model: support MiMo-V2-Flash (#18328)
* mimov2: convert ok * rename mimov2 --> mimo2 * fix conversion * runnable not incorrect * use sink * add_sliding_window_pattern * add swa and per-layer n_head_kv * correct params * somewhat working * correct gating func * nits * mimo2: wire RMS eps + MoE bias + converter guards * add co-author Co-authored-by: Aaryan-Kapoor <Aaryan-Kapoor@users.noreply.github.com> * use add_rope_freq_base_swa --------- Co-authored-by: Aaryan Kapoor <aaryankapoor2006@gmail.com> Co-authored-by: Aaryan-Kapoor <Aaryan-Kapoor@users.noreply.github.com>
This commit is contained in:
Xuan-Son Nguyen
GitHub
parent
c184284230
commit
4cbafad4f0
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@ -7362,6 +7362,90 @@ class MiniMaxM2Model(TextModel):
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return super().modify_tensors(data_torch, name, bid)
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@ModelBase.register("MiMoV2FlashForCausalLM")
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class MimoV2Model(TextModel):
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model_arch = gguf.MODEL_ARCH.MIMO2
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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assert self.hparams["swa_head_dim"] == self.hparams["head_dim"]
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assert self.hparams["swa_num_attention_heads"] == self.hparams["num_attention_heads"]
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assert self.hparams["swa_v_head_dim"] == self.hparams["v_head_dim"]
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assert self.hparams["topk_method"] == "noaux_tc"
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n_head_kv = self.hparams["num_key_value_heads"]
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n_head_kv_swa = self.hparams["swa_num_key_value_heads"]
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n_head_kv_arr = [n_head_kv_swa if use_swa == 1 else n_head_kv for use_swa in self.hparams["hybrid_layer_pattern"]]
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self.gguf_writer.add_head_count_kv(n_head_kv_arr)
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self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
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self.gguf_writer.add_sliding_window_pattern(self.hparams["hybrid_layer_pattern"])
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self.gguf_writer.add_rope_freq_base_swa(self.hparams["swa_rope_theta"])
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self.gguf_writer.add_value_length(self.hparams["v_head_dim"])
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self.gguf_writer.add_expert_count(self.hparams["n_routed_experts"])
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self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
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rope_dim = int(self.hparams["head_dim"] * self.hparams["partial_rotary_factor"])
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self.gguf_writer.add_rope_dimension_count(rope_dim)
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self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon", 1e-5))
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_experts: list[dict[str, Tensor]] | None = None
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def modify_tensors(self, data_torch, name, bid):
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if name.endswith("e_score_correction_bias"):
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name = name.replace("e_score_correction_bias", "e_score_correction.bias")
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if "attention_sink" in name and not name.endswith(".weight"):
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name += ".weight"
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# TODO: mimo v2 does not indicate the number of next-token-prediction layers, therefore we cannot do the same way as GLM4_MOE
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if "model.mtp." in name:
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return []
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# process the experts separately
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if name.find("mlp.experts") != -1:
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n_experts = self.hparams["n_routed_experts"]
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assert bid is not None
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if self._experts is None:
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self._experts = [{} for _ in range(self.block_count)]
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self._experts[bid][name] = data_torch
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if len(self._experts[bid]) >= n_experts * 3:
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tensors: list[tuple[str, Tensor]] = []
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# merge the experts into a single 3d tensor
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for w_name in ["gate_proj", "up_proj", "down_proj"]:
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datas: list[Tensor] = []
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for xid in range(n_experts):
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ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
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datas.append(self._experts[bid][ename_to_retrieve])
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del self._experts[bid][ename_to_retrieve]
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data_torch = torch.stack(datas, dim=0)
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merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
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new_name = self.map_tensor_name(merged_name)
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tensors.append((new_name, data_torch))
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return tensors
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else:
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return []
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return [(self.map_tensor_name(name), data_torch)]
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def prepare_tensors(self):
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super().prepare_tensors()
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if self._experts is not None:
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# flatten `list[dict[str, Tensor]]` into `list[str]`
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experts = [k for d in self._experts for k in d.keys()]
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if len(experts) > 0:
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raise ValueError(f"Unprocessed experts: {experts}")
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@ModelBase.register("PanguEmbeddedForCausalLM")
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class PanguEmbeddedModel(TextModel):
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model_arch = gguf.MODEL_ARCH.PANGU_EMBED
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@ -449,6 +449,7 @@ class MODEL_ARCH(IntEnum):
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RND1 = auto()
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PANGU_EMBED = auto()
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MISTRAL3 = auto()
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MIMO2 = auto()
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LLAMA_EMBED = auto()
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@ -845,6 +846,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.RND1: "rnd1",
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MODEL_ARCH.PANGU_EMBED: "pangu-embedded",
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MODEL_ARCH.MISTRAL3: "mistral3",
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MODEL_ARCH.MIMO2: "mimo2",
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MODEL_ARCH.LLAMA_EMBED: "llama-embed",
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}
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@ -3198,6 +3200,26 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_DOWN_EXP,
