- [x] [Ling models](https://huggingface.co/collections/inclusionAI/ling-67c51c85b34a7ea0aba94c32)
- [x] [LFM2 models](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38)
- [x] [Hunyuan models](https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7)
+- [x] [BailingMoeV2 (Ring/Ling 2.0) models](https://huggingface.co/collections/inclusionAI/ling-v2-68bf1dd2fc34c306c1fa6f86)
#### Multimodal
# ref: https://huggingface.co/JetBrains/Mellum-4b-base
res = "mellum"
if chkhsh == "9b1be57e70d20d9501b2b3186e792d81181ae36ada3903c26f9fea418cf87206":
- # ref: https://huggingface.co/inclusionAI/LLaDA-MoE-7B-A1B-Base
- res = "llada-moe"
+ # ref: https://huggingface.co/inclusionAI/Ling-mini-base-2.0
+ res = "bailingmoe2"
if chkhsh == "53e325976a6e142379c19b09afcae354f2f496f147afa8f9e189a33fe4e3024e":
# ref: https://huggingface.co/ibm-granite/granite-docling-258M
res = "granite-docling"
raise ValueError(f"Unprocessed experts: {experts}")
+@ModelBase.register("BailingMoeV2ForCausalLM")
+class BailingMoeV2Model(TextModel):
+ model_arch = gguf.MODEL_ARCH.BAILINGMOE2
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ if nextn_layers := self.hparams.get("num_nextn_predict_layers", 0):
+ self.block_count = self.hparams["num_hidden_layers"] + nextn_layers
+ self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+ def set_vocab(self):
+ self._set_vocab_gpt2()
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters()
+ hparams = self.hparams
+ if (rope_dim := hparams.get("head_dim")) is None:
+ rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+
+ self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
+ rope_scaling = self.hparams.get("rope_scaling") or {}
+ if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
+ self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+ self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
+ self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
+ else:
+ self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+ self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
+ self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+ self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
+ self.gguf_writer.add_expert_shared_feed_forward_length(hparams.get("moe_shared_expert_intermediate_size", hparams["moe_intermediate_size"] * hparams["num_shared_experts"]))
+ self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
+ self.gguf_writer.add_expert_count(hparams["num_experts"])
+ self.gguf_writer.add_expert_shared_count(hparams["num_shared_experts"])
+ self.gguf_writer.add_expert_group_count(hparams["n_group"])
+ self.gguf_writer.add_expert_group_used_count(hparams["topk_group"])
+ self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])
+
+ if hparams["score_function"] == "sigmoid":
+ self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+ elif hparams["score_function"] == "softmax":
+ self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
+ else:
+ raise ValueError(f"Unsupported score_function value: {hparams['score_function']}")
+
+ if (nextn_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
+ self.gguf_writer.add_nextn_predict_layers(nextn_layers)
+
+ _experts: list[dict[str, Tensor]] | None = None
+
+ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+ if "mlp.experts" in name:
+ n_experts = self.hparams["num_experts"]
+ assert bid is not None
+
+ tensors: list[tuple[str, Tensor]] = []
+
+ if self._experts is None:
+ self._experts = [{} for _ in range(self.block_count)]
+
+ self._experts[bid][name] = data_torch
+
+ if len(self._experts[bid]) >= n_experts * 3:
+ # merge the experts into a single 3d tensor
+ for w_name in ["down_proj", "gate_proj", "up_proj"]:
+ datas: list[Tensor] = []
+
+ for xid in range(n_experts):
+ ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+ datas.append(self._experts[bid][ename])
+ del self._experts[bid][ename]
+
+ data_torch = torch.stack(datas, dim=0)
+
+ merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+ new_name = self.map_tensor_name(merged_name)
+
+ tensors.append((new_name, data_torch))
+
+ return tensors
+
+ if name.endswith(".expert_bias"):
+ name = name.replace(".expert_bias", ".expert_bias.bias")
+
+ return [(self.map_tensor_name(name), data_torch)]
+
+ def prepare_tensors(self):
+ super().prepare_tensors()
+
+ if self._experts is not None:
+ # flatten `list[dict[str, Tensor]]` into `list[str]`
+ experts = [k for d in self._experts for k in d.keys()]
+ if len(experts) > 0:
+ raise ValueError(f"Unprocessed experts: {experts}")
+
+
@ModelBase.register("GroveMoeForCausalLM", "modeling_grove_moe.GroveMoeForCausalLM")
class GroveMoeModel(TextModel):
model_arch = gguf.MODEL_ARCH.GROVEMOE
{"name": "lfm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"},
{"name": "exaone4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", },
{"name": "mellum", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", },
- {"name": "llada-moe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/LLaDA-MoE-7B-A1B-Base", },
+ {"name": "bailingmoe2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-mini-base-2.0", },
{"name": "granite-docling", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-docling-258M", },
]
EXPERT_COUNT = "{arch}.expert_count"
EXPERT_USED_COUNT = "{arch}.expert_used_count"
EXPERT_SHARED_COUNT = "{arch}.expert_shared_count"
+ EXPERT_GROUP_COUNT = "{arch}.expert_group_count"
+ EXPERT_GROUP_USED_COUNT = "{arch}.expert_group_used_count"
EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale"
EXPERT_WEIGHTS_NORM = "{arch}.expert_weights_norm"
EXPERT_GATING_FUNC = "{arch}.expert_gating_func"
WAVTOKENIZER_DEC = auto()
PLM = auto()
BAILINGMOE = auto()
+ BAILINGMOE2 = auto()
DOTS1 = auto()
ARCEE = auto()
ERNIE4_5 = auto()
MODEL_ARCH.WAVTOKENIZER_DEC: "wavtokenizer-dec",
MODEL_ARCH.PLM: "plm",
MODEL_ARCH.BAILINGMOE: "bailingmoe",
+ MODEL_ARCH.BAILINGMOE2: "bailingmoe2",
MODEL_ARCH.DOTS1: "dots1",
MODEL_ARCH.ARCEE: "arcee",
MODEL_ARCH.ERNIE4_5: "ernie4_5",
MODEL_TENSOR.FFN_DOWN_SHEXP,
MODEL_TENSOR.FFN_UP_SHEXP,
],
+ MODEL_ARCH.BAILINGMOE2: [
+ MODEL_TENSOR.TOKEN_EMBD,
+ MODEL_TENSOR.OUTPUT_NORM,
+ MODEL_TENSOR.OUTPUT,
+ MODEL_TENSOR.ATTN_NORM,
+ MODEL_TENSOR.ATTN_Q_NORM,
+ MODEL_TENSOR.ATTN_K_NORM,
+ MODEL_TENSOR.ATTN_QKV,
+ MODEL_TENSOR.ATTN_OUT,
+ MODEL_TENSOR.FFN_GATE_INP,
+ MODEL_TENSOR.FFN_EXP_PROBS_B,
+ MODEL_TENSOR.FFN_NORM,
+ MODEL_TENSOR.FFN_GATE,
+ MODEL_TENSOR.FFN_DOWN,
+ MODEL_TENSOR.FFN_UP,
+ MODEL_TENSOR.FFN_GATE_EXP,
+ MODEL_TENSOR.FFN_DOWN_EXP,
+ MODEL_TENSOR.FFN_UP_EXP,
+ MODEL_TENSOR.FFN_GATE_SHEXP,
+ MODEL_TENSOR.FFN_DOWN_SHEXP,
+ MODEL_TENSOR.FFN_UP_SHEXP,
+ MODEL_TENSOR.NEXTN_EH_PROJ,
+ MODEL_TENSOR.NEXTN_EMBED_TOKENS,
+ MODEL_TENSOR.NEXTN_ENORM,
+ MODEL_TENSOR.NEXTN_HNORM,
+ MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
+ MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
+ MODEL_TENSOR.LAYER_OUT_NORM,
+ ],
MODEL_ARCH.DOTS1: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
def add_expert_shared_count(self, count: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_SHARED_COUNT.format(arch=self.arch), count)
+ def add_expert_group_count(self, count: int) -> None:
