llama : support LiquidAI LFM2-MoE hybrid model (#16464)
* llama : support LiquidAI LFM2-MoE hybrid model Add support for [LiquidAI/LFM2-8B-A1B](https://huggingface.co/LiquidAI/LFM2-8B-A1B) model. For more information about models, please read [the blog post](https://www.liquid.ai/company/news). [HF PR](https://github.com/huggingface/transformers/pull/41401) [GGUFs](https://huggingface.co/LiquidAI/LFM2-8B-A1B-GGUF) * Do not use defaultdict * Address PR feedback
This commit is contained in:
Tarek Dakhran
GitHub
parent
df1b612e29
commit
aeaf8a36f0
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@ -8836,6 +8836,75 @@ class LFM2Model(TextModel):
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return [(self.map_tensor_name(name), data_torch)]
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@ModelBase.register("Lfm2MoeForCausalLM")
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class LFM2MoeModel(TextModel):
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model_arch = gguf.MODEL_ARCH.LFM2MOE
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def set_gguf_parameters(self):
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# set num_key_value_heads only for attention layers
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self.hparams["num_key_value_heads"] = [
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self.hparams["num_key_value_heads"] if layer_type == "full_attention" else 0
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for layer_type in self.hparams["layer_types"]
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]
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super().set_gguf_parameters()
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self.gguf_writer.add_expert_count(self.hparams["num_experts"])
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self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
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self.gguf_writer.add_leading_dense_block_count(self.hparams["num_dense_layers"])
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self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
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self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
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self.gguf_writer.add_shortconv_l_cache(self.hparams["conv_L_cache"])
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# cache for experts weights for merging
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_experts_cache: dict[int, dict[str, Tensor]] = {}
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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# conv op requires 2d tensor
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if 'conv.conv' in name:
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data_torch = data_torch.squeeze(1)
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if name.endswith(".expert_bias"):
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name = name.replace(".expert_bias", ".expert_bias.bias")
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# merge expert weights
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if 'experts' in name:
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n_experts = self.hparams["num_experts"]
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assert bid is not None
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expert_cache = self._experts_cache.setdefault(bid, {})
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expert_cache[name] = data_torch
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expert_weights = ["w1", "w2", "w3"]
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# not enough expert weights to merge
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if len(expert_cache) < n_experts * len(expert_weights):
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return []
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tensors: list[tuple[str, Tensor]] = []
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for w_name in expert_weights:
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datas: list[Tensor] = []
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for xid in range(n_experts):
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ename = f"model.layers.{bid}.feed_forward.experts.{xid}.{w_name}.weight"
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datas.append(expert_cache[ename])
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del expert_cache[ename]
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data_torch = torch.stack(datas, dim=0)
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merged_name = f"layers.{bid}.feed_forward.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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del self._experts_cache[bid]
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return tensors
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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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assert not self._experts_cache
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@ModelBase.register("Lfm2VlForConditionalGeneration")
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class LFM2VLModel(MmprojModel):
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def __init__(self, *args, **kwargs):
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@ -407,6 +407,7 @@ class MODEL_ARCH(IntEnum):
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SMOLLM3 = auto()
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GPT_OSS = auto()
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LFM2 = auto()
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LFM2MOE = auto()
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DREAM = auto()
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SMALLTHINKER = auto()
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LLADA = auto()
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@ -749,6 +750,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.SMOLLM3: "smollm3",
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MODEL_ARCH.GPT_OSS: "gpt-oss",
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MODEL_ARCH.LFM2: "lfm2",
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MODEL_ARCH.LFM2MOE: "lfm2moe",
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MODEL_ARCH.DREAM: "dream",
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MODEL_ARCH.SMALLTHINKER: "smallthinker",
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MODEL_ARCH.LLADA: "llada",
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@ -2698,6 +2700,29 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.OUTPUT,
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],
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MODEL_ARCH.LFM2MOE: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.TOKEN_EMBD_NORM,
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MODEL_TENSOR.SHORTCONV_CONV,
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MODEL_TENSOR.SHORTCONV_INPROJ,
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MODEL_TENSOR.SHORTCONV_OUTPROJ,
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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_NORM,
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MODEL_TENSOR.ATTN_NORM, # operator_norm
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MODEL_TENSOR.ATTN_Q_NORM,
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MODEL_TENSOR.ATTN_K_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_OUT,
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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.SMALLTHINKER: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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@ -358,6 +358,7 @@ class TensorNameMap:
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"model.layers.{bid}.mlp.router", # openai-moe
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"model.layers.{bid}.mlp.gate.wg", # hunyuan
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"model.layers.{bid}.block_sparse_moe.primary_router", # smallthinker
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"model.layers.{bid}.feed_forward.gate", # lfm2moe
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),
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MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
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@ -367,6 +368,7 @@ class TensorNameMap:
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MODEL_TENSOR.FFN_EXP_PROBS_B: (
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"model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3 dots1
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"model.layers.{bid}.mlp.moe_statics.e_score_correction", # ernie4.5-moe
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"model.layers.{bid}.feed_forward.expert_bias", # lfm2moe
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),
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# Feed-forward up
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@ -93,6 +93,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_SMOLLM3, "smollm3" },
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{ LLM_ARCH_OPENAI_MOE, "gpt-oss" },
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{ LLM_ARCH_LFM2, "lfm2" },
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{ LLM_ARCH_LFM2MOE, "lfm2moe" },
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{ LLM_ARCH_DREAM, "dream" },
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{ LLM_ARCH_SMALLTHINKER, "smallthinker" },
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{ LLM_ARCH_LLADA, "llada" },
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@ -2104,6 +2105,32 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
