model : Initial support for DeepseekV32ForCausalLM (for now with dense attention). Needs manual change of add_bos_token to true in tokenizer_config.json before conversion.
This commit is contained in:
Stanisław Szymczyk
parent
b5fe4559ae
commit
a337ebd7bd
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@ -8153,6 +8153,145 @@ class DeepseekV2Model(TextModel):
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raise ValueError(f"Unprocessed experts: {experts}")
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@ModelBase.register(
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"DeepseekV32ForCausalLM",
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)
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class DeepseekV32Model(TextModel):
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model_arch = gguf.MODEL_ARCH.DEEPSEEK32
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# TODO @ngxson : remove this when we support MTP for deepseek models
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skip_mtp = True
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
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self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
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def set_vocab(self):
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self._set_vocab_gpt2()
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def set_gguf_parameters(self):
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# note: deepseek32 using MLA converts into MQA (ie: GQA with 1 group)
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self.hparams["num_key_value_heads"] = 1
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super().set_gguf_parameters()
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hparams = self.hparams
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# first_k_dense_replace: number of leading layers using dense FFN instead of MoE
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first_k_dense_replace = hparams.get("first_k_dense_replace")
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self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
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self.gguf_writer.add_vocab_size(hparams["vocab_size"])
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self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
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self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])
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# note: deepseek32 using MLA converts into MQA with larger heads, then decompresses to MHA
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self.gguf_writer.add_key_length(hparams["kv_lora_rank"] + hparams["qk_rope_head_dim"])
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self.gguf_writer.add_value_length(hparams["kv_lora_rank"])
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self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
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self.gguf_writer.add_value_length_mla(hparams["v_head_dim"])
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# MoE parameters (required by C++ code for DEEPSEEK32 arch)
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self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
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self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
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self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
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self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
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self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
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self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
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if (rope_mscale_all := self.rope_parameters.get("mscale_all_dim")) is not None:
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# [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
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# note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul
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# ref https://github.com/ggml-org/llama.cpp/pull/17945
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self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_mscale_all)
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# NextN/MTP prediction layers
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if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
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self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
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# DSA indexer parameters
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self.gguf_writer.add_indexer_head_count(self.hparams["index_n_heads"])
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self.gguf_writer.add_indexer_key_length(self.hparams["index_head_dim"])
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self.gguf_writer.add_indexer_top_k(self.hparams["index_topk"])
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_experts: list[dict[str, Tensor]] | None = None
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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if name.startswith("language_model."):
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name = name.replace("language_model.", "")
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# rename e_score_correction_bias tensors
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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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# skip Multi-Token Prediction (MTP) layers
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if self.skip_mtp:
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block_count = self.hparams["num_hidden_layers"]
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match = re.match(r"model.layers.(\d+)", name)
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if match and int(match.group(1)) >= block_count:
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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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# merge the experts into a single 3d tensor
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for w_name in ["down_proj", "gate_proj", "up_proj"]:
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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}.mlp.experts.{xid}.{w_name}.weight"
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datas.append(self._experts[bid][ename])
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del self._experts[bid][ename]
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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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yield from super().modify_tensors(data_torch, merged_name, bid)
