class Step35Model(TextModel):
model_arch = gguf.MODEL_ARCH.STEP35
+ # The --mtp / --no-mtp toggles are ModelBase.mtp_only / no_mtp (set in
+ # convert_hf_to_gguf.py main()). Unlike Qwen3.5, which stores MTP under a
+ # `mtp.*` namespace, Step3.5 appends MTP layers at
+ # `model.layers.{num_hidden_layers + i}`, so we filter them by layer index.
+ # The trunk layer count is captured before indexing so the classmethod
+ # filter_tensors can tell the appended MTP block(s) apart from the trunk.
+ _n_main_layers: int | None = None
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ # NextN/MTP layers are appended past num_hidden_layers; extend the
+ # tensor map to cover them so the MTP block's tensors get correctly
+ # indexed names. When --no-mtp drops the MTP blocks, fall back to the
+ # base num_hidden_layers so we don't reserve unused slots.
+ n_nextn = int(self.hparams.get("num_nextn_predict_layers", 0))
+ if n_nextn > 0 and not self.no_mtp:
+ self.block_count += n_nextn
+ self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+ def index_tensors(self, remote_hf_model_id: str | None = None):
+ # filter_tensors is a classmethod and can't reach self.hparams; stash
+ # the trunk layer count here (before indexing runs) so it can detect
+ # the appended MTP layers by index.
+ hparams = {**self.hparams, **self.hparams.get("text_config", {})}
+ key = next((k for k in ["n_layers", "num_hidden_layers", "n_layer", "num_layers"] if k in hparams), None)
+ type(self)._n_main_layers = hparams.get(key)
+ return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
+
def set_gguf_parameters(self):
rope_theta = self.hparams.get("rope_theta")
if isinstance(rope_theta, list):
n_head_swa = attn_other.get("num_attention_heads", n_head_base)
n_kv_swa = attn_other.get("num_attention_groups", n_kv_base)
- layer_types = layer_types[: self.block_count]
- partial_rotary_factors = partial_rotary_factors[: self.block_count]
+ n_nextn = int(self.hparams.get("num_nextn_predict_layers", 0))
+
+ # The Step3p5 HF checkpoint stores layer_types/partial_rotary_factors
+ # entries for the MTP blocks past num_hidden_layers; preserve them so
+ # the MTP layer's attention shape, SWA flag, and partial RoPE dim are
+ # set correctly. Pad with full-attention defaults if the checkpoint
+ # truncated them.
+ def _pad(arr, n, default):
+ arr = list(arr)
+ if len(arr) < n:
+ arr = arr + [default] * (n - len(arr))
+ return arr[:n]
+
+ layer_types = _pad(layer_types, self.block_count, "full_attention")
+ partial_rotary_factors = _pad(
+ partial_rotary_factors,
+ self.block_count,
+ 0.5, # full_attention default for Step3p5
+ )
assert [1.0 if lt == "sliding_attention" else 0.5 for lt in layer_types] == partial_rotary_factors
head_arr = [n_head_swa if lt == "sliding_attention" else n_head_base for lt in layer_types]
kv_arr = [n_kv_swa if lt == "sliding_attention" else n_kv_base for lt in layer_types]
self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-5))
- # Optional per-layer SwiGLU clamps.
+ # Optional per-layer SwiGLU clamps. MTP layers default to no clamping (0.0).
if (limits := self.hparams.get("swiglu_limits")) is not None:
- limits_f = [0.0 if v is None else float(v) for v in limits[: self.block_count]]
+ limits_f = _pad(
+ [0.0 if v is None else float(v) for v in limits],
+ self.block_count,
+ 0.0,
+ )
self.gguf_writer.add_swiglu_clamp_exp(limits_f)
if (limits_shared := self.hparams.get("swiglu_limits_shared")) is not None:
- limits_shared_f = [0.0 if v is None else float(v) for v in limits_shared[: self.block_count]]
+ limits_shared_f = _pad(
+ [0.0 if v is None else float(v) for v in limits_shared],
+ self.block_count,
+ 0.0,
+ )
self.gguf_writer.add_swiglu_clamp_shexp(limits_shared_f)
+ if n_nextn > 0 and not self.no_mtp:
+ self.gguf_writer.add_nextn_predict_layers(n_nextn)
+
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
- name, gen = item
+ if (titem := super().filter_tensors(item)) is None:
+ return None
+ name, gen = titem
# Map router bias (expert selection bias) to a GGUF bias tensor
if name.endswith(".moe.router_bias"):
name += ".bias"
- return super().filter_tensors((name, gen))
+ # Step3.5 appends the MTP block(s) past num_hidden_layers.