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MODEL_TENSOR.FFN_UP_EXP,
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],
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MODEL_ARCH.MIMO2: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_SINKS,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.FFN_GATE_INP,
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MODEL_TENSOR.FFN_GATE_EXP,
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MODEL_TENSOR.FFN_DOWN_EXP,
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MODEL_TENSOR.FFN_UP_EXP,
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MODEL_TENSOR.FFN_EXP_PROBS_B,
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],
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MODEL_ARCH.LLAMA_EMBED: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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@ -3217,7 +3239,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_GATE_EXP,
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MODEL_TENSOR.FFN_DOWN_EXP,
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MODEL_TENSOR.FFN_UP_EXP,
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]
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],
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# TODO
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}
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@ -320,6 +320,7 @@ class TensorNameMap:
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MODEL_TENSOR.ATTN_SINKS: (
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"model.layers.{bid}.self_attn.sinks", # openai-moe
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"model.layers.{bid}.self_attn.attention_sink_bias", # mimov2
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),
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MODEL_TENSOR.ATTN_GATE: (
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@ -88,6 +88,7 @@ add_library(llama
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models/llama-iswa.cpp
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models/llama.cpp
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models/mamba.cpp
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models/mimo2-iswa.cpp
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models/minicpm3.cpp
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models/minimax-m2.cpp
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models/modern-bert.cpp
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@ -115,6 +115,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_RND1, "rnd1" },
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{ LLM_ARCH_PANGU_EMBED, "pangu-embedded" },
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{ LLM_ARCH_MISTRAL3, "mistral3" },
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{ LLM_ARCH_MIMO2, "mimo2" },
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{ LLM_ARCH_LLAMA_EMBED, "llama-embed" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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@ -2190,6 +2191,27 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
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LLM_TENSOR_VISEXP_FFN_DOWN,
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LLM_TENSOR_VISEXP_FFN_UP,
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};
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case LLM_ARCH_MIMO2:
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return {
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LLM_TENSOR_TOKEN_EMBD,
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LLM_TENSOR_OUTPUT_NORM,
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LLM_TENSOR_OUTPUT,
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LLM_TENSOR_ATTN_NORM,
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LLM_TENSOR_ATTN_Q,
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LLM_TENSOR_ATTN_K,
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LLM_TENSOR_ATTN_V,
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LLM_TENSOR_ATTN_SINKS,
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LLM_TENSOR_ATTN_OUT,
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LLM_TENSOR_FFN_NORM,
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LLM_TENSOR_FFN_GATE,
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LLM_TENSOR_FFN_DOWN,
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LLM_TENSOR_FFN_UP,
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LLM_TENSOR_FFN_GATE_INP,
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LLM_TENSOR_FFN_GATE_EXPS,
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LLM_TENSOR_FFN_DOWN_EXPS,
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LLM_TENSOR_FFN_UP_EXPS,
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LLM_TENSOR_FFN_EXP_PROBS_B,
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};
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case LLM_ARCH_GPTJ:
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case LLM_ARCH_UNKNOWN:
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return {
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@ -119,6 +119,7 @@ enum llm_arch {
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LLM_ARCH_RND1,
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LLM_ARCH_PANGU_EMBED,
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LLM_ARCH_MISTRAL3,
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LLM_ARCH_MIMO2,
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LLM_ARCH_LLAMA_EMBED,
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LLM_ARCH_UNKNOWN,
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};
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@ -123,10 +123,11 @@ struct llama_hparams {
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llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
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// the size of the sliding window (0 - no SWA)
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uint32_t n_swa = 0;
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// if swa_layers[il] == true, then layer il is SWA
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// if swa_layers[il] == false, then layer il is dense (i.e. non-SWA)
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// if swa_layers[il] == 1, then layer il is SWA
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// if swa_layers[il] == 0, then layer il is dense (i.e. non-SWA)
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// by default, all layers are dense
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std::array<bool, LLAMA_MAX_LAYERS> swa_layers;
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// note: using uint32_t type for compatibility reason
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std::array<uint32_t, LLAMA_MAX_LAYERS> swa_layers;
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// for State Space Models
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uint32_t ssm_d_conv = 0;
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@ -130,6 +130,7 @@ const char * llm_type_name(llm_type type) {
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case LLM_TYPE_230B_A10B: return "230B.A10B";
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case LLM_TYPE_235B_A22B: return "235B.A22B";
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case LLM_TYPE_300B_A47B: return "300B.A47B";
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case LLM_TYPE_310B_A15B: return "310B.A15B";
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case LLM_TYPE_355B_A32B: return "355B.A32B";
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case LLM_TYPE_E2B: return "E2B";
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case LLM_TYPE_E4B: return "E4B";
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@ -2339,6 +2340,22 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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case LLM_ARCH_MIMO2:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
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ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
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ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