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return
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else:
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return
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# note: MLA with the absorption optimization, needs these two split and k_b_proj transposed
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if name.endswith("kv_b_proj.weight"):
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name_kb = name.replace("kv_b_proj", "k_b_proj")
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name_vb = name.replace("kv_b_proj", "v_b_proj")
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n_head_kv = self.hparams["num_key_value_heads"]
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v_head_dim = self.hparams["v_head_dim"]
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qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
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assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim)
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kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
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k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
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k_b = k_b.transpose(1, 2)
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yield from super().modify_tensors(k_b, name_kb, bid)
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yield from super().modify_tensors(v_b, name_vb, bid)
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return
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yield from super().modify_tensors(data_torch, name, bid)
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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("MiniMaxM2ForCausalLM")
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class MiniMaxM2Model(TextModel):
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model_arch = gguf.MODEL_ARCH.MINIMAXM2
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@ -431,6 +431,7 @@ class MODEL_ARCH(IntEnum):
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ARCTIC = auto()
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DEEPSEEK = auto()
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DEEPSEEK2 = auto()
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DEEPSEEK32 = auto()
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CHATGLM = auto()
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GLM4 = auto()
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GLM4_MOE = auto()
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@ -874,6 +875,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.ARCTIC: "arctic",
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MODEL_ARCH.DEEPSEEK: "deepseek",
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MODEL_ARCH.DEEPSEEK2: "deepseek2",
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MODEL_ARCH.DEEPSEEK32: "deepseek32",
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MODEL_ARCH.CHATGLM: "chatglm",
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MODEL_ARCH.GLM4: "glm4",
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MODEL_ARCH.GLM4_MOE: "glm4moe",
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@ -2623,6 +2625,47 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_UP_SHEXP,
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MODEL_TENSOR.FFN_EXP_PROBS_B,
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],
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MODEL_ARCH.DEEPSEEK32: [
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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.ROPE_FREQS,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_Q_A,
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MODEL_TENSOR.ATTN_Q_B,
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MODEL_TENSOR.ATTN_KV_A_MQA,
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MODEL_TENSOR.ATTN_KV_B,
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MODEL_TENSOR.ATTN_K_B,
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MODEL_TENSOR.ATTN_V_B,
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MODEL_TENSOR.ATTN_Q_A_NORM,
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MODEL_TENSOR.ATTN_KV_A_NORM,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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MODEL_TENSOR.FFN_GATE_INP,
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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_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_GATE_SHEXP,
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MODEL_TENSOR.FFN_DOWN_SHEXP,
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MODEL_TENSOR.FFN_UP_SHEXP,
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MODEL_TENSOR.FFN_EXP_PROBS_B,
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MODEL_TENSOR.INDEXER_K_NORM,
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MODEL_TENSOR.INDEXER_PROJ,
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MODEL_TENSOR.INDEXER_ATTN_K,
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MODEL_TENSOR.INDEXER_ATTN_Q_B,
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# NextN/MTP tensors - preserved but unused
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MODEL_TENSOR.NEXTN_EH_PROJ,
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MODEL_TENSOR.NEXTN_EMBED_TOKENS,
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MODEL_TENSOR.NEXTN_ENORM,
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MODEL_TENSOR.NEXTN_HNORM,
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MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
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MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
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],
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MODEL_ARCH.ERNIE4_5_MOE: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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@ -3698,6 +3741,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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MODEL_ARCH.DEEPSEEK32: [
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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MODEL_ARCH.CHATGLM: [
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MODEL_TENSOR.ROPE_FREQS,
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],
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@ -57,6 +57,7 @@ add_library(llama
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models/deci.cpp
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models/deepseek.cpp
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models/deepseek2.cpp
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models/deepseek32.cpp
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models/delta-net-base.cpp