+ assert cls._n_main_layers is not None
+ is_mtp = (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers
+
+ # --no-mtp: drop the appended MTP block(s) entirely.
+ if is_mtp and cls.no_mtp:
+ return None
+ # --mtp: keep ONLY MTP-block tensors plus the shared embeddings/norm/
+ # lm_head (so the resulting GGUF carries just the draft head).
+ if cls.mtp_only and not is_mtp and name not in (
+ "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
+ ):
+ return None
+
+ # The checkpoint nests the per-MTP-layer shared head under
+ # `model.layers.{N+i}.transformer.shared_head.{norm,output}.weight`;
+ # strip the `transformer.` infix and rename `output` → `head` so the
+ # existing NEXTN_SHARED_HEAD_{NORM,HEAD} tensor mapping picks them up.
+ # Mirrors vllm's `_rewrite_spec_layer_name` (step3p5_mtp.py).
+ if is_mtp:
+ name = name.replace(".transformer.", ".")
+ name = name.replace("shared_head.output", "shared_head.head")
+
+ return name, gen
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
- # remove mtp layers
- if (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None:
- il = int(m.group(1))
- n_main = int(self.hparams.get("num_hidden_layers", self.block_count))
- if il >= n_main:
- return
if name.endswith("norm.weight"):
data_torch += 1.0
yield from super().modify_tensors(data_torch, name, bid)
+ def prepare_metadata(self, vocab_only: bool):
+ from_dir = self.fname_out.is_dir()
+ super().prepare_metadata(vocab_only=vocab_only)
+
+ # Mirror Qwen3.5's behavior: when emitting a draft-only file into a
+ # directory, prefix with "mtp-" so it doesn't collide with the trunk.
+ if not self.mtp_only or not from_dir:
+ return
+
+ output_type: str = self.ftype.name.partition("_")[2]
+ fname_default: str = gguf.naming_convention(
+ self.metadata.name, self.metadata.basename, self.metadata.finetune,
+ self.metadata.version, size_label=None, output_type=output_type, model_type=None)
+ self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
+
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
# Step35 can optionally use Llama-3 style RoPE scaling (HF: rope_scaling.rope_type == "llama3").
# llama.cpp represents this via a single extra tensor: "rope_freqs.weight" (aka MODEL_TENSOR.ROPE_FREQS).
ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp, hparams.n_layer, false);
ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer, false);
- switch (hparams.n_layer) {
+ // NextN/MTP (Step3p5): extra decoder block appended beyond the main stack.
+ ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
+ GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
+
+ switch (hparams.n_layer - hparams.nextn_predict_layers) {
case 45: type = LLM_TYPE_196B_A11B; break;
default: type = LLM_TYPE_UNKNOWN;
}
}
-void llama_model_step35::load_arch_tensors(llama_model_loader &) {
+void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
+ const uint32_t n_main = n_layer - hparams.nextn_predict_layers;
+ const bool mtp_only = (hparams.nextn_predict_layers > 0) &&
+ (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
+ // Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP
+ // tensors live in a separate file (e.g. user split target/draft). Mark
+ // MTP tensors NOT_REQUIRED so the trunk loads cleanly.
