ikawrakow/ik_llama.cpp
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iAdding support for dense Qwen-3.5 models (#1326)

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Kawrakow
2026-02-26 08:51:01 +01:00
committed by GitHub
parent 2616efa296
commit 0aa6f7e7cd
9 changed files with 263 additions and 1 deletions
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2
src/llama-arch.cpp
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@@ -31,6 +31,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_QWEN3VL, "qwen3vl" },
{ LLM_ARCH_QWEN3VLMOE, "qwen3vlmoe" },
{ LLM_ARCH_QWEN35MOE, "qwen35moe" },
{ LLM_ARCH_QWEN35, "qwen35" },
{ LLM_ARCH_PHI2, "phi2" },
{ LLM_ARCH_PHI3, "phi3" },
{ LLM_ARCH_PLAMO, "plamo" },
@@ -260,6 +261,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
switch (arch) {
case LLM_ARCH_QWEN3NEXT:
case LLM_ARCH_QWEN35MOE:
case LLM_ARCH_QWEN35:
return true;
default:
return false;

1
src/llama-arch.h
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@@ -30,6 +30,7 @@ enum llm_arch {
LLM_ARCH_QWEN3VL,
LLM_ARCH_QWEN3VLMOE,
LLM_ARCH_QWEN35MOE,
LLM_ARCH_QWEN35,
LLM_ARCH_PHI2,
LLM_ARCH_PHI3,
LLM_ARCH_PLAMO,

134
src/llama-build-context.cpp
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@@ -4663,6 +4663,136 @@ ggml_cgraph * llm_build_context::build_qwen35moe() {
return gf;
}
ggml_cgraph * llm_build_context::build_qwen35() {
static constexpr int QWEN3NEXT_CHUNK_SIZE = 64;
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model, n_tokens), false);
delta_net delta(lctx, batch);
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
int sections[4];
std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
auto build_layer_attn = [&](ggml_tensor * cur, ggml_tensor * inp_pos, ggml_tensor * KQ_mask, int il) -> ggml_tensor * {
auto Qaux = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
auto Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
auto Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
cb(Qaux, "Qaux", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
ggml_build_forward_expand(gf, Qaux);
ggml_build_forward_expand(gf, Kcur);
ggml_build_forward_expand(gf, Vcur);
Qaux = ggml_reshape_3d(ctx0, Qaux, n_embd_head * 2, n_head, n_tokens);
auto Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, Qaux, n_embd_head, n_head, n_tokens, Qaux->nb[1], Qaux->nb[2], 0));
auto gate = ggml_cont_2d(ctx0, ggml_view_3d(ctx0, Qaux, n_embd_head, n_head, n_tokens, Qaux->nb[1], Qaux->nb[2], n_embd_head*ggml_element_size(Qaux)), n_embd_head*n_head, n_tokens);
cb(Qcur, "Qcur", il);
cb(gate, "gate", 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);
Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, cb, il);
cb(Qcur, "Qcur_normed", il);
Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, cb, il);
cb(Kcur, "Kcur_normed", il);
Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,
n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,
n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
cb(Qcur, "Qcur_roped", il);
cb(Kcur, "Kcur_roped", il);
ggml_tensor * attn = llm_build_kv(ctx0, lctx, kv_self, gf, nullptr, nullptr,
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv,
hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale, cb, il);
cb(attn, "attn_pregate", il);
gate = ggml_sigmoid(ctx0, gate);
cb(gate, "gate_sigmoid", il);
attn = ggml_mul(ctx0, attn, gate);
cb(attn, "attn_gated", il);
attn = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, attn);
cb(attn, "attn_output", il);
return attn;
};
ggml_tensor * inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = n_tokens > 1 ? build_inp_out_ids() : nullptr;
ggml_tensor * KQ_mask = build_inp_KQ_mask();
lctx.inp_s_seq_qnext = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, 1, n_tokens);
cb(lctx.inp_s_seq_qnext, "inp_s_seq_qnext", -1);
ggml_set_input(lctx.inp_s_seq_qnext);
ggml_tensor * causal_mask = nullptr;
ggml_tensor * identity = nullptr;
ggml_tensor * diag_mask = nullptr;
causal_mask = ggml_tri(ctx0,
ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, QWEN3NEXT_CHUNK_SIZE, QWEN3NEXT_CHUNK_SIZE), 1.0f),
GGML_TRI_TYPE_LOWER);
identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, QWEN3NEXT_CHUNK_SIZE), 1.0f));
diag_mask = ggml_add(ctx0, causal_mask, identity);
ggml_build_forward_expand(gf, causal_mask);
ggml_build_forward_expand(gf, identity);
ggml_build_forward_expand(gf, diag_mask);
ggml_tensor * cur = nullptr;
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
if (hparams.is_recurrent(il)) {
cur = delta.build_layer_attn_linear(ctx0, gf, cur, causal_mask, identity, diag_mask, il, cb);
} else {
cur = build_layer_attn(cur, inp_pos, KQ_mask, il);
}
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
cur = ggml_add(ctx0, cur, inpSA);
cb(cur, "attn_residual", il);
cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, cur,
model.layers[il].ffn_up, NULL, NULL,
model.layers[il].ffn_gate, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il, gf, true, false);
cur = lctx.cvec.apply_to(ctx0, cur, il);
cb(cur, "l_out", il);
inpL = cur;
}
cur = build_output(lctx, ctx0, inpL, model.output, model.output_norm, cb);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
ggml_cgraph * llm_build_context::build_qwen3vl() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model, n_tokens), false);
@@ -9840,6 +9970,10 @@ ggml_cgraph * llm_build_context::llama_build_graph(
{
result = llm.build_qwen35moe();
} break;
case LLM_ARCH_QWEN35:
{
result = llm.build_qwen35();
} break;
case LLM_ARCH_QWEN3VL:
{
result = llm.build_qwen3vl();