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ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa);
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ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
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switch (hparams.n_layer) {
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case 48: type = LLM_TYPE_310B_A15B; break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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default: throw std::runtime_error("unsupported model architecture");
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}
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@ -6648,6 +6665,44 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { hparams.n_ff_shexp, n_embd }, 0);
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}
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} break;
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case LLM_ARCH_MIMO2:
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{
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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// output
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i);
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uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
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uint32_t n_head = hparams.n_head(i);
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layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
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layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0);
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layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_v * n_head, n_embd }, 0);
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
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layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, TENSOR_NOT_REQUIRED);
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
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// non-MoE branch
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, TENSOR_NOT_REQUIRED);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
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// MoE branch
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int64_t n_ff_exp = hparams.n_ff_exp;
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layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
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layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED);
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layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED);
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layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
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}
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} break;
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default:
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throw std::runtime_error("unknown architecture");
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}
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@ -7710,6 +7765,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
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{
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llm = std::make_unique<llm_build_mistral3>(*this, params);
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} break;
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case LLM_ARCH_MIMO2:
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{
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llm = std::make_unique<llm_build_mimo2_iswa>(*this, params);
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} break;
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default:
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GGML_ABORT("fatal error");
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}
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@ -7940,6 +7999,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
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case LLM_ARCH_PANGU_EMBED:
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case LLM_ARCH_AFMOE:
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case LLM_ARCH_QWEN3NEXT:
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case LLM_ARCH_MIMO2:
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return LLAMA_ROPE_TYPE_NEOX;
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case LLM_ARCH_QWEN2VL:
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@ -123,6 +123,7 @@ enum llm_type {
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LLM_TYPE_230B_A10B, // Minimax M2
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LLM_TYPE_235B_A22B,
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LLM_TYPE_300B_A47B, // Ernie MoE big
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LLM_TYPE_310B_A15B, // /MiMo-V2-Flash
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LLM_TYPE_355B_A32B, // GLM-4.5
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LLM_TYPE_E2B,
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LLM_TYPE_E4B,
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@ -0,0 +1,123 @@
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#include "models.h"
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llm_build_mimo2_iswa::llm_build_mimo2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
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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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ggml_tensor * inp_pos = build_inp_pos();
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auto * inp_attn = build_attn_inp_kv_iswa();
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||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
ggml_tensor * inpSA = inpL;
|
||||
|
||||
uint32_t n_head_l = hparams.n_head(il);
|
||||
uint32_t n_head_kv_l = 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);
|
||||
|
||||
cur = inpL;
|
||||
|
||||
// self_attention
|
||||
{
|
||||
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// compute Q and K and RoPE them
|
||||
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l, n_tokens);
|
||||
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens);
|
||||
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens);
|
||||
|
||||
Qcur = ggml_rope_ext(
|
||||
ctx0, Qcur, inp_pos, nullptr,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
|
||||
Kcur = ggml_rope_ext(
|
||||
ctx0, Kcur, inp_pos, nullptr,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
ggml_tensor * sinks = model.layers[il].attn_sinks;
|
||||
|
||||
cur = build_attn(inp_attn,
|
||||
model.layers[il].wo, NULL,
|
||||
Qcur, Kcur, Vcur, nullptr, sinks, nullptr, 1.0f/sqrtf(float(n_embd_head_k)), 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);
|
||||
}
|
||||
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
cur = build_norm(ffn_inp,
|
||||
model.layers[il].ffn_norm, NULL,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
// feed-forward network
|
||||
if (model.layers[il].ffn_gate_inp == nullptr) {
|
||||
// dense branch
|
||||
cur = build_ffn(cur,
|
||||
model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
|
||||
model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
|
||||
model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
|
||||
NULL,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
} else {
|
||||
// MoE branch
|
||||
cur = 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, true, false,
|
||||
0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID, il);
|
||||
cb(cur, "ffn_moe_out", il);
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
|
||||
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);
|
||||
}
|
||||
|
|
@ -316,6 +316,10 @@ struct llm_build_mamba : public llm_graph_context_mamba {
|
|||
llm_build_mamba(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct llm_build_mimo2_iswa : public llm_graph_context {
|
||||
llm_build_mimo2_iswa(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct llm_build_minicpm3 : public llm_graph_context {
|
||||
llm_build_minicpm3(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
|
|
|||
Loading…
Reference in New Issue