+ self.add_uint32(Keys.LLM.EXPERT_GROUP_COUNT.format(arch=self.arch), count)
+
+ def add_expert_group_used_count(self, count: int) -> None:
+ self.add_uint32(Keys.LLM.EXPERT_GROUP_USED_COUNT.format(arch=self.arch), count)
+
def add_expert_weights_scale(self, value: float) -> None:
self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value)
"h.{bid}.self_attention.query_key_value", # bloom
"language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon
"model.layers.{bid}.self_attn.query_key_value", # persimmon
+ "model.layers.{bid}.attention.query_key_value", # bailingmoe2
"h.{bid}.attn.c_attn", # gpt2
"transformer.h.{bid}.mixer.Wqkv", # phi2
"encoder.layers.{bid}.attn.Wqkv", # nomic-bert
"transformer.h.{bid}.attn.out_proj", # gpt-j
"language_model.encoder.layers.{bid}.self_attention.dense", # persimmon
"model.layers.{bid}.self_attn.dense", # persimmon
+ "model.layers.{bid}.attention.dense", # bailingmoe2
"h.{bid}.attn.c_proj", # gpt2
"transformer.h.{bid}.mixer.out_proj", # phi2
"model.layers.layers.{bid}.self_attn.o_proj", # plamo
MODEL_TENSOR.FFN_EXP_PROBS_B: (
"model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3 dots1
"model.layers.{bid}.mlp.moe_statics.e_score_correction", # ernie4.5-moe
+ "model.layers.{bid}.mlp.gate.expert_bias", # bailingmoe2
"model.layers.{bid}.feed_forward.expert_bias", # lfm2moe
),
"language_model.encoder.layers.{bid}.self_attention.q_layernorm",
"model.layers.{bid}.self_attn.q_layernorm", # persimmon
"model.layers.{bid}.self_attn.query_layernorm", # hunyuan
+ "model.layers.{bid}.attention.query_layernorm", # bailingmoe2
"model.layers.{bid}.self_attn.q_norm", # cohere olmoe chameleon olmo2
"layers.{bid}.self_attn.q_norm", # embeddinggemma
"transformer.blocks.{bid}.attn.q_ln", # sea-lion
"language_model.encoder.layers.{bid}.self_attention.k_layernorm",
"model.layers.{bid}.self_attn.k_layernorm", # persimmon
"model.layers.{bid}.self_attn.key_layernorm", # hunyuan
+ "model.layers.{bid}.attention.key_layernorm", # bailingmoe2
"model.layers.{bid}.self_attn.k_norm", # cohere olmoe chameleon olmo2
"layers.{bid}.self_attn.k_norm", # embeddinggemma
"transformer.blocks.{bid}.attn.k_ln", # sea-lion
"transformer.decoder_layer.{bid}.rms_norm_3", # Grok
"encoder.layer.{bid}.mlp.layernorm", # jina-bert-v2
"encoder.layer.{bid}.layer_norm_2", # jina-v2-code
+ "model.layers.{bid}.final_layernorm", # bailingmoe2
),
MODEL_TENSOR.PER_LAYER_TOKEN_EMBD: (
{ LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
{ LLM_ARCH_PLM, "plm" },
{ LLM_ARCH_BAILINGMOE, "bailingmoe" },
+ { LLM_ARCH_BAILINGMOE2, "bailingmoe2" },
{ LLM_ARCH_DOTS1, "dots1" },
{ LLM_ARCH_ARCEE, "arcee" },
{ LLM_ARCH_ERNIE4_5, "ernie4_5" },
{ 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" },
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
},
},
+ {
+ 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_DOTS1,
{
LLM_ARCH_WAVTOKENIZER_DEC,
LLM_ARCH_PLM,
LLM_ARCH_BAILINGMOE,
+ LLM_ARCH_BAILINGMOE2,
LLM_ARCH_DOTS1,
LLM_ARCH_ARCEE,
LLM_ARCH_ERNIE4_5,
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,
{ "megrez", LLM_CHAT_TEMPLATE_MEGREZ },
{ "yandex", LLM_CHAT_TEMPLATE_YANDEX },
{ "bailing", LLM_CHAT_TEMPLATE_BAILING },
+ { "bailing-think", LLM_CHAT_TEMPLATE_BAILING_THINK },
+ { "bailing2", LLM_CHAT_TEMPLATE_BAILING2 },
{ "llama4", LLM_CHAT_TEMPLATE_LLAMA4 },
{ "smolvlm", LLM_CHAT_TEMPLATE_SMOLVLM },
{ "hunyuan-moe", LLM_CHAT_TEMPLATE_HUNYUAN_MOE },
return LLM_CHAT_TEMPLATE_YANDEX;
} 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|>")) {
if (add_ass) {
ss << " Ассистент:[SEP]";
