ikawrakow/ik_llama.cpp
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Adding Ling/Ring (a.k.a., Bailing-MoE2)

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This commit is contained in:
Iwan Kawrakow
2025-10-14 16:24:16 +03:00
parent 8d0d01a593
commit 6dc8c79d81
11 changed files with 381 additions and 19 deletions
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9
src/llama-arch.cpp
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@@ -58,11 +58,12 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_GRANITE, "granite" },
{ LLM_ARCH_GRANITE_MOE, "granitemoe" },
{ LLM_ARCH_COHERE2, "cohere2" },
{ LLM_ARCH_DOTS1, "dots1" },
{ LLM_ARCH_ERNIE4_5, "ernie4_5" },
{ LLM_ARCH_ERNIE4_5_MOE, "ernie4_5-moe" },
{ LLM_ARCH_DOTS1, "dots1" },
{ LLM_ARCH_ERNIE4_5, "ernie4_5" },
{ LLM_ARCH_ERNIE4_5_MOE, "ernie4_5-moe" },
{ LLM_ARCH_HUNYUAN_MOE, "hunyuan-moe" },
{ LLM_ARCH_OPENAI_MOE, "gpt-oss" },
{ LLM_ARCH_BAILINGMOE2, "bailingmoe2" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@@ -103,6 +104,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_EXPERT_COUNT, "%s.expert_count" },
{ LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" },
{ LLM_KV_EXPERT_SHARED_COUNT, "%s.expert_shared_count" },
{ LLM_KV_EXPERT_GROUP_COUNT, "%s.expert_group_count" },
{ LLM_KV_EXPERT_GROUP_USED_COUNT, "%s.expert_group_used_count" },
{ LLM_KV_EXPERT_WEIGHTS_SCALE, "%s.expert_weights_scale" },
{ LLM_KV_EXPERT_WEIGHTS_NORM, "%s.expert_weights_norm" },
{ LLM_KV_EXPERT_GATING_FUNC, "%s.expert_gating_func" },

3
src/llama-arch.h
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@@ -62,6 +62,7 @@ enum llm_arch {
LLM_ARCH_ERNIE4_5_MOE,
LLM_ARCH_HUNYUAN_MOE,
LLM_ARCH_OPENAI_MOE,
LLM_ARCH_BAILINGMOE2,
LLM_ARCH_UNKNOWN,
};
@@ -92,6 +93,8 @@ enum llm_kv {
LLM_KV_EXPERT_COUNT,
LLM_KV_EXPERT_USED_COUNT,
LLM_KV_EXPERT_SHARED_COUNT,
LLM_KV_EXPERT_GROUP_COUNT,
LLM_KV_EXPERT_GROUP_USED_COUNT,
LLM_KV_EXPERT_WEIGHTS_SCALE,
LLM_KV_EXPERT_WEIGHTS_NORM,
LLM_KV_EXPERT_GATING_FUNC,

137
src/llama-build-context.cpp
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@@ -8192,6 +8192,139 @@ ggml_cgraph * llm_build_context::build_openai_moe() {
return gf;
}
ggml_cgraph * llm_build_context::build_bailingmoe2() {
ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
//auto * inp_attn = build_attn_inp_kv();
ggml_tensor * KQ_mask = build_inp_KQ_mask();
//const int64_t n_embd_head = hparams.n_embd_head_v;
const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
ggml_tensor * inp_out_ids = build_inp_out_ids();
const int n_transformer_layers = n_layer - hparams.nextn_predict_layers;
for (int il = 0; il < n_transformer_layers; ++il) {
ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
// self_attention
{
cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd));
//ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, cb, il);
cb(Qcur, "Qcur_normed", il);
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, cb, il);
cb(Kcur, "Kcur_normed", il);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
cur = llm_build_kv(ctx0, lctx, kv_self, gf, model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
}
if (il == n_transformer_layers - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * sa_out = ggml_add(ctx0, cur, inpSA);
cb(sa_out, "sa_out", il);
// MoE branch
cur = llm_build_norm(ctx0, sa_out, hparams, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
if (static_cast<uint32_t>(il) < hparams.n_layer_dense_lead) {
cur = llm_build_ffn(ctx0, lctx, 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);
cb(cur, "ffn_out", il);
} else {
ggml_tensor * moe_out =
llm_build_moe_ffn(ctx0, lctx, cur,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
model.layers[il].ffn_gate_exps,
model.layers[il].ffn_down_exps,
model.layers[il].ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_SILU, hparams.expert_weights_norm,
true, hparams.expert_weights_scale,
(llm_expert_gating_func_type) hparams.expert_gating_func,
cb, il, gf);
cb(moe_out, "ffn_moe_out", il);
ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, lctx, cur,
model.layers[il].ffn_up_shexp, NULL, NULL,
model.layers[il].ffn_gate_shexp, NULL, NULL,
model.layers[il].ffn_down_shexp, NULL, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(ffn_shexp, "ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, sa_out);
cur = lctx.cvec.apply_to(ctx0, cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
ggml_cgraph * llm_build_context::llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
llama_batch dummy;
dummy.n_tokens = 0;
@@ -8513,6 +8646,10 @@ ggml_cgraph * llm_build_context::llama_build_graph(
{
result = llm.build_openai_moe();
} break;
case LLM_ARCH_BAILINGMOE2:
{
result = llm.build_bailingmoe2();
} break;
default:
GGML_ABORT("fatal error");
}

