorch code
ggml_tensor * Qcur;
{
Qcur = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s);
cb(Qcur, "Qcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, freq_factors, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Qcur, "Qcur_pos", il);
}
// self-attention
if (hparams.has_kv(il)) {
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = model.layers[il].wv
? build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s)
: Kcur; // if v_proj is not present, use Kcur as Vcur
cb(Vcur, "Vcur", il);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
Vcur = ggml_rms_norm(ctx0, Vcur, hparams.f_norm_rms_eps);
cb(Kcur, "Kcur_normed", il);
cb(Vcur, "Vcur_normed", il);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, freq_factors, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Kcur, "Kcur_pos", il);
cur = build_attn(inp_attn, model.layers[il].wo,
nullptr, model.layers[il].wo_s, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
hparams.f_attention_scale, il);
} else {
// reuse KV cache of earlier layers
cur = build_attn(inp_attn,
model.layers[il].wo, nullptr, model.layers[il].wo_s,
Qcur, nullptr, nullptr, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);
}
// TODO @ngxson : strip unused token right after the last KV layer to speed up prompt processing
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}
cur = build_norm(cur,
model.layers[il].attn_post_norm, nullptr,
LLM_NORM_RMS, il);
cb(cur, "attn_post_norm", il);
ggml_tensor * attn_out = ggml_add(ctx0, cur, inpL);
cb(attn_out, "attn_out", il);
// feed-forward network
const bool is_moe_layer = model.layers[il].ffn_gate_inp != nullptr;
if (is_moe_layer) {
// MLP (shared exp)
ggml_tensor * cur_mlp = build_norm(attn_out,
model.layers[il].ffn_norm, nullptr,
LLM_NORM_RMS, il);
cb(cur_mlp, "ffn_norm_1", il);
cur_mlp = build_ffn(cur_mlp,
model.layers[il].ffn_up, nullptr, model.layers[il].ffn_up_s,
model.layers[il].ffn_gate, nullptr, model.layers[il].ffn_gate_s,
model.layers[il].ffn_down, nullptr, model.layers[il].ffn_down_s,
nullptr,
LLM_FFN_GELU, LLM_FFN_PAR, il);
cur_mlp = build_norm(cur_mlp,
model.layers[il].ffn_post_norm_1, nullptr,
LLM_NORM_RMS, il);
cb(cur_mlp, "ffn_mlp", il);
// Expert FFN
ggml_tensor * cur_moe = build_norm(attn_out,
model.layers[il].ffn_pre_norm_2, nullptr,
LLM_NORM_RMS, il);
cb(cur_moe, "ffn_norm_2", il);
// custom MoE logits calculation (router operates on attn_out, not cur)
ggml_tensor * tmp = ggml_rms_norm(ctx0, attn_out, hparams.f_norm_rms_eps);
tmp = ggml_scale(ctx0, tmp, 1.0f / sqrtf((float) n_embd));
tmp = ggml_mul(ctx0, tmp, model.layers[il].ffn_gate_inp_s);
ggml_tensor * logits = build_lora_mm(model.layers[il].ffn_gate_inp, tmp); // [n_expert, n_tokens]
cb(logits, "ffn_moe_logits", il);
cur_moe = build_moe_ffn(cur_moe,
nullptr, // gate_inp
model.layers[il].ffn_up_exps,
model.layers[il].ffn_gate_exps,
model.layers[il].ffn_down_exps,
nullptr, // exp_probs_b (not used for gemma4)
n_expert, n_expert_used,
LLM_FFN_GELU, true,
1.0f,
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
il, logits,
model.layers[il].ffn_gate_up_exps,
model.layers[il].ffn_up_exps_s,
model.layers[il].ffn_gate_exps_s,
model.layers[il].ffn_down_exps_s);
cur_moe = build_norm(cur_moe,
model.layers[il].ffn_post_norm_2, nullptr,
LLM_NORM_RMS, il);
cb(cur_moe, "ffn_moe", il);
cur = ggml_add(ctx0, cur_mlp, cur_moe);
cb(cur, "ffn_moe_combined", il);
} else {
cur = build_norm(attn_out,
model.layers[il].ffn_norm, nullptr,
LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
cur = build_ffn(cur,
model.layers[il].ffn_up, nullptr, model.layers[il].ffn_up_s,
model.layers[il].ffn_gate, nullptr, model.layers[il].ffn_gate_s,
model.layers[il].ffn_down, nullptr, model.layers[il].ffn_down_s,
nullptr,
LLM_FFN_GELU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
}
cur = build_norm(cur,
model.layers[il].ffn_post_norm, nullptr,
LLM_NORM_RMS, -1);
cb(cur, "ffn_post_norm", il);
// residual connection
cur = ggml_add(ctx0, cur, attn_out);
// per-layer embedding
if (inp_per_layer) {
ggml_tensor * pe_in = cur;
cb(cur, "pe_in", il);
cur = build_lora_mm(model.layers[il].per_layer_inp_gate, cur); // [n_embd_per_layer, n_tokens]