+ const std::string mtp_probe = "blk." + std::to_string(n_main) + ".nextn.eh_proj.weight";
+ const bool trunk_only = (hparams.nextn_predict_layers > 0) &&
+ (ml.get_weight(mtp_probe.c_str()) == nullptr);
+ const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
+ const int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
+
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
- output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
+ output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, trunk_flags);
// STEP35 supports per-layer partial RoPE dims; rope factors are stored as a single shared tensor
// ("rope_freqs.weight") and ggml uses only the first (n_rot_l/2) entries per layer.
n_rot_max = n_rot;
}
- for (int i = 0; i < n_layer; ++i) {
+ auto load_block_trunk = [&](int i, int flags) {
auto & layer = layers[i];
const uint32_t n_head_l = hparams.n_head(i);
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i);
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
- layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
}
- create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_l, n_embd_k_gqa, n_embd_v_gqa, 0);
- layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v * n_head_l, n_embd}, 0);
+ create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_l, n_embd_k_gqa, n_embd_v_gqa, flags);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v * n_head_l, n_embd}, flags);
// head-wise attention gate (Step35 self_attn.g_proj)
layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_l}, TENSOR_NOT_REQUIRED);
- layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
// dense MLP (leading dense blocks)
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
+ };
+
+ auto load_block_mtp = [&](int i, bool is_first_mtp) {
+ auto & layer = layers[i];
+
+ const uint32_t n_head_l = hparams.n_head(i);
+ const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i);
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
+
+ // The MTP block is a full Step3p5 decoder layer (mtp_block) plus the
+ // NextN-specific wiring (enorm/hnorm/eh_proj + optional shared head).
+ // `mtp_flags` becomes NOT_REQUIRED when the GGUF is trunk-only.
+ //
+ // Only the FIRST MTP block (i == n_main) is required for the
+ // single-block MTP runtime; trailing MTP blocks are always tolerated
+ // as missing so pruned GGUFs (block 0 only) load cleanly. Override
+ // mtp_flags to NOT_REQUIRED for those.
+ const int eff_mtp_flags = is_first_mtp ? mtp_flags : (mtp_flags | TENSOR_NOT_REQUIRED);
+
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, eff_mtp_flags);
+ layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
+ layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
+
+ if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+ layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | TENSOR_DUPLICATED);
+ layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | TENSOR_DUPLICATED);
+ } else {
+ layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | TENSOR_DUPLICATED);
+ }
+
+ create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_l, n_embd_k_gqa, n_embd_v_gqa, eff_mtp_flags);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v * n_head_l, n_embd}, eff_mtp_flags);
+
+ layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_l}, TENSOR_NOT_REQUIRED);
+
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, eff_mtp_flags);
+
+ // dense MLP (leading dense blocks) — present if the MTP block isn't MoE
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, TENSOR_NOT_REQUIRED);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
+
+ // MoE routed experts + selection bias (router_bias)
+ const int64_t 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}, 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}, TENSOR_NOT_REQUIRED);
+ layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED);
+ layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+ layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
+ layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
+ layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
+
+ // NextN-specific tensors that define the MTP block.
+ layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, eff_mtp_flags);
+ layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, eff_mtp_flags);
+ layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, eff_mtp_flags);
+ layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
+ layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
+ layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED);
+ };
+
+ for (int i = 0; i < (int) n_main; ++i) {
+ load_block_trunk(i, trunk_flags);
+ }
+ // Only the first MTP block (i == n_main) is required at runtime — the
+ // single-block-MTP graph in build_arch_graph always uses that one.
+ // Trailing MTP blocks are loaded if present (so an un-pruned GGUF with
+ // all MTP layers still works) but tolerated when absent via the pruning
+ // path. See scripts/prune_step35_extra_mtp.py for the pruner.
+ for (int i = (int) n_main; i < n_layer; ++i) {
+ load_block_mtp(i, /*is_first_mtp=*/ i == (int) n_main);
}
}
std::unique_ptr<llm_graph_context> llama_model_step35::build_arch_graph(const llm_graph_params & params) const {
+ if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
+ return std::make_unique<graph_mtp>(*this, params);
+ }
return std::make_unique<graph>(*this, params);
}
auto * inp_attn = build_attn_inp_kv_iswa();
ggml_tensor * inp_out_ids = build_inp_out_ids();
- for (int il = 0; il < n_layer; ++il) {
+ // MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass.