2
src/llama-build-context.h
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@@ -251,6 +251,8 @@ struct llm_build_context {
ggml_cgraph * build_openai_moe();
ggml_cgraph * build_bailingmoe2();
//
static ggml_tensor * llm_build_lora_mm(llama_context & lctx, ggml_context * ctx0,
ggml_tensor * w, ggml_tensor * cur);

25
src/llama-hparams.cpp
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@@ -894,6 +894,31 @@ void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_BAILINGMOE2:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_GROUP_COUNT, hparams.n_expert_groups);
ml.get_key(LLM_KV_EXPERT_GROUP_USED_COUNT, hparams.n_group_used);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
// TODO: when MTP is implemented, this should probably be updated if needed
hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
switch (hparams.n_layer) {
case 20: model.type = MODEL_16B_A1B; break;
case 21: model.type = MODEL_16B_A1B; break;
case 32: model.type = MODEL_100B_A6B; break;
case 33: model.type = MODEL_100B_A6B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_DOTS1:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

39
src/llama-hparams.h
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@@ -24,6 +24,7 @@ struct llama_hparams {
uint32_t n_ctx_train; // context size the model was trained on
uint32_t n_embd;
uint32_t n_layer;
int32_t n_layer_kv_from_start = -1; // if non-negative, the first n_layer_kv_from_start layers have KV cache
uint32_t n_rot;
uint32_t n_swa = 0; // sliding window attention (SWA)
uint32_t n_swa_pattern = 1; // by default, all layers use non-sliding-window attention
@@ -39,22 +40,30 @@ struct llama_hparams {
std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
uint32_t n_layer_dense_lead = 0;
uint32_t n_lora_q = 0;
uint32_t n_lora_kv = 0;
uint32_t n_ff_exp = 0;
uint32_t n_ff_shexp = 0;
uint32_t n_expert_shared = 0;
float expert_weights_scale = 0.0;
bool expert_weights_norm = false;
uint32_t expert_gating_func = LLM_EXPERT_GATING_FUNC_SOFTMAX;
uint32_t n_lora_q = 0;
uint32_t n_lora_kv = 0;
uint32_t n_ff_exp = 0;
uint32_t n_ff_shexp = 0;
uint32_t n_expert_shared = 0;
uint32_t n_norm_groups = 0;
uint32_t n_expert_groups = 0;
uint32_t n_group_used = 0;
uint32_t n_group_experts = 0;
float expert_group_scale = 0.05f;
float expert_weights_scale = 0.0f;
bool expert_weights_norm = false;
uint32_t expert_gating_func = LLM_EXPERT_GATING_FUNC_SOFTMAX;
uint32_t moe_every_n_layers = 0;
uint32_t nextn_predict_layers = 0;
float f_norm_eps;
float f_norm_rms_eps;
float f_norm_group_eps;
float f_attn_logit_softcapping = 50.0f;
float f_attn_logit_softcapping = 50.0f;
float f_router_logit_softcapping = 30.0f;
float f_final_logit_softcapping = 30.0f;
float f_final_logit_softcapping = 30.0f;
float rope_attn_factor = 1.0f;
float rope_freq_base_train;
@@ -62,12 +71,12 @@ struct llama_hparams {
float rope_freq_scale_train;
float rope_freq_scale_train_swa;
uint32_t n_ctx_orig_yarn;
float rope_yarn_log_mul;
float rope_yarn_log_mul = 0.0f;
float yarn_ext_factor = -1.0f;
float yarn_attn_factor = 1.0f;
float yarn_beta_fast = 32.0f;
float yarn_beta_slow = 1.0f;
float yarn_ext_factor = -1.0f;
float yarn_attn_factor = 1.0f;
float yarn_beta_fast = 32.0f;
float yarn_beta_slow = 1.0f;
std::array<int, 4> rope_sections;