}
- } else if (tmpl == LLM_CHAT_TEMPLATE_BAILING) {
- // Bailing (Ling) template
+ } 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);
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>";
}
LLM_CHAT_TEMPLATE_MEGREZ,
LLM_CHAT_TEMPLATE_YANDEX,
LLM_CHAT_TEMPLATE_BAILING,
+ LLM_CHAT_TEMPLATE_BAILING_THINK,
+ LLM_CHAT_TEMPLATE_BAILING2,
LLM_CHAT_TEMPLATE_LLAMA4,
LLM_CHAT_TEMPLATE_SMOLVLM,
LLM_CHAT_TEMPLATE_DOTS1,
cb(selection_probs, "ffn_moe_probs_biased", il);
}
+ // select top n_group_used expert groups
+ // https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/e815299b0bcbac849fa540c768ef21845365c9eb/modeling_deepseek.py#L440-L457
+ if (hparams.n_expert_groups > 1 && n_tokens > 0) {
+ const int64_t n_exp_per_group = n_expert / hparams.n_expert_groups;
+
+ // organize experts into n_expert_groups
+ ggml_tensor * selection_groups = ggml_reshape_3d(ctx0, selection_probs, n_exp_per_group, hparams.n_expert_groups, n_tokens); // [n_exp_per_group, n_expert_groups, n_tokens]
+
+ ggml_tensor * group_scores = ggml_top_k(ctx0, selection_groups, 2); // [2, n_expert_groups, n_tokens]
+ group_scores = ggml_get_rows(ctx0, ggml_reshape_4d(ctx0, selection_groups, 1, selection_groups->ne[0], selection_groups->ne[1], selection_groups->ne[2]), group_scores); // [1, 2, n_expert_groups, n_tokens]
+
+ // get top n_group_used expert groups
+ group_scores = ggml_sum_rows(ctx0, ggml_reshape_3d(ctx0, group_scores, group_scores->ne[1], group_scores->ne[2], group_scores->ne[3])); // [1, n_expert_groups, n_tokens]
+ group_scores = ggml_reshape_2d(ctx0, group_scores, group_scores->ne[1], group_scores->ne[2]); // [n_expert_groups, n_tokens]
+
+ ggml_tensor * expert_groups = ggml_top_k(ctx0, group_scores, hparams.n_group_used); // [n_group_used, n_tokens]
+ cb(expert_groups, "ffn_moe_group_topk", il);
+
+ // mask out the other groups
+ selection_probs = ggml_get_rows(ctx0, selection_groups, expert_groups); // [n_exp_per_group, n_group_used, n_tokens]
+ selection_probs = ggml_set_rows(ctx0, ggml_scale_bias(ctx0, selection_groups, 0.0f, -INFINITY), selection_probs, expert_groups); // [n_exp_per_group, n_expert_groups, n_tokens]
+ selection_probs = ggml_reshape_2d(ctx0, selection_probs, n_expert, n_tokens); // [n_expert, n_tokens]
+ cb(selection_probs, "ffn_moe_probs_masked", il);
+ }
+
// select experts
ggml_tensor * selected_experts = ggml_top_k(ctx0, selection_probs, n_expert_used); // [n_expert_used, n_tokens]
cb(selected_experts->src[0], "ffn_moe_argsort", il);
ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights); // [1, n_tokens]
cb(weights_sum, "ffn_moe_weights_sum", il);
+ if (arch == LLM_ARCH_BAILINGMOE2) {
+ weights_sum = ggml_scale_bias(ctx0, weights_sum, 1.0, 1e-20);
+ cb(weights_sum, "ffn_moe_weights_sum_biased", il);
+ }
+
weights = ggml_div(ctx0, weights, weights_sum); // [n_expert_used, n_tokens]
cb(weights, "ffn_moe_weights_norm", il);
uint32_t n_ff_chexp = 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;
case LLM_TYPE_A13B: return "A13B";
case LLM_TYPE_7B_A1B: return "7B.A1B";
case LLM_TYPE_8B_A1B: return "8B.A1B";
+ case LLM_TYPE_16B_A1B: return "16B.A1B";
case LLM_TYPE_21B_A3B: return "21B.A3B";
case LLM_TYPE_30B_A3B: return "30B.A3B";
+ case LLM_TYPE_100B_A6B: return "100B.A6B";
case LLM_TYPE_106B_A12B: return "106B.A12B";
case LLM_TYPE_235B_A22B: return "235B.A22B";
case LLM_TYPE_300B_A47B: return "300B.A47B";
return;
}
- ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train);
- ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd);
- ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer);
- ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
- ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
+ ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train);
+ ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd);
+ ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer);
+ ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
+ ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
+ ml.get_key(LLM_KV_EXPERT_GROUP_COUNT, hparams.n_expert_groups, false);
+ ml.get_key(LLM_KV_EXPERT_GROUP_USED_COUNT, hparams.n_group_used, false);
if (arch == LLM_ARCH_WAVTOKENIZER_DEC) {
ml.get_key(LLM_KV_FEATURES_LENGTH, hparams.n_embd_features);
GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
if (hparams.n_expert > 0) {
GGML_ASSERT(hparams.n_expert_used > 0);
+ GGML_ASSERT(hparams.n_expert_groups < hparams.n_expert);
+ if (hparams.n_expert_groups > 1) {
+ GGML_ASSERT(hparams.n_expert % hparams.n_expert_groups == 0);
+ GGML_ASSERT(hparams.n_group_used > 0);
+ GGML_ASSERT(hparams.n_group_used < hparams.n_expert_groups);
+ }
} else {
GGML_ASSERT(hparams.n_expert_used == 0);
+ GGML_ASSERT(hparams.n_expert_groups == 0);
}
std::fill(hparams.n_head_arr.begin(), hparams.n_head_arr.end(), 0);
default: type = LLM_TYPE_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_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: type = LLM_TYPE_16B_A1B; break;
+ case 21: type = LLM_TYPE_16B_A1B; break;
+ case 32: type = LLM_TYPE_100B_A6B; break;
+ case 33: type = LLM_TYPE_100B_A6B; break;
+ default: type = LLM_TYPE_UNKNOWN;
+ }
+ } break;
case LLM_ARCH_DOTS1:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
}
} break;
+ case LLM_ARCH_BAILINGMOE2:
+ {
+ const int64_t n_ff_exp = hparams.n_ff_exp;
+ const int64_t n_expert_shared = hparams.n_expert_shared;
+
+ tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+ // output
+ output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+ output = create_tensor(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) {
+ 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 |= TENSOR_SKIP;
+ }
+
+ auto & layer = layers[i];
+
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
+
+ layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, flags);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, flags);
+
+ layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, flags);
+ layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, flags);
+
+ layer.ffn_norm = create_tensor(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(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
+ layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | flags);
+
+ layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
+ layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags);
+ layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
+
+ layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, flags);
+ layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, flags);
+ layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, flags);
+ } else { // Dense layers
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, flags);
+ layer.ffn_up = create_tensor(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(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
+ layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
+ layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
+ layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
+ layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
+ layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED | flags);
+ layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, flags);
+ }
+ }
+ } break;
case LLM_ARCH_DOTS1:
{
const int64_t n_ff_exp = hparams.n_ff_exp;
LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm);
}
+ if (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((llama_expert_gating_func_type) hparams.expert_gating_func));
+ LLAMA_LOG_INFO("%s: nextn_predict_layers = %d\n", __func__, hparams.nextn_predict_layers);
+ }
+
if (arch == LLM_ARCH_SMALLTHINKER || arch == LLM_ARCH_LFM2MOE) {
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
}
};
+struct llm_build_bailingmoe2 : public llm_graph_context {
+ llm_build_bailingmoe2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+ 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 = build_inp_embd(model.tok_embd);
+
+ // inp_pos - contains the positions
+ ggml_tensor * inp_pos = build_inp_pos();
+
+ auto * inp_attn = build_attn_inp_kv();
+
+ 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 = build_norm(inpL,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+
+ // self_attention
+ {
+ cur = build_lora_mm(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));
+
+ Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, 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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, 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 = build_attn(inp_attn,
+ model.layers[il].wo, model.layers[il].bo,
+ Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), 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 = build_norm(sa_out,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ if (static_cast<uint32_t>(il) < hparams.n_layer_dense_lead) {
+ cur = build_ffn(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, il);
+ cb(cur, "ffn_out", il);
+ } else {
+ ggml_tensor * moe_out =
+ build_moe_ffn(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,
+ (llama_expert_gating_func_type) hparams.expert_gating_func,
+ il);
+ cb(moe_out, "ffn_moe_out", il);
+
+ {
+ ggml_tensor * ffn_shexp = build_ffn(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, 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 = build_cvec(cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = build_norm(cur,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, -1);
+
+ cb(cur, "result_norm", -1);
+ res->t_embd = cur;
+
+ // lm_head
+ cur = build_lora_mm(model.output, cur);
+
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+ }
+};
+
struct llm_build_dots1 : public llm_graph_context {
llm_build_dots1(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
{
llm = std::make_unique<llm_build_bailingmoe>(*this, params);
} break;
+ case LLM_ARCH_BAILINGMOE2:
+ {
+ llm = std::make_unique<llm_build_bailingmoe2>(*this, params);
+ } break;
case LLM_ARCH_SEED_OSS:
{
llm = std::make_unique<llm_build_seed_oss>(*this, params);
case LLM_ARCH_EXAONE:
case LLM_ARCH_EXAONE4:
case LLM_ARCH_MINICPM3:
+ case LLM_ARCH_BAILINGMOE2:
case LLM_ARCH_DOTS1:
case LLM_ARCH_HUNYUAN_MOE:
case LLM_ARCH_OPENAI_MOE:
LLM_TYPE_A13B,
LLM_TYPE_7B_A1B,
LLM_TYPE_8B_A1B, // lfm2moe
+ LLM_TYPE_16B_A1B,
LLM_TYPE_21B_A3B, // Ernie MoE small
LLM_TYPE_30B_A3B,
+ LLM_TYPE_100B_A6B,
LLM_TYPE_106B_A12B, // GLM-4.5-Air
LLM_TYPE_235B_A22B,
LLM_TYPE_300B_A47B, // Ernie MoE big
clean_spaces = false;
} else if (
tokenizer_pre == "bailingmoe" ||
+ tokenizer_pre == "bailingmoe2" ||
tokenizer_pre == "llada-moe") {
pre_type = LLAMA_VOCAB_PRE_TYPE_BAILINGMOE;
clean_spaces = false;
overview / pkg / ggml / sources / llama.cpp / commitdiff