+ const int n_transformer_layers = n_layer - (int) hparams.nextn_predict_layers;
+ for (int il = 0; il < n_transformer_layers; ++il) {
ggml_tensor * inpSA = inpL;
const uint32_t n_head_l = hparams.n_head(il);
cb(cur, "attn_proj", il);
}
- if (il == n_layer - 1 && inp_out_ids) {
- cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ if (il == n_transformer_layers - 1 && inp_out_ids && cparams.embeddings_pre_norm_masked) {
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
cur = inpL;
+ cb(cur, "h_pre_norm", -1);
+ res->t_h_pre_norm = cur;
+
+ if (!cparams.embeddings_pre_norm_masked && inp_out_ids) {
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ }
+
cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
ggml_build_forward_expand(gf, cur);
}
+
+// LLM_GRAPH_TYPE_DECODER_MTP draft head for Step3p5 (MoE)
+llama_model_step35::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
+ : llm_graph_context(params) {
+ GGML_ASSERT(hparams.nextn_predict_layers > 0 && "STEP35 MTP requires nextn_predict_layers > 0");
+
+ // Single-block MTP only: always run the first trained MTP block (Qwen
+ // MTP / vLLM single-MTP-layer style). Multi-block round-robin proved to
+ // be a much deeper refactor than this PR justifies; the trailing MTP
+ // blocks are loaded with TENSOR_NOT_REQUIRED so pruned GGUFs (with just
+ // block 0) also work — see load_arch_tensors below and
+ // scripts/prune_step35_extra_mtp.py.
+ const int il = (int) hparams.n_layer - (int) hparams.nextn_predict_layers;
+ const auto & layer = model.layers[il];
+
+ GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
+ GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm");
+ GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm");
+
+ const uint32_t n_head_l = hparams.n_head(il);
+ const uint32_t n_head_kv_l = hparams.n_head_kv(il);
+
+ const float freq_base_l = model.get_rope_freq_base(cparams, il);
+ const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
+ auto inp = std::make_unique<llm_graph_input_embd>(hparams.n_embd);
+
+ inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+ ggml_set_input(inp->tokens);
+
+ inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
+ ggml_set_input(inp->embd);
+ ggml_set_name(inp->embd, "mtp_h_input");
+
+ ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
+
+ ggml_tensor * h_input = inp->embd;
+ ggml_tensor * tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
+ cb(tok_embd, "mtp_tok_embd", il);
+
+ res->add_input(std::move(inp));
+
+ ggml_tensor * inp_pos = build_inp_pos();
+ auto * inp_attn = build_attn_inp_kv_iswa();
+
+ ggml_tensor * h_norm = build_norm(h_input, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
+ cb(h_norm, "mtp_hnorm", il);
+
+ ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
+ cb(e_norm, "mtp_enorm", il);
+
+ ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
+ cb(concat, "mtp_concat", il);
+
+ ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat);
+ cb(cur, "mtp_eh_proj", il);
+
+ ggml_tensor * inpSA = cur;
+
+ // mtp_block: full Step3p5 decoder layer (attention with optional head-wise gate, then MoE/dense FFN)
+ cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
+ cb(cur, "mtp_attn_norm", il);
+
+ ggml_tensor * Qcur = build_lora_mm(layer.wq, cur, layer.wq_s);
+ ggml_tensor * Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
+ ggml_tensor * Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
+ cb(Qcur, "mtp_Qcur", il);
+ cb(Kcur, "mtp_Kcur", il);
+ cb(Vcur, "mtp_Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens);
+ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens);
+
+ if (layer.attn_q_norm) {