75
src/llama-load-tensors.cpp
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@@ -124,6 +124,8 @@ struct create_tensors_helper : public create_tensors_helper_interface {
bool create_openai_moe_tensors(const LLM_TN & tn);
bool create_bailingmoe2_tensors(const LLM_TN & tn);
llama_model_loader & ml;
llama_model & model;
@@ -2205,6 +2207,77 @@ bool create_tensors_helper::create_dots1_tensors(const LLM_TN & tn) {
return use_mmap_buffer;
}
bool create_tensors_helper::create_bailingmoe2_tensors(const LLM_TN & tn) {
LOADING_PRELUDE
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_expert_shared = hparams.n_expert_shared;
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for bailingmoe2");
GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for bailingmoe2");
for (int i = 0; i < n_layer; ++i) {
auto & layer = model.layers[i];
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
int flags = 0;
if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
// skip all tensors in the NextN layers
flags |= llama_model_loader::TENSOR_SKIP;
}
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, flags);
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, flags);
layer.attn_q_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, flags);
layer.attn_k_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, flags);
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
if (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) { // MoE layers
const int64_t n_ff_shexp = (hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp) * n_expert_shared;
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
layer.ffn_exp_probs_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert},
llama_model_loader::TENSOR_NOT_REQUIRED | flags);
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags);
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
layer.ffn_gate_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, flags);
layer.ffn_down_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, flags);
layer.ffn_up_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, flags);
} else { // Dense layers
layer.ffn_gate = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, flags);
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, flags);
}
// NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
layer.nextn.eh_proj = create_tensor(ctx_split, tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
layer.nextn.embed_tokens = create_tensor(ctx_split, tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab },
llama_model_loader::TENSOR_NOT_REQUIRED | flags);
layer.nextn.enorm = create_tensor(ctx_layer, tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
layer.nextn.hnorm = create_tensor(ctx_layer, tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
layer.nextn.shared_head_head = create_tensor(ctx_split, tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, llama_model_loader::TENSOR_NOT_REQUIRED | flags);
layer.nextn.shared_head_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED | flags);
layer.layer_out_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, flags);
}
}
return use_mmap_buffer;
}
bool create_tensors_helper::create_ernie45_tensors(const LLM_TN & tn) {
LOADING_PRELUDE
@@ -2460,6 +2533,8 @@ bool create_tensors_helper::create_tensors() {
use_mmap_buffer = create_hunyuan_tensors(tn); break;
case LLM_ARCH_OPENAI_MOE:
use_mmap_buffer = create_openai_moe_tensors(tn); break;
case LLM_ARCH_BAILINGMOE2:
use_mmap_buffer = create_bailingmoe2_tensors(tn); break;
default:
throw std::runtime_error("unknown architecture");
}

34
src/llama-model.cpp
View File

@@ -1157,6 +1157,38 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
{
LLM_ARCH_BAILINGMOE2,
{
{ 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_Q_NORM, "blk.%d.attn_q_norm" },
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ 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_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
{ LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
{ LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
{ LLM_TENSOR_NEXTN_EH_PROJ, "blk.%d.nextn.eh_proj" },
{ LLM_TENSOR_NEXTN_EMBED_TOKENS, "blk.%d.nextn.embed_tokens" },
{ LLM_TENSOR_NEXTN_ENORM, "blk.%d.nextn.enorm" },
{ LLM_TENSOR_NEXTN_HNORM, "blk.%d.nextn.hnorm" },
{ LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "blk.%d.nextn.shared_head_head" },
{ LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "blk.%d.nextn.shared_head_norm" },
{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
},
},
{
LLM_ARCH_UNKNOWN,
{
@@ -1368,6 +1400,8 @@ const char * llama_model_type_name(e_model type) {
case MODEL_80B_A13B: return "80B.A13B";
case MODEL_21B_A3B: return "21B.A3B";
case MODEL_300B_A47B: return "300B.A47B";
case MODEL_16B_A1B: return "16B.A1B";
case MODEL_100B_A6B: return "100B.A6B";
default: return "?B";
}
}

2
src/llama-model.h
View File

@@ -84,6 +84,8 @@ enum e_model {
MODEL_17B_128E,
MODEL_80B_A13B,
MODEL_300B_A47B, // Ernie MoE big
MODEL_16B_A1B,
MODEL_100B_A6B,
};
struct llama_layer_nextn {

4
src/llama-vocab.cpp
View File

@@ -1961,7 +1961,9 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
pre_type = LLAMA_VOCAB_PRE_TYPE_TRILLION;
clean_spaces = false;
} else if (
tokenizer_pre == "bailingmoe") {
tokenizer_pre == "bailingmoe" ||
tokenizer_pre == "bailingmoe2" ||
tokenizer_pre == "llada-moe") {
pre_type = LLAMA_VOCAB_PRE_TYPE_BAILINGMOE;
clean_spaces = false;
} else if (