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models/dots1.cpp
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models/dream.cpp
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@ -73,6 +73,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_ARCTIC, "arctic" },
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{ LLM_ARCH_DEEPSEEK, "deepseek" },
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{ LLM_ARCH_DEEPSEEK2, "deepseek2" },
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{ LLM_ARCH_DEEPSEEK32, "deepseek32" },
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{ LLM_ARCH_CHATGLM, "chatglm" },
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{ LLM_ARCH_GLM4, "glm4" },
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{ LLM_ARCH_GLM4_MOE, "glm4moe" },
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@ -1616,6 +1617,44 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
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LLM_TENSOR_FFN_UP_SHEXP,
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LLM_TENSOR_FFN_EXP_PROBS_B,
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};
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case LLM_ARCH_DEEPSEEK32:
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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_A_NORM,
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LLM_TENSOR_ATTN_KV_A_NORM,
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LLM_TENSOR_ATTN_Q_A,
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LLM_TENSOR_ATTN_Q_B,
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LLM_TENSOR_ATTN_KV_A_MQA,
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LLM_TENSOR_ATTN_KV_B,
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LLM_TENSOR_ATTN_K_B,
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LLM_TENSOR_ATTN_V_B,
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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_UP,
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LLM_TENSOR_FFN_DOWN,
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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_GATE_SHEXP,
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LLM_TENSOR_FFN_DOWN_SHEXP,
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LLM_TENSOR_FFN_UP_SHEXP,
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LLM_TENSOR_FFN_EXP_PROBS_B,
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LLM_TENSOR_INDEXER_K_NORM,
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LLM_TENSOR_INDEXER_PROJ,
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LLM_TENSOR_INDEXER_ATTN_K,
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LLM_TENSOR_INDEXER_ATTN_Q_B,
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LLM_TENSOR_NEXTN_EH_PROJ,
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LLM_TENSOR_NEXTN_EMBED_TOKENS,
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LLM_TENSOR_NEXTN_ENORM,
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LLM_TENSOR_NEXTN_HNORM,
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LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,
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LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
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};
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case LLM_ARCH_PLM:
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return {
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LLM_TENSOR_TOKEN_EMBD,
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@ -77,6 +77,7 @@ enum llm_arch {
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LLM_ARCH_ARCTIC,
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LLM_ARCH_DEEPSEEK,
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LLM_ARCH_DEEPSEEK2,
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LLM_ARCH_DEEPSEEK32,
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LLM_ARCH_CHATGLM,
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LLM_ARCH_GLM4,
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LLM_ARCH_GLM4_MOE,
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@ -143,6 +143,7 @@ const char * llm_type_name(llm_type type) {
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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_397B_A17B: return "397B.A17B";
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case LLM_TYPE_685B_A37B: return "685B.A37B";
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case LLM_TYPE_744B_A40B: return "744B.A40B";
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case LLM_TYPE_E2B: return "E2B";
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case LLM_TYPE_E4B: return "E4B";
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@ -1634,6 +1635,55 @@ 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_DEEPSEEK32:
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{
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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_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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hparams.f_norm_eps = 1e-6; // eps for layer norm
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ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
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// MoE parameters
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ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert);
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ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used);
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ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
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ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
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// deepseek MLA parameters
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ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
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ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
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ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl, false);
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ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl, false);
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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_SHARED_COUNT, hparams.n_expert_shared);
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// DSA parameters
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ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
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ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
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ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k);
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// Expert gating function
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ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
