def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
num_heads = self.hparams["num_attention_heads"]
num_kv_heads = self.hparams["num_key_value_heads"]
- head_dim = self.hparams["head_dim"]
+ if (head_dim := self.hparams.get("head_dim")) is None:
+ head_dim = self.hparams["hidden_size"] // num_heads
if "ernie." in name:
name = name.replace("ernie.", "model.")
return [(self.map_tensor_name(name), data_torch)]
+@ModelBase.register("Ernie4_5_MoeForCausalLM")
+class Ernie4_5MoeModel(Ernie4_5Model):
+ model_arch = gguf.MODEL_ARCH.ERNIE4_5_MOE
+ _experts: list[dict[str, Tensor]] | None = None
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self._experts = [{} for _ in range(self.block_count)]
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters()
+ self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
+ self.gguf_writer.add_expert_used_count(self.hparams["moe_k"])
+ self.gguf_writer.add_interleave_moe_layer_step(self.hparams["moe_layer_interval"])
+ self.gguf_writer.add_leading_dense_block_count(self.hparams["moe_layer_start_index"])
+ self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
+ if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+ self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+ if (shared_expert_intermediate_size := self.hparams.get('intermediate_size')) is not None and (num_key_value_heads := self.hparams.get('num_key_value_heads')) is not None:
+ self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size // num_key_value_heads)
+
+ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+ # Modify correction bias name as in DeepseekV2
+ if name.endswith("e_score_correction_bias"):
+ name = name.replace("e_score_correction_bias", "e_score_correction.bias")
+
+ # skip Multi-Token Prediction (MTP) layers (again, same as DeepseekV2)
+ match = re.match(r"model.mtp_block.(\d+)", name)
+ if match:
+ return []
+
+ # skip all other MTP tensors for now
+ match = re.match(r"model.mtp_emb_norm.(\d+)", name)
+ if match:
+ return []
+
+ match = re.match(r"model.mtp_hidden_norm.(\d+)", name)
+ if match:
+ return []
+
+ match = re.match(r"model.mtp_linear_proj.(\d+)", name)
+ if match:
+ return []
+
+ # process the experts separately
+ if name.find("mlp.experts") != -1:
+ n_experts = self.hparams["moe_num_experts"]
+ assert bid is not None
+
+ if self._experts is None:
+ self._experts = [{} for _ in range(self.block_count)]
+
+ self._experts[bid][name] = data_torch
+
+ if len(self._experts[bid]) >= n_experts * 3:
+ tensors: list[tuple[str, Tensor]] = []
+
+ # merge the experts into a single 3d tensor
+ for w_name in ["gate_proj", "up_proj", "down_proj"]:
+ datas: list[Tensor] = []
+
+ for xid in range(n_experts):
+ ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+ datas.append(self._experts[bid][ename_to_retrieve])
+ del self._experts[bid][ename_to_retrieve]
+
+ data_torch = torch.stack(datas, dim=0)
+ merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+ new_name = self.map_tensor_name(merged_name)
+ tensors.append((new_name, data_torch))
+
+ return tensors
+ else:
+ return []
+ return [(self.map_tensor_name(name), data_torch)]
+
+ def prepare_tensors(self):
+ super().prepare_tensors()
+
+ if self._experts is not None:
+ # flatten `list[dict[str, Tensor]]` into `list[str]`
+ experts = [k for d in self._experts for k in d.keys()]
+ if len(experts) > 0:
+ raise ValueError(f"Unprocessed experts: {experts}")
+
+
@ModelBase.register(
"Qwen2VLModel",
"Qwen2VLForConditionalGeneration",
),
MODEL_TENSOR.FFN_EXP_PROBS_B: (
- "model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3 dots1
+ "model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3 dots1
+ "model.layers.{bid}.mlp.moe_statics.e_score_correction", # ernie4.5-moe
),
# Feed-forward up
),
MODEL_TENSOR.FFN_UP_EXP: (
- "layers.{bid}.feed_forward.experts.w3", # mixtral (merged)
- "transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged)
- "transformer.blocks.{bid}.ffn.experts.mlp.v1", # dbrx
- "model.layers.{bid}.mlp.experts.up_proj", # qwen2moe olmoe (merged)
- "model.layers.{bid}.block_sparse_moe.experts.w3", # phimoe (merged)
- "model.layers.{bid}.feed_forward.experts.up_proj", # llama4
- "encoder.layers.{bid}.mlp.experts.mlp.w1", # nomic-bert-moe
+ "layers.{bid}.feed_forward.experts.w3", # mixtral (merged)
+ "transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged)