+ Qcur = build_norm(Qcur, layer.attn_q_norm, nullptr, LLM_NORM_RMS, il);
+ cb(Qcur, "mtp_Qcur_normed", il);
+ }
+ if (layer.attn_k_norm) {
+ Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
+ cb(Kcur, "mtp_Kcur_normed", il);
+ }
+
+ const bool is_swa = hparams.is_swa(il);
+ ggml_tensor * rope_factors = is_swa ? nullptr : model.get_rope_factors(cparams, il);
+ const int64_t n_rot_l = hparams.n_rot(il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, rope_factors,
+ n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, rope_factors,
+ n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ cb(Qcur, "mtp_Qcur_pos", il);
+ cb(Kcur, "mtp_Kcur_pos", il);
+
+ const float kq_scale = 1.0f / sqrtf(float(n_embd_head_k));
+ ggml_tensor * attn_out = build_attn(inp_attn,
+ nullptr, nullptr, nullptr,
+ Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+ cb(attn_out, "mtp_attn_out", il);
+
+ // head-wise attention gate: sigmoid(g_proj(x))
+ if (layer.wqkv_gate) {
+ ggml_tensor * gate = build_lora_mm(layer.wqkv_gate, cur); // [n_head_l, n_tokens]
+ cb(gate, "mtp_attn_gate", il);
+
+ gate = ggml_sigmoid(ctx0, gate);
+ cb(gate, "mtp_attn_gate_sigmoid", il);
+
+ ggml_tensor * attn_3d = ggml_reshape_3d(ctx0, attn_out, n_embd_head_v, n_head_l, n_tokens);
+ ggml_tensor * gate_3d = ggml_reshape_3d(ctx0, gate, 1, n_head_l, n_tokens);
+ cb(gate_3d, "mtp_attn_gate_3d", il);
+
+ attn_3d = ggml_mul(ctx0, attn_3d, gate_3d);
+ cb(attn_3d, "mtp_attn_gated_3d", il);
+
+ attn_out = ggml_reshape_2d(ctx0, attn_3d, n_embd_head_v * n_head_l, n_tokens);
+ cb(attn_out, "mtp_attn_gated", il);
+ }
+
+ cur = build_lora_mm(layer.wo, attn_out, layer.wo_s);
+ cb(cur, "mtp_attn_proj", il);
+
+ cur = ggml_add(ctx0, cur, inpSA);
+ cb(cur, "mtp_attn_residual", il);
+
+ ggml_tensor * ffn_inp = cur;
+ cur = build_norm(cur, layer.ffn_norm, nullptr, LLM_NORM_RMS, il);
+ cb(cur, "mtp_ffn_norm", il);
+
+ // FFN: dense MLP or MoE (mirrors trunk path)
+ if (layer.ffn_gate_inp == nullptr) {
+ cur = build_ffn(cur,
+ layer.ffn_up, layer.ffn_up_b, nullptr,
+ layer.ffn_gate, layer.ffn_gate_b, nullptr,
+ layer.ffn_down, layer.ffn_down_b, nullptr,
+ nullptr,
+ LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(cur, "mtp_ffn_out", il);
+ } else {
+ ggml_tensor * moe_out = build_moe_ffn(cur,
+ layer.ffn_gate_inp,
+ layer.ffn_up_exps,
+ layer.ffn_gate_exps,
+ layer.ffn_down_exps,
+ layer.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);
+ cb(moe_out, "mtp_ffn_moe_out", il);
+
+ ggml_tensor * sh_out = build_ffn(cur,
+ layer.ffn_up_shexp, nullptr, nullptr,
+ layer.ffn_gate_shexp, nullptr, nullptr,
+ layer.ffn_down_shexp, nullptr, nullptr,
+ nullptr,
+ LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(sh_out, "mtp_ffn_shared_out", il);
+
+ cur = ggml_add(ctx0, moe_out, sh_out);
+ cb(cur, "mtp_ffn_out", il);
+ }
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "mtp_post_ffn", il);
+
+ // Pre-norm hidden state: used by the AR draft loop to seed the next MTP step.
+ cb(cur, "h_pre_norm", -1);
+ res->t_h_pre_norm = cur;
+
+ ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
+ ? layer.nextn.shared_head_norm
+ : model.output_norm;
+ GGML_ASSERT(head_norm_w && "STEP35 MTP: missing both nextn.shared_head_norm and output_norm");
+ cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
+ cb(cur, "mtp_shared_head_norm", -1);
+
+ ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
+ GGML_ASSERT(head_w && "STEP35 MTP: missing LM head (nextn.shared_head_head or model.output)");
+ cur = build_lora_mm(head_w, cur);
+ cb(cur, "result_output", -1);
+
+ res->t_logits = cur;
+ ggml_build_forward_expand(gf, cur);
+}
overview / pkg / ggml / sources / llama.cpp / commitdiff