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if (ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, 0.0f)) {
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// [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
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// cancel the factor from the convert script
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hparams.rope_yarn_log_mul /= 0.1f;
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}
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// NextN/MTP parameters
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ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
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|
||||
// 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 61: type = LLM_TYPE_685B_A37B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
|
||||
case LLM_ARCH_PLM:
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
|
|
@ -4964,6 +5014,108 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
|
|||
}
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_DEEPSEEK32:
|
||||
{
|
||||
const bool is_mla = hparams.is_mla();
|
||||
if (!is_mla) {
|
||||
throw std::runtime_error("DEEPSEEK32 architecture requires MLA");
|
||||
}
|
||||
|
||||
// note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
|
||||
const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
|
||||
const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
|
||||
|
||||
const int64_t n_embd_head_qk_rope = hparams.n_rot();
|
||||
const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
|
||||
|
||||
const int64_t q_lora_rank = hparams.n_lora_q;
|
||||
const int64_t kv_lora_rank = hparams.n_lora_kv;
|
||||
|
||||
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);
|
||||
// try to load output.weight, if not found, use token_embd (tied embeddings)
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
||||
if (!output) {
|
||||
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
||||
}
|
||||
|
||||
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
|
||||
// TODO @ngxson : TENSOR_NOT_REQUIRED was a hack, need to remove it later
|
||||
flags |= TENSOR_SKIP | TENSOR_NOT_REQUIRED;
|
||||
}
|
||||
|
||||
auto & layer = layers[i];
|
||||
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
|
||||
layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags);
|
||||
layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags);
|
||||
|
||||
layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags);
|
||||
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, flags);
|
||||
|
||||
layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, flags);
|
||||
|
||||
// note: only old legacy GGUF files will have the unsplit wkv_b tensor in
|
||||
layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, flags);
|
||||
layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, flags);
|
||||
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, flags);
|
||||
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
|
||||
|
||||
// DSA indexer
|
||||
layer.indexer_k_norm = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "weight", i), {hparams.indexer_head_size}, flags);
|
||||
layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "bias", i), {hparams.indexer_head_size}, flags);
|
||||
layer.indexer_proj = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ, "weight", i), {n_embd, hparams.indexer_n_head}, flags);
|
||||
layer.indexer_attn_k = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K, "weight", i), {n_embd, hparams.indexer_head_size}, flags);
|
||||
layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags);
|
||||
if (i < (int) hparams.n_layer_dense_lead) {
|
||||
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);
|
||||
} else {
|
||||
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);
|
||||
|
||||
if (n_expert == 0) {
|
||||
throw std::runtime_error("n_expert must be > 0");
|
||||
}
|
||||
if (n_expert_used == 0) {
|
||||
throw std::runtime_error("n_expert_used must be > 0");
|
||||
}
|
||||
|
||||
// MoE branch
|
||||
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);
|
||||
|
||||
// Shared expert branch
|
||||
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
|
||||
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, flags);
|
||||
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 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.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);
|
||||
|
||||
// Optional tensors
|
||||
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
|
||||
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
|
||||
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED);
|
||||
}
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_PLM:
|
||||
{
|
||||
const int64_t n_embd_head_qk_rope = hparams.n_rot();
|
||||
|
|
@ -7772,7 +7924,7 @@ void llama_model::print_info() const {
|
|||
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
|
||||
}
|
||||
|
||||
if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_GLM_DSA) {
|
||||
if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_DEEPSEEK32) {
|
||||
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
|
||||
LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q);
|
||||
LLAMA_LOG_INFO("%s: n_lora_kv = %d\n", __func__, hparams.n_lora_kv);
|
||||
|
|
@ -8353,6 +8505,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
|
|||
{
|
||||
llm = std::make_unique<llm_build_deepseek2>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_DEEPSEEK32:
|
||||
{
|
||||
llm = std::make_unique<llm_build_deepseek32>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_CHATGLM:
|
||||
{
|
||||
llm = std::make_unique<llm_build_chatglm>(*this, params);
|
||||
|
|
@ -8748,6 +8904,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
|||
case LLM_ARCH_ARCTIC:
|
||||
case LLM_ARCH_DEEPSEEK:
|
||||
case LLM_ARCH_DEEPSEEK2:
|
||||
case LLM_ARCH_DEEPSEEK32:
|
||||
case LLM_ARCH_PLM:
|
||||
case LLM_ARCH_CHATGLM:
|
||||
case LLM_ARCH_GRANITE:
|
||||
|
|
|
|||
|
|
@ -134,6 +134,7 @@ enum llm_type {
|
|||
LLM_TYPE_310B_A15B, // /MiMo-V2-Flash
|
||||
LLM_TYPE_355B_A32B, // GLM-4.5
|
||||
LLM_TYPE_397B_A17B, // Qwen3.5
|
||||
LLM_TYPE_685B_A37B, // DeepSeek V3.2
|
||||
LLM_TYPE_744B_A40B, // GLM-5
|
||||
LLM_TYPE_E2B,
|
||||
LLM_TYPE_E4B,
|
||||
|
|
|
|||
|
|
@ -0,0 +1,240 @@
|
|||
#include "models.h"
|
||||
|
||||
llm_build_deepseek32::llm_build_deepseek32(const llama_model & model, const llm_graph_params & params) :
|
||||
llm_graph_context(params) {
|
||||
const bool is_mla = hparams.is_mla();
|
||||
|
||||
// note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
|
||||
const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();
|
||||
const int64_t n_embd_head_v = hparams.n_embd_head_v_mla();
|
||||
|
||||
const int64_t n_embd_head_qk_rope = hparams.n_rot();
|
||||
const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
|
||||
|
||||
const uint32_t kv_lora_rank = hparams.n_lora_kv;
|
||||
|
||||