2
src/llama-build-context.h
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@@ -213,6 +213,8 @@ struct llm_build_context {
ggml_cgraph * build_qwen35moe();
ggml_cgraph * build_qwen35();
ggml_cgraph * build_phi2();
ggml_cgraph * build_phi3();

27
src/llama-hparams.cpp
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@@ -507,6 +507,33 @@ void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_QWEN35:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
// Load linear attention (gated delta net) parameters
ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
// Mark recurrent layers (linear attention layers)
{
uint32_t full_attn_interval = 4;
ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
for (uint32_t i = 0; i < hparams.n_layer; ++i) {
hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
}
}
switch (hparams.n_layer) {
case 24: model.type = e_model::MODEL_2B; break;
case 64: model.type = e_model::MODEL_27B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_QWEN3VLMOE:
{
ml.get_key(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers, false);

67
src/llama-load-tensors.cpp
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@@ -77,6 +77,8 @@ struct create_tensors_helper : public create_tensors_helper_interface {
bool create_qwen35moe_tensors(const LLM_TN & tn);
bool create_qwen35_tensors(const LLM_TN & tn);
bool create_phi2_tensors(const LLM_TN & tn);
bool create_phi3_tensors(const LLM_TN & tn);
@@ -1465,6 +1467,69 @@ bool create_tensors_helper::create_qwen35moe_tensors(const LLM_TN & tn) {
return use_mmap_buffer;
}
bool create_tensors_helper::create_qwen35_tensors(const LLM_TN & tn) {
LOADING_PRELUDE
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
if (model.output == NULL) {
model.output = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
}
}
const int64_t head_k_dim = hparams.ssm_d_state;
const int64_t head_v_dim = hparams.ssm_d_state;
const int64_t n_k_heads = hparams.ssm_n_group;
const int64_t n_v_heads = hparams.ssm_dt_rank;
const int64_t key_dim = head_k_dim * n_k_heads;
const int64_t value_dim = head_v_dim * n_v_heads;
const int64_t conv_dim = key_dim * 2 + value_dim;
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
layer.attn_post_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
layer.ffn_norm = layer.attn_post_norm;
if (!hparams.is_recurrent(i)) {
// Attention layers
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0);
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, 0);
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
// Q/K normalization for attention layers
layer.attn_q_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
layer.attn_k_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
} else {
// Linear attention (gated delta net) specific tensors
// Create tensors with calculated dimensions
