mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-08-04 00:50:47 -05:00
model : support MTP in GLM-4.7-Flash (#24868)
This commit is contained in:
jacekpoplawski
@@ -206,10 +206,70 @@ class Glm4MoeModel(TextModel):
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@ModelBase.register("Glm4MoeLiteForCausalLM")
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class Glm4MoeLiteModel(DeepseekV2Model):
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model_arch = gguf.MODEL_ARCH.DEEPSEEK2
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skip_mtp = False
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supports_mtp_export = True
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_n_main_layers: int | None = None
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def set_vocab(self):
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return self._set_vocab_glm()
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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num_hidden_layers = self.hparams["num_hidden_layers"]
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self.num_nextn_predict_layers = self.hparams.get("num_nextn_predict_layers", 0)
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self.skip_mtp = self.no_mtp or self.num_nextn_predict_layers == 0
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if self.skip_mtp:
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self.block_count = num_hidden_layers
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else:
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self.block_count = num_hidden_layers + self.num_nextn_predict_layers
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self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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if self.skip_mtp:
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return
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self.gguf_writer.add_nextn_predict_layers(self.num_nextn_predict_layers)
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def index_tensors(self, remote_hf_model_id: str | None = None):
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type(self)._n_main_layers = self.hparams["num_hidden_layers"]
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return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
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@classmethod
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def filter_tensors(cls, item):
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if (titem := super().filter_tensors(item)) is None:
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return None
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name, gen = titem
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if cls._n_main_layers is not None:
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match = re.match(r"model\.layers\.(\d+)\.", name)
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is_mtp = match is not None and int(match.group(1)) >= cls._n_main_layers
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if is_mtp and cls.no_mtp:
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return None
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if cls.mtp_only and not is_mtp and name not in (
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"model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
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):
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return None
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return name, gen
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def prepare_metadata(self, vocab_only: bool):
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from_dir = self.fname_out.is_dir()
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super().prepare_metadata(vocab_only=vocab_only)
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if not self.mtp_only or not from_dir:
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return
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output_type: str = self.ftype.name.partition("_")[2]
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fname_default: str = gguf.naming_convention(
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self.metadata.name, self.metadata.basename, self.metadata.finetune,
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self.metadata.version, size_label=None, output_type=output_type, model_type=None)
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self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
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@ModelBase.register("GlmMoeDsaForCausalLM")
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class GlmMoeDsaModel(DeepseekV2Model):
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@@ -3221,6 +3221,13 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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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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# NextN/MTP tensors
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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.DEEPSEEK2OCR: [
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MODEL_TENSOR.TOKEN_EMBD,
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@@ -37,6 +37,11 @@ void llama_model_deepseek2::load_arch_hparams(llama_model_loader & ml) {
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hparams.rope_yarn_log_mul /= 0.1f;
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}
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// NextN/MTP
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ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
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GGML_ASSERT(hparams.n_layer_nextn == 0 ||
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hparams.n_layer() + hparams.n_layer_nextn == hparams.n_layer_all);
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// (optional) temperature tuning - used by mistral-large
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ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false);
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ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false); // FIXME why not use temperature_length?
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@@ -52,10 +57,20 @@ void llama_model_deepseek2::load_arch_hparams(llama_model_loader & ml) {
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}
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}
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void llama_model_deepseek2::load_arch_tensors(llama_model_loader &) {
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void llama_model_deepseek2::load_arch_tensors(llama_model_loader & ml) {
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LLAMA_LOAD_LOCALS;
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const int64_t n_expert_shared = hparams.n_expert_shared;
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const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
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const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
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const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
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const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
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int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
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if (!ml.load_mtp) {
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mtp_flags |= TENSOR_SKIP;
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}
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const bool is_mla = hparams.is_mla();
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// note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
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@@ -81,44 +96,45 @@ void llama_model_deepseek2::load_arch_tensors(llama_model_loader &) {
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output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
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}
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for (int i = 0; i < n_layer; ++i) {
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for (int i = 0; i < n_layer_all; ++i) {
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auto & layer = layers[i];
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const int flags = i < n_layer ? trunk_flags : mtp_flags;
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
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if (q_lora_rank > 0) {