cur = ggml_gelu(ctx0, cur);
ggml_tensor * inp_this_layer = ggml_view_2d_slice(ctx0, inp_per_layer, il); // [n_embd_per_layer, n_tokens]
// TODO @ngxson : improve this
if (il == n_layer - 1 && inp_out_ids) {
inp_this_layer = ggml_get_rows(ctx0, inp_this_layer, inp_out_ids);
}
cur = ggml_mul(ctx0, cur, inp_this_layer);
cur = build_lora_mm(model.layers[il].per_layer_proj, cur); // [n_embd, n_tokens]
cur = build_norm(cur, model.layers[il].per_layer_post_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "per_layer_embd_out", il);
// residual connection
cur = ggml_add(ctx0, pe_in, cur);
}
// layer_scalar
if (model.layers[il].out_scale) {
cur = ggml_mul(ctx0, cur, model.layers[il].out_scale);
cb(cur, "out_scaled", il);
}
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = build_norm(cur,
model.output_norm, nullptr,
LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
// lm_head
cur = build_lora_mm(model.output, cur);
if (hparams.f_final_logit_softcapping) {
cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
cur = ggml_tanh(ctx0, cur);
cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);
}
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
// equivalent to get_per_layer_inputs() in python code
// output shape: [n_embd_per_layer, n_layer, n_tokens]
ggml_tensor * llama_model_gemma4::graph::build_inp_per_layer() {
auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
ggml_tensor * inp_per_layer;
float tok_embd_scale = sqrtf((float) n_embd_per_layer);
if (ubatch.token) {
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
ggml_set_input(inp->tokens);
res->t_inp_tokens = inp->tokens;
inp_per_layer = ggml_get_rows (ctx0, model.per_layer_tok_embd, inp->tokens);
inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_per_layer, n_layer, n_tokens);
inp_per_layer = ggml_scale (ctx0, inp_per_layer, tok_embd_scale);
cb(inp_per_layer, "inp_per_layer_selected", -1);
res->add_input(std::move(inp));
} else {
// Multimodal embedding path: use padding token (ID=0) embedding
// TODO: verify if this is the correct behavior in transformers implementation
const int64_t embd_size = model.per_layer_tok_embd->ne[0]; // n_embd_per_layer * n_layer
// Extract and dequantize padding token embedding (row 0)
ggml_tensor * padding = ggml_view_1d(ctx0, model.per_layer_tok_embd, embd_size, 0);
inp_per_layer = ggml_cast (ctx0, padding, GGML_TYPE_F32);
inp_per_layer = ggml_scale(ctx0, inp_per_layer, tok_embd_scale);
// Reshape to [n_embd_per_layer, n_layer, 1]
inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_per_layer, n_layer, 1);
cb(inp_per_layer, "inp_per_layer_multimodal", -1);
}
return inp_per_layer;
}
// equivalent to project_per_layer_inputs() in python code
// this calculates the per-layer inputs, so the final tensor shape will have n_layer as the last dim
// inp_batch shape: [n_embd, n_tokens]
// inp_per_layer shape: [n_embd_per_layer, n_layer, n_tokens] (from build_inp_per_layer)
// output shape: [n_embd_per_layer, n_tokens, n_layer]
ggml_tensor * llama_model_gemma4::graph::project_per_layer_inputs(ggml_tensor * inp_batch, ggml_tensor * inp_per_layer) {
const float per_layer_projection_scale = 1.0f / sqrtf((float) n_embd);
const float per_layer_input_scale = 1.0f / sqrtf(2.0f);
// note: this matrix multiplication will be performed in the input layer (i.e. on the CPU)
ggml_tensor * per_layer_proj;
per_layer_proj = ggml_mul_mat (ctx0, model.per_layer_model_proj, inp_batch);
per_layer_proj = ggml_scale (ctx0, per_layer_proj, per_layer_projection_scale);
per_layer_proj = ggml_reshape_3d(ctx0, per_layer_proj, n_embd_per_layer, n_layer, n_tokens);
per_layer_proj = build_norm(per_layer_proj, model.per_layer_proj_norm, nullptr, LLM_NORM_RMS, -1);
cb(per_layer_proj, "per_layer_proj", -1);
inp_per_layer = ggml_add (ctx0, per_layer_proj, inp_per_layer);
inp_per_layer = ggml_scale(ctx0, inp_per_layer, per_layer_input_scale);
cb(inp_per_layer, "inp_per_layer", -1);
// permute to shape: [n_embd_per_layer, n_tokens, n_layer]
inp_per_layer = ggml_cont(ctx0, ggml_permute(ctx0, inp_per_layer, 0, 2, 1, 3));
return inp_per_layer;
}
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