// We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
|
||||
// See https://github.com/ggml-org/llama.cpp/discussions/7416 for detailed explanation.
|
||||
// And also: https://github.com/ggml-org/llama.cpp/pull/17945 [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
|
||||
|
||||
// first cancel the adjustment from llama_hparams::yarn_attn_factor_adjust to get the original attn_factor
|
||||
GGML_ASSERT(ext_factor >= 0.0f);
|
||||
const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale));
|
||||
|
||||
// use the original attn_factor to pre-scale the kq_scale
|
||||
const float mscale = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
|
||||
const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k));
|
||||
|
||||
ggml_tensor * cur;
|
||||
ggml_tensor * inpL;
|
||||
|
||||
// {n_embd, n_tokens}
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
auto * inp_attn_kv = !is_mla ? build_attn_inp_kv() : nullptr;
|
||||
auto * inp_attn_k = is_mla ? build_attn_inp_k() : nullptr;
|
||||
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
int effective_n_layers = hparams.n_layer - hparams.nextn_predict_layers;
|
||||
for (int il = 0; il < effective_n_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
|
||||
{
|
||||
ggml_tensor * q = NULL;
|
||||
|
||||
const bool is_lite = model.layers[il].wq;
|
||||
|
||||
q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
|
||||
cb(q, "q", il);
|
||||
|
||||
q = build_norm(q, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(q, "q", il);
|
||||
|
||||
q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
|
||||
cb(q, "q", il);
|
||||
|
||||
// split into {n_embd_head_qk_nope, n_head, n_tokens}
|
||||
ggml_tensor * q_nope =
|
||||
ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
|
||||
ggml_row_size(q->type, n_embd_head_k) * n_head, 0);
|
||||
cb(q_nope, "q_nope", il);
|
||||
|
||||
// and {n_embd_head_qk_rope, n_head, n_tokens}
|
||||
ggml_tensor * q_pe = ggml_view_3d(
|
||||
ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
|
||||
ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope));
|
||||
cb(q_pe, "q_pe", il);
|
||||
|
||||
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
|
||||
cb(kv_cmpr_pe, "kv_cmpr_pe", il);
|
||||
|
||||
// split into {kv_lora_rank, n_tokens}
|
||||
ggml_tensor * kv_cmpr =
|
||||
ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
|
||||
cb(kv_cmpr, "kv_cmpr", il);
|
||||
|
||||
// and {n_embd_head_qk_rope, 1, n_tokens}
|
||||
ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
|
||||
cb(k_pe, "k_pe", il);
|
||||
|
||||
q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(q_pe, "q_pe", il);
|
||||
|
||||
k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(k_pe, "k_pe", il);
|
||||
|
||||
kv_cmpr = build_norm(kv_cmpr, model.layers[il].attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(kv_cmpr, "kv_cmpr", il);
|
||||
|
||||
if (is_mla) {
|
||||
// {n_embd_head_qk_nope, n_tokens, n_head}
|
||||
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
|
||||
cb(q_nope, "q_nope_perm", il);
|
||||
|
||||
// {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}
|
||||
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope);
|
||||
cb(q_nope_absorbed, "q_nope_absorbed", il);
|
||||
|
||||
// {kv_lora_rank, n_head, n_tokens}
|
||||
q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
|
||||
cb(q_nope_absorbed, "q_nope_absorbed_perm", il);
|
||||
|
||||
// {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}
|
||||
// note: rope must go first for in-place context shifting in build_rope_shift()
|
||||
ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
|
||||
cb(kv_cmpr, "kv_cmpr_reshape", il);
|
||||
|
||||
// {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
|
||||
ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
// {kv_lora_rank, 1, n_tokens}
|
||||
ggml_tensor * Vcur = kv_cmpr;
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
// note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group)
|
||||
cur = build_attn(inp_attn_k,
|
||||
model.layers[il].wo, NULL,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il);
|
||||
} else {
|
||||
ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_cmpr);
|
||||
cb(kv, "kv", il);
|
||||
|
||||
// split into {n_embd_head_qk_nope, n_head, n_tokens}
|
||||
ggml_tensor * k_nope =
|
||||
ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
|
||||
ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v),
|
||||
ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v) * n_head, 0);
|
||||
cb(k_nope, "k_nope_view", il);
|
||||
|
||||
// and {n_embd_head_v, n_head, n_tokens}
|
||||
ggml_tensor * Vcur = ggml_view_3d(ctx0, kv, n_embd_head_v, n_head, n_tokens,
|
||||
ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v),
|
||||
ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v) * n_head,
|
||||
ggml_row_size(kv->type, n_embd_head_qk_nope));
|
||||
cb(Vcur, "Vcur_view", il);
|
||||
|
||||
Vcur = ggml_cont(ctx0, Vcur);
|
||||
cb(Vcur, "Vcur_cont", il);
|
||||
|
||||
ggml_tensor * Qcur = ggml_concat(ctx0, q_nope, q_pe, 0);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
ggml_tensor * Kcur = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
// note: MLA without the absorption optimization converts into MHA (ie: GQA with full n_head groups)
|
||||
cur = build_attn(inp_attn_kv,
|
||||
model.layers[il].wo, NULL,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
|
||||
}
|
||||
}
|
||||
if (il == effective_n_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 * 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);
|
||||
|
||||
if ((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 {
|
||||
// MoE branch
|
||||
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,
|
||||
hparams.expert_weights_scale,
|
||||
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
il,
|
||||
nullptr,
|
||||
model.layers[il].ffn_gate_up_exps);
|
||||
cb(moe_out, "ffn_moe_out", il);
|
||||
|
||||
// FFN shared expert
|
||||
{
|
||||
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, 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 = ggml_mul_mat(ctx0, model.output, cur);
|
||||
|
||||
cb(cur, "result_output", -1);
|
||||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
|
@ -166,12 +166,16 @@ struct llm_build_deci : public llm_graph_context {
|
|||
llm_build_deci(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct llm_build_deepseek : public llm_graph_context {
|
||||
llm_build_deepseek(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct llm_build_deepseek2 : public llm_graph_context {
|
||||
llm_build_deepseek2(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct llm_build_deepseek : public llm_graph_context {
|
||||
llm_build_deepseek(const llama_model & model, const llm_graph_params & params);
|
||||
struct llm_build_deepseek32 : public llm_graph_context {
|
||||
llm_build_deepseek32(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct llm_build_dots1 : public llm_graph_context {
|
||||
|
|
|
|||
Loading…
Reference in New Issue