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layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
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layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags);
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}
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layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
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layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags);
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if (q_lora_rank > 0) {
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layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
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layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0);
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layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags);
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layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, flags);
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} else {
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layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, 0);
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layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, flags);
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}
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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}, 0);
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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);
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// note: only old legacy GGUF files will have the unsplit wkv_b tensor in
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if (is_mla) {
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layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, 0);
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layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0);
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layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, flags);
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layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, flags);
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} else {
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layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v_mla)}, 0);
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layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v_mla)}, flags);
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}
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, flags);
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
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if (i < (int) hparams.n_layer_dense_lead) {
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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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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, flags);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, flags);
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} else {
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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_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
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layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
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layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | flags);
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if (n_expert == 0) {
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throw std::runtime_error("n_expert must be > 0");
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@@ -128,21 +144,281 @@ void llama_model_deepseek2::load_arch_tensors(llama_model_loader &) {
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}
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// MoE branch
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
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create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags);
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create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, flags);
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// Shared expert branch
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layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
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layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0);
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layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
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layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
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layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, flags);
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layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
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}
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// NextN/MTP tensors
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if (i >= n_layer) {
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layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, mtp_flags);
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layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, mtp_flags);
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layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, mtp_flags);
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layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
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layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
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layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED | flags);
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}
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}
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}
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std::unique_ptr<llm_graph_context> llama_model_deepseek2::build_arch_graph(const llm_graph_params & params) const {
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if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
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return std::make_unique<graph_mtp>(*this, params);
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}
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return std::make_unique<graph>(*this, params);
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}
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llama_model_deepseek2::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) :
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llm_graph_context(params) {
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GGML_ASSERT(hparams.n_layer_nextn > 0 && "GLM4 MTP requires n_layer_nextn > 0");
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GGML_ASSERT(hparams.n_layer_nextn == 1 && "GLM4 MTP currently only supports a single MTP block");
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GGML_ASSERT(hparams.is_mla() && "GLM4 MTP requires MLA");
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GGML_ASSERT(hparams.f_attn_temp_scale == 0.0f && "GLM4 MTP does not support attention temperature scaling");
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// The appended MTP block is stored immediately after the main decoder layers.
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const int il = hparams.n_layer();
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const auto & layer = model.layers[il];
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GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
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GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm");
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GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm");
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GGML_ASSERT((uint32_t) il >= hparams.n_layer_dense_lead && "GLM4 MTP block expected to use MoE FFN");
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const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
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const int64_t n_embd_head_qk_rope = hparams.n_rot();
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const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
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const int64_t kv_lora_rank = hparams.n_lora_kv;
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GGML_ASSERT(n_embd_head_qk_nope >= 1);