70
src/llama.cpp
View File

@@ -194,6 +194,9 @@ enum llm_chat_template {
LLM_CHAT_TEMPLATE_KIMI_K2,
LLM_CHAT_TEMPLATE_OPENAI_MOE,
LLM_CHAT_TEMPLATE_GROK_2,
LLM_CHAT_TEMPLATE_BAILING,
LLM_CHAT_TEMPLATE_BAILING_THINK,
LLM_CHAT_TEMPLATE_BAILING2,
LLM_CHAT_TEMPLATE_UNKNOWN,
};
@@ -236,6 +239,10 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
{ "gpt-oss", LLM_CHAT_TEMPLATE_OPENAI_MOE },
{ "bitnet", LLM_CHAT_TEMPLATE_BITNET },
{ "grok-2", LLM_CHAT_TEMPLATE_GROK_2 },
{ "bailing", LLM_CHAT_TEMPLATE_BAILING },
{ "bailing-think", LLM_CHAT_TEMPLATE_BAILING_THINK },
{ "bailing2", LLM_CHAT_TEMPLATE_BAILING2 },
};
//
@@ -1200,6 +1207,19 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
}
if (model.arch == LLM_ARCH_BAILINGMOE2) {
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp);
LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared);
LLAMA_LOG_INFO("%s: n_expert_groups = %d\n", __func__, hparams.n_expert_groups);
LLAMA_LOG_INFO("%s: n_group_used = %d\n", __func__, hparams.n_group_used);
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm);
LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((llm_expert_gating_func_type) hparams.expert_gating_func));
LLAMA_LOG_INFO("%s: nextn_predict_layers = %d\n", __func__, hparams.nextn_predict_layers);
}
vocab.print_info();
}
@@ -4444,6 +4464,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_DOTS1:
case LLM_ARCH_HUNYUAN_MOE:
case LLM_ARCH_OPENAI_MOE:
case LLM_ARCH_BAILINGMOE2:
return LLAMA_ROPE_TYPE_NEOX;
case LLM_ARCH_QWEN2VL:
@@ -6255,6 +6276,12 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) {
return LLM_CHAT_TEMPLATE_GIGACHAT;
} else if (tmpl_contains("<|role_start|>")) {
return LLM_CHAT_TEMPLATE_MEGREZ;
} else if (tmpl_contains("<role>ASSISTANT</role>") && tmpl_contains("'HUMAN'")) {
return LLM_CHAT_TEMPLATE_BAILING;
} else if (tmpl_contains("<role>ASSISTANT</role>") && tmpl_contains("\"HUMAN\"") && tmpl_contains("<think>")) {
return LLM_CHAT_TEMPLATE_BAILING_THINK;
} else if (tmpl_contains("<role>ASSISTANT</role>") && tmpl_contains("<role>HUMAN</role>") && tmpl_contains("<|role_end|>")) {
return LLM_CHAT_TEMPLATE_BAILING2;
} else if (tmpl_contains("<|header_start|>") && tmpl_contains("<|header_end|>")) {
return LLM_CHAT_TEMPLATE_LLAMA4;
} else if (tmpl_contains("<|endofuserprompt|>")) {
@@ -6657,6 +6684,49 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "<|role_start|>assistant<|role_end|>";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_BAILING || tmpl == LLM_CHAT_TEMPLATE_BAILING_THINK) {
// Bailing (Ling/Ring) template
for (auto message : chat) {
std::string role(message->role);
if (role == "user") {
role = "HUMAN";
} else {
std::transform(role.begin(), role.end(), role.begin(), ::toupper);
}
ss << "<role>" << role << "</role>" << message->content;
}
if (add_ass) {
ss << "<role>ASSISTANT</role>";
if (tmpl == LLM_CHAT_TEMPLATE_BAILING_THINK) {
ss << "<think>";
}
}
} else if (tmpl == LLM_CHAT_TEMPLATE_BAILING2) {
// Bailing2 (Ling 2.0) template
bool has_system = !chat.empty() && std::string(chat[0]->role) == "system";
if (!has_system) {
ss << "<role>SYSTEM</role>detailed thinking off<|role_end|>";
}
for (auto message : chat) {
std::string role(message->role);
if (role == "user") {
role = "HUMAN";
} else {
std::transform(role.begin(), role.end(), role.begin(), ::toupper);
}
ss << "<role>" << role << "</role>" << message->content << "<|role_end|>";
}
if (add_ass) {
ss << "<role>ASSISTANT</role>";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_LLAMA4) {
// Llama 4
for (auto message : chat) {