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, key_dim * 2 + value_dim }, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.wqkv_gate = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_GATE, "weight", i), { n_embd, value_dim }, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ssm_conv1d = create_tensor(ctx_split, tn(LLM_TENSOR_SSM_CONV1D, "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
layer.ssm_dt = create_tensor(ctx_split, tn(LLM_TENSOR_SSM_DT, "bias", i), { hparams.ssm_dt_rank }, 0);
layer.ssm_a = create_tensor(ctx_split, tn(LLM_TENSOR_SSM_A_NOSCAN, i), { hparams.ssm_dt_rank }, 0);
layer.ssm_beta = create_tensor(ctx_split, tn(LLM_TENSOR_SSM_BETA, "weight", i), { n_embd, n_v_heads }, 0);
layer.ssm_alpha = create_tensor(ctx_split, tn(LLM_TENSOR_SSM_ALPHA, "weight", i), { n_embd, n_v_heads }, 0);
layer.ssm_norm = create_tensor(ctx_split, tn(LLM_TENSOR_SSM_NORM, "weight", i), { head_v_dim }, 0);
layer.ssm_out = create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), { value_dim, n_embd }, 0);
}
layer.ffn_gate = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
}
return use_mmap_buffer;
}
bool create_tensors_helper::create_mimo2_tensors(const LLM_TN & tn) {
LOADING_PRELUDE
@@ -3402,6 +3467,8 @@ bool create_tensors_helper::create_tensors() {
use_mmap_buffer = create_qwen3next_tensors(tn); break;
case LLM_ARCH_QWEN35MOE:
use_mmap_buffer = create_qwen35moe_tensors(tn); break;
case LLM_ARCH_QWEN35:
use_mmap_buffer = create_qwen35_tensors(tn); break;
case LLM_ARCH_PHI2:
use_mmap_buffer = create_phi2_tensors(tn); break;
case LLM_ARCH_PHI3:

28
src/llama-model.cpp
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@@ -496,6 +496,34 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
},
},
{
LLM_ARCH_QWEN35,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_POST_NORM, "blk.%d.post_attention_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_GATE, "blk.%d.attn_gate" },
{ LLM_TENSOR_SSM_CONV1D, "blk.%d.ssm_conv1d" },
{ LLM_TENSOR_SSM_DT, "blk.%d.ssm_dt" },
{ LLM_TENSOR_SSM_A_NOSCAN, "blk.%d.ssm_a" },
{ LLM_TENSOR_SSM_BETA, "blk.%d.ssm_beta" },
{ LLM_TENSOR_SSM_ALPHA, "blk.%d.ssm_alpha" },
{ LLM_TENSOR_SSM_NORM, "blk.%d.ssm_norm" },
{ LLM_TENSOR_SSM_OUT, "blk.%d.ssm_out" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_QWEN3VL,
{

2
src/llama-model.h
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@@ -427,7 +427,7 @@ struct llama_model {
size_t max_nodes(int n_tokens) const {
auto n_tensors = tensors_by_name.size();
if (split_mode == LLAMA_SPLIT_MODE_GRAPH && !devices.empty()) n_tensors *= devices.size();
if (arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35MOE) {
if (arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35MOE || arch == LLM_ARCH_QWEN35) {
return std::max<size_t>(n_tokens * 40, 32u * n_tensors);
}
//return std::max<size_t>(1024, 8*n_tensors);

1
src/llama.cpp
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@@ -5358,6 +5358,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_QWEN3VL:
case LLM_ARCH_QWEN3VLMOE:
case LLM_ARCH_QWEN35MOE:
case LLM_ARCH_QWEN35:
return LLAMA_ROPE_TYPE_IMROPE;
// all model arches should be listed explicitly here