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GGML_ASSERT(hparams.n_lora_q > 0);
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GGML_ASSERT(layer.wq_a);
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GGML_ASSERT(layer.attn_q_a_norm);
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GGML_ASSERT(layer.wq_b);
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GGML_ASSERT(layer.wkv_a_mqa);
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GGML_ASSERT(layer.attn_kv_a_norm);
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GGML_ASSERT(layer.wk_b);
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const bool has_split_exps =
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layer.ffn_up_exps != nullptr &&
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layer.ffn_gate_exps != nullptr;
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const bool has_fused_exps = layer.ffn_gate_up_exps != nullptr;
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GGML_ASSERT(has_split_exps || has_fused_exps);
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GGML_ASSERT(layer.ffn_norm);
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GGML_ASSERT(layer.ffn_gate_inp);
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GGML_ASSERT(layer.ffn_down_exps);
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GGML_ASSERT(layer.ffn_gate_shexp);
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GGML_ASSERT(layer.ffn_down_shexp);
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GGML_ASSERT(layer.ffn_up_shexp);
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auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);
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inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
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ggml_set_input(inp->tokens);
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inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);
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ggml_set_input(inp->embd);
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ggml_tensor * tok_embd;
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if (ubatch.token) {
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ggml_tensor * tok_embd_w = layer.nextn.embed_tokens
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? layer.nextn.embed_tokens
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: model.tok_embd;
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tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
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} else {
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tok_embd = inp->embd;
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}
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cb(tok_embd, "mtp_tok_embd", il);
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inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
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ggml_set_input(inp->h);
|
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ggml_set_name(inp->h, "mtp_h_input");
|
||||
|
||||
ggml_tensor * h_embd = inp->h;
|
||||
|
||||
res->add_input(std::move(inp));
|
||||
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
auto * inp_attn_k = build_attn_inp_k();
|
||||
|
||||
ggml_tensor * h_norm = build_norm(h_embd, 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, 0);
|
||||
cb(concat, "mtp_concat", il);
|
||||
|
||||
ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
|
||||
cb(cur, "mtp_eh_proj", il);
|
||||
|
||||
ggml_tensor * inpSA = cur;
|
||||
|
||||
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(cur, "mtp_attn_norm", il);
|
||||
|
||||
ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_a, cur);
|
||||
cb(q, "mtp_q_a", il);
|
||||
|
||||
q = build_norm(q, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(q, "mtp_q_a_norm", il);
|
||||
|
||||
q = ggml_mul_mat(ctx0, layer.wq_b, q);
|
||||
cb(q, "mtp_q_b", il);
|
||||
|
||||
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_mla),
|
||||
ggml_row_size(q->type, n_embd_head_k_mla) * n_head, 0);
|
||||
cb(q_nope, "mtp_q_nope", il);
|
||||
|
||||
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_mla),
|
||||
ggml_row_size(q->type, n_embd_head_k_mla) * n_head,
|
||||
ggml_row_size(q->type, n_embd_head_qk_nope));
|
||||
cb(q_pe, "mtp_q_pe", il);
|
||||
|
||||
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
|
||||
cb(kv_cmpr_pe, "mtp_kv_cmpr_pe", il);
|
||||
|
||||
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, "mtp_kv_cmpr", il);
|
||||
|
||||
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, "mtp_k_pe", il);
|
||||
|
||||
kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(kv_cmpr, "mtp_kv_cmpr_norm", il);
|
||||
|
||||
GGML_ASSERT(ext_factor >= 0.0f);
|
||||
|
||||
const float attn_factor_org =
|
||||
attn_factor * (1.0f + 0.1f * logf(1.0f / freq_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_mla));
|
||||
|
||||
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, "mtp_q_pe_rope", 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, "mtp_k_pe_rope", il);
|
||||
|
||||
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
|
||||
cb(q_nope, "mtp_q_nope_perm", il);
|
||||
|
||||
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);
|
||||
cb(q_nope_absorbed, "mtp_q_nope_absorbed", il);
|
||||
|
||||
q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
|
||||
cb(q_nope_absorbed, "mtp_q_nope_absorbed_perm", il);
|
||||
|
||||
ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
|
||||
cb(Qcur, "mtp_Qcur", il);
|
||||
|
||||
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, hparams.n_lora_kv, 1, n_tokens);
|
||||
cb(kv_cmpr, "mtp_kv_cmpr_reshape", il);
|
||||
|
||||
ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
|
||||
cb(Kcur, "mtp_Kcur", il);
|
||||
|
||||
ggml_tensor * Vcur = kv_cmpr;
|
||||
cb(Vcur, "mtp_Vcur", il);
|
||||
|
||||
cur = build_attn(inp_attn_k,
|
||||
layer.wo, nullptr, layer.wo_s,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale, il);
|
||||
cb(cur, "mtp_attn_out", il);
|
||||
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "mtp_ffn_inp", il);
|
||||
|
||||
cur = build_norm(ffn_inp, layer.ffn_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(cur, "mtp_ffn_norm", il);
|
||||
|
||||
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,
|
||||
nullptr,
|
||||
layer.ffn_gate_up_exps);
|
||||
cb(moe_out, "mtp_ffn_moe_out", il);
|
||||
|
||||
ggml_tensor * ffn_shexp = 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(ffn_shexp, "mtp_ffn_shexp", il);
|
||||
|
||||
cur = ggml_add(ctx0, moe_out, ffn_shexp);
|
||||
cb(cur, "mtp_ffn_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "mtp_post_ffn", il);
|
||||
|
||||
ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
|
||||
? layer.nextn.shared_head_norm
|
||||
: model.output_norm;
|
||||
GGML_ASSERT(head_norm_w && "GLM4 MTP: missing both nextn.shared_head_norm and output_norm");
|
||||
|
||||
cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
|
||||
cb(cur, "h_nextn", -1);
|
||||
res->t_h_nextn = cur;
|
||||
|
||||
if (inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
}
|
||||
cb(cur, "mtp_shared_head_norm", -1);
|
||||
|
||||
ggml_tensor * head_w = layer.nextn.shared_head_head
|
||||
? layer.nextn.shared_head_head
|
||||
: model.output;
|
||||
|
||||
ggml_tensor * head_s = layer.nextn.shared_head_head
|
||||
? layer.nextn.shared_head_head_s
|
||||
: model.output_s;
|
||||
|
||||
GGML_ASSERT(head_w && "GLM4 MTP: missing LM head (nextn.shared_head_head or model.output)");
|
||||
|
||||
cur = build_lora_mm(head_w, cur, head_s);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
res->t_logits = cur;
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
||||
llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_params & params) :
|
||||
llm_graph_context(params) {
|
||||
// lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B
|
||||
@@ -365,7 +641,7 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
|
||||
}
|
||||
}
|
||||
if (il == n_layer - 1 && inp_out_ids) {
|
||||
if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
@@ -425,6 +701,13 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
|
||||
|
||||
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
|
||||
|
||||
cb(cur, "h_nextn", -1);
|
||||
res->t_h_nextn = cur;
|
||||
|
||||
if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
}
|
||||
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
|
||||
@@ -1084,6 +1084,10 @@ struct llama_model_deepseek2 : public llama_model_base {
|
||||
graph(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct graph_mtp : public llm_graph_context {
|
||||
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
||||
};
|
||||
|
||||
|
||||
Reference in New Issue
Block a user