+ "transformer.blocks.{bid}.ffn.experts.mlp.v1", # dbrx
+ "model.layers.{bid}.mlp.experts.up_proj", # qwen2moe olmoe (merged) ernie4.5-moe
+ "model.layers.{bid}.block_sparse_moe.experts.w3", # phimoe (merged)
+ "model.layers.{bid}.feed_forward.experts.up_proj", # llama4
+ "encoder.layers.{bid}.mlp.experts.mlp.w1", # nomic-bert-moe
),
MODEL_TENSOR.FFN_UP_SHEXP: (
),
MODEL_TENSOR.FFN_GATE_EXP: (
- "layers.{bid}.feed_forward.experts.w1", # mixtral (merged)
- "transformer.decoder_layer.{bid}.moe.linear", # Grok (merged)
- "transformer.blocks.{bid}.ffn.experts.mlp.w1", # dbrx
- "model.layers.{bid}.mlp.experts.gate_proj", # qwen2moe olmoe (merged)
- "model.layers.{bid}.block_sparse_moe.experts.w1", # phimoe (merged)
- "model.layers.{bid}.feed_forward.experts.gate_proj", # llama4
+ "layers.{bid}.feed_forward.experts.w1", # mixtral (merged)
+ "transformer.decoder_layer.{bid}.moe.linear", # Grok (merged)
+ "transformer.blocks.{bid}.ffn.experts.mlp.w1", # dbrx
+ "model.layers.{bid}.mlp.experts.gate_proj", # qwen2moe olmoe (merged) ernie4.5-moe
+ "model.layers.{bid}.block_sparse_moe.experts.w1", # phimoe (merged)
+ "model.layers.{bid}.feed_forward.experts.gate_proj", # llama4
),
MODEL_TENSOR.FFN_GATE_SHEXP: (
),
MODEL_TENSOR.FFN_DOWN_EXP: (
- "layers.{bid}.feed_forward.experts.w2", # mixtral (merged)
- "transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged)
- "transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx
- "model.layers.{bid}.mlp.experts.down_proj", # qwen2moe olmoe (merged)
- "model.layers.{bid}.block_sparse_moe.output_linear", # granitemoe
- "model.layers.{bid}.block_sparse_moe.experts.w2", # phimoe (merged)
- "model.layers.{bid}.feed_forward.experts.down_proj", # llama4
- "encoder.layers.{bid}.mlp.experts.mlp.w2", # nomic-bert-moe
+ "layers.{bid}.feed_forward.experts.w2", # mixtral (merged)
+ "transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged)
+ "transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx
+ "model.layers.{bid}.mlp.experts.down_proj", # qwen2moe olmoe (merged) ernie4.5-moe
+ "model.layers.{bid}.block_sparse_moe.output_linear", # granitemoe
+ "model.layers.{bid}.block_sparse_moe.experts.w2", # phimoe (merged)
+ "model.layers.{bid}.feed_forward.experts.down_proj", # llama4
+ "encoder.layers.{bid}.mlp.experts.mlp.w2", # nomic-bert-moe
),
MODEL_TENSOR.FFN_DOWN_SHEXP: (
{ LLM_ARCH_DOTS1, "dots1" },
{ LLM_ARCH_ARCEE, "arcee" },
{ LLM_ARCH_ERNIE4_5, "ernie4_5" },
+ { LLM_ARCH_ERNIE4_5_MOE, "ernie4_5-moe" },
{ LLM_ARCH_HUNYUAN_MOE, "hunyuan-moe" },
{ LLM_ARCH_SMOLLM3, "smollm3" },
{ LLM_ARCH_LFM2, "lfm2" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
+ {
+ LLM_ARCH_ERNIE4_5_MOE,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
+ { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
+ { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
+ { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
+ { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
+ { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
+ { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
+ { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
+ { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
+ { LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" },
+ },
+ },
{
LLM_ARCH_HUNYUAN_MOE,
{
case LLM_TYPE_17B_16E: return "17Bx16E (Scout)";
case LLM_TYPE_17B_128E: return "17Bx128E (Maverick)";
case LLM_TYPE_A13B: return "A13B";
+ case LLM_TYPE_21B_A3B: return "21B.A3B";
case LLM_TYPE_30B_A3B: return "30B.A3B";
case LLM_TYPE_235B_A22B: return "235B.A22B";
+ case LLM_TYPE_300B_A47B: return "300B.A47B";
case LLM_TYPE_E2B: return "E2B";
case LLM_TYPE_E4B: return "E4B";
default: return "?B";
}
} break;
case LLM_ARCH_ERNIE4_5:
+ case LLM_ARCH_ERNIE4_5_MOE:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ if (arch == LLM_ARCH_ERNIE4_5_MOE) {
+ ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+ ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+ ml.get_key(LLM_KV_INTERLEAVE_MOE_LAYER_STEP, hparams.n_moe_layer_step);
+ ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
+ }
+
switch (hparams.n_layer) {
case 18: type = LLM_TYPE_0_3B; break;
+ case 28: type = LLM_TYPE_21B_A3B; break;
+ case 54: type = LLM_TYPE_300B_A47B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
}
} break;
case LLM_ARCH_ERNIE4_5:
+ case LLM_ARCH_ERNIE4_5_MOE:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
- layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
- layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
- layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
+
+ if (arch == LLM_ARCH_ERNIE4_5_MOE && static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) { // MoE layers
+ int n_ff_exp = hparams.n_ff_exp;
+
+ layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+ layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+ layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED);
+ layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert}, 0);
+ layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
+
+ // Shared expert (if present)
+ if (hparams.n_ff_shexp > 0) {
+ layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, hparams.n_ff_shexp}, 0);
+ layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd }, 0);
+ layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, hparams.n_ff_shexp}, 0);
+ }
+ } else { // Dense layers
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
+ }
}
} break;
case LLM_ARCH_FALCON_H1:
}
};
+struct llm_build_ernie4_5_moe : public llm_graph_context {
+ llm_build_ernie4_5_moe(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+ GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ inpL = build_inp_embd(model.tok_embd);
+
+ // inp_pos - contains the positions
+ ggml_tensor * inp_pos = build_inp_pos();
+
+ auto * inp_attn = build_attn_inp_kv_unified();
+
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+ GGML_ASSERT(hparams.n_moe_layer_step > 0 && "Ernie 4.5 MoE requires n_moe_layer_step > 0");
+ for (int il = 0; il < n_layer; ++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
+ {
+ // compute Q and K and RoPE them
+ ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+ if (model.layers[il].bq) {
+ Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+ cb(Qcur, "Qcur", il);
+ }
+
+ ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+ if (model.layers[il].bk) {
+ Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+ cb(Kcur, "Kcur", il);
+ }
+
+ ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+ if (model.layers[il].bv) {
+ Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+ cb(Vcur, "Vcur", il);
+ }
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ cur = build_attn(inp_attn, gf,
+ model.layers[il].wo, NULL,
+ Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+ cb(cur, "attn_out", 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);
+
+ // feed-forward network
+ bool is_moe_layer = static_cast<uint32_t>(il) >= hparams.n_layer_dense_lead && (il + 1) % hparams.n_moe_layer_step == 0;
+
+ if (!is_moe_layer) {
+ cur = build_norm(ffn_inp,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ 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
+ cur = build_norm(ffn_inp,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ 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, true,
+ false, 0.0,
+ LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+ il);
+ cb(moe_out, "ffn_moe_out", il);
+
+ // Shared expert (if present)
+ if (hparams.n_ff_shexp > 0) {
+ 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);
+ } else {
+ cur = moe_out;
+ }
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "ffn_out", il);
+
+ cur = build_cvec(cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = build_norm(cur,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, -1);
+
+ cb(cur, "result_norm", -1);
+ res->t_embd = cur;
+
+ // lm_head
+ cur = build_lora_mm(model.output, cur);
+
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+ }
+};
+
struct llm_build_falcon_h1 : public llm_graph_context_mamba {
llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context_mamba(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
{
llm = std::make_unique<llm_build_ernie4_5>(*this, params, gf);
} break;
+ case LLM_ARCH_ERNIE4_5_MOE:
+ {
+ llm = std::make_unique<llm_build_ernie4_5_moe>(*this, params, gf);
+ } break;
case LLM_ARCH_HUNYUAN_MOE:
{
llm = std::make_unique<llm_build_hunyuan_moe>(*this, params, gf);
case LLM_ARCH_SMOLLM3:
case LLM_ARCH_ARCEE:
case LLM_ARCH_ERNIE4_5:
+ case LLM_ARCH_ERNIE4_5_MOE:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2
overview / pkg / ggml / sources / llama.cpp / commitdiff