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{ LLM_TENSOR_OUTPUT, "output" },
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}
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},
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{
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LLM_ARCH_LFM2MOE,
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{
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
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{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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{ LLM_TENSOR_SHORTCONV_CONV, "blk.%d.shortconv.conv" },
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{ LLM_TENSOR_SHORTCONV_INPROJ, "blk.%d.shortconv.in_proj" },
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{ LLM_TENSOR_SHORTCONV_OUTPROJ, "blk.%d.shortconv.out_proj" },
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
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{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
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{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
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{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
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{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
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{ LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" },
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}
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},
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{
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LLM_ARCH_SMALLTHINKER,
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{
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@ -2493,6 +2520,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
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case LLM_ARCH_PLAMO2:
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case LLM_ARCH_GRANITE_HYBRID:
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case LLM_ARCH_LFM2:
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case LLM_ARCH_LFM2MOE:
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case LLM_ARCH_NEMOTRON_H:
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return true;
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default:
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@ -97,6 +97,7 @@ enum llm_arch {
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LLM_ARCH_SMOLLM3,
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LLM_ARCH_OPENAI_MOE,
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LLM_ARCH_LFM2,
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LLM_ARCH_LFM2MOE,
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LLM_ARCH_DREAM,
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LLM_ARCH_SMALLTHINKER,
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LLM_ARCH_LLADA,
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@ -114,6 +114,7 @@ const char * llm_type_name(llm_type type) {
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case LLM_TYPE_17B_16E: return "17Bx16E (Scout)";
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case LLM_TYPE_17B_128E: return "17Bx128E (Maverick)";
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case LLM_TYPE_A13B: return "A13B";
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case LLM_TYPE_8B_A1B: return "8B.A1B";
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case LLM_TYPE_21B_A3B: return "21B.A3B";
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case LLM_TYPE_30B_A3B: return "30B.A3B";
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case LLM_TYPE_106B_A12B: return "106B.A12B";
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@ -1995,6 +1996,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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for (uint32_t il = 0; il < hparams.n_layer; ++il) {
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hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
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}
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hparams.n_layer_dense_lead = hparams.n_layer;
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switch (hparams.n_ff()) {
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case 4608: type = LLM_TYPE_350M; break;
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case 6912: type = LLM_TYPE_700M; break;
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@ -2003,6 +2005,20 @@ 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_LFM2MOE:
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{
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ml.get_key(LLM_KV_SHORTCONV_L_CACHE, hparams.n_shortconv_l_cache);
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
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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_EXPERT_GATING_FUNC, hparams.expert_gating_func);
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for (uint32_t il = 0; il < hparams.n_layer; ++il) {
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hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
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}
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type = LLM_TYPE_8B_A1B;
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} break;
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case LLM_ARCH_SMALLTHINKER:
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{
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const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
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@ -5814,6 +5830,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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}
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} break;
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case LLM_ARCH_LFM2:
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case LLM_ARCH_LFM2MOE:
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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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tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
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@ -5825,11 +5842,23 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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// ffn is same for transformer and conv layers
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const bool is_moe_layer = i >= static_cast<int>(hparams.n_layer_dense_lead);
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// ffn/moe is same for transformer and conv layers
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
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if (is_moe_layer) {
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GGML_ASSERT(n_expert && n_expert_used);
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layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
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layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, hparams.n_ff_exp, n_expert}, 0);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {hparams.n_ff_exp, n_embd, n_expert}, 0);
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layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, hparams.n_ff_exp, n_expert}, 0);
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layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
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} else { // dense
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
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}
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// for operator_norm
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
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@ -6310,7 +6339,7 @@ void llama_model::print_info() const {
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LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm);
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}
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if (arch == LLM_ARCH_SMALLTHINKER) {
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if (arch == LLM_ARCH_SMALLTHINKER || arch == LLM_ARCH_LFM2MOE) {
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LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
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LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
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}
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@ -18602,6 +18631,8 @@ struct llm_build_lfm2 : public llm_graph_context {
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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for (int il = 0; il < n_layer; ++il) {
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const bool is_moe_layer = il >= static_cast<int>(hparams.n_layer_dense_lead);
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auto * prev_cur = cur;
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cur = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "model.layers.{}.operator_norm", il);
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@ -18616,7 +18647,16 @@ struct llm_build_lfm2 : public llm_graph_context {
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}
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cur = ggml_add(ctx0, prev_cur, cur);
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cur = ggml_add(ctx0, cur, build_feed_forward(cur, il));
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auto * ffn_norm_out = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
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cb(ffn_norm_out, "model.layers.{}.ffn_norm", il);
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ggml_tensor * ffn_out = is_moe_layer ?
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build_moe_feed_forward(ffn_norm_out, il) :
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build_dense_feed_forward(ffn_norm_out, il);
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cb(ffn_norm_out, "model.layers.{}.ffn_out", il);
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cur = ggml_add(ctx0, cur, ffn_out);
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}
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cur = build_norm(cur, model.tok_norm, NULL, LLM_NORM_RMS, -1);
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@ -18631,23 +18671,32 @@ struct llm_build_lfm2 : public llm_graph_context {
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ggml_build_forward_expand(gf, cur);
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}
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ggml_tensor * build_feed_forward(ggml_tensor * cur,
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ggml_tensor * build_moe_feed_forward(ggml_tensor * cur,
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int il) const {
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cur = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "model.layers.{}.ffn_norm", il);
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return build_moe_ffn(cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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model.layers[il].ffn_gate_exps,
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model.layers[il].ffn_down_exps,
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model.layers[il].ffn_exp_probs_b,
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n_expert, n_expert_used,
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LLM_FFN_SILU, true,
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false, 0.0,
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static_cast<llama_expert_gating_func_type>(hparams.expert_gating_func),
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il);
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}
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ggml_tensor * build_dense_feed_forward(ggml_tensor * cur,
|
||||
int il) const {
|
||||
GGML_ASSERT(!model.layers[il].ffn_up_b);
|
||||
GGML_ASSERT(!model.layers[il].ffn_gate_b);
|
||||
GGML_ASSERT(!model.layers[il].ffn_down_b);
|
||||
cur = build_ffn(cur,
|
||||
return 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, "model.layers.{}.feed_forward.w2", il);
|
||||
|
||||
return cur;
|
||||
}
|
||||
|
||||
ggml_tensor * build_attn_block(ggml_tensor * cur,
|
||||
|
|
@ -19817,6 +19866,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
|
|||
llm = std::make_unique<llm_build_falcon_h1>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_LFM2:
|
||||
case LLM_ARCH_LFM2MOE:
|
||||
{
|
||||
llm = std::make_unique<llm_build_lfm2>(*this, params);
|
||||
} break;
|
||||
|
|
@ -20039,6 +20089,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
|||
case LLM_ARCH_OPENAI_MOE:
|
||||
case LLM_ARCH_HUNYUAN_DENSE:
|
||||
case LLM_ARCH_LFM2:
|
||||
case LLM_ARCH_LFM2MOE:
|
||||
case LLM_ARCH_SMALLTHINKER:
|
||||
case LLM_ARCH_GLM4_MOE:
|
||||
case LLM_ARCH_SEED_OSS:
|
||||
|
|
|
|||
|
|
@ -107,6 +107,7 @@ enum llm_type {
|
|||
LLM_TYPE_17B_16E, // llama4 Scout
|
||||
LLM_TYPE_17B_128E, // llama4 Maverick
|
||||
LLM_TYPE_A13B,
|
||||
LLM_TYPE_8B_A1B, // lfm2moe
|
||||
LLM_TYPE_21B_A3B, // Ernie MoE small
|
||||
LLM_TYPE_30B_A3B,
|
||||
LLM_TYPE_106B_A12B, // GLM-4.5-Air
|
||||
|
|
|
|||
Loading…
Reference in New Issue