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model : support MTP in GLM-4.5-Air (#26534)

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This commit is contained in:
jacekpoplawski
2026-08-23 21:20:44 +03:00
committed by GitHub
parent ccc8fd2baa
commit c060ca974c
4 changed files with 233 additions and 22 deletions
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src/models/glm4-moe.cpp
+186 -16
View File
@@ -29,10 +29,19 @@ void llama_model_glm4_moe::load_arch_hparams(llama_model_loader & ml) {
}
}
void llama_model_glm4_moe::load_arch_tensors(llama_model_loader &) {
void llama_model_glm4_moe::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
const int64_t n_expert_shared = hparams.n_expert_shared;
const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
if (!ml.load_mtp) {
mtp_flags |= TENSOR_SKIP;
}
GGML_ASSERT(hparams.n_expert > 0 && "n_expert must be > 0 for GLM4_MOE MoE layers");
GGML_ASSERT(hparams.n_expert_used > 0 && "n_expert_used must be > 0 for GLM4_MOE MoE layers");
@@ -47,16 +56,9 @@ void llama_model_glm4_moe::load_arch_tensors(llama_model_loader &) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
}
// Load ALL tensors including NextN layer to satisfy total tensor count
// but only PROCESS up to last layer (skipping final NextN layer) in forward pass
for (int i = 0; i < n_layer_all; ++i) {
int flags = 0;
if (i >= n_layer) {
// skip all tensors in the NextN layers
flags |= TENSOR_SKIP;
}
auto & layer = layers[i];
const int flags = i < n_layer ? trunk_flags : mtp_flags;
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags);
@@ -110,24 +112,186 @@ void llama_model_glm4_moe::load_arch_tensors(llama_model_loader &) {
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, flags);
}
// NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
// NextN/MTP tensors
if (i >= n_layer) {
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);
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED | flags);
}
}
}
std::unique_ptr<llm_graph_context> llama_model_glm4_moe::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);
}
llama_model_glm4_moe::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
: llm_graph_context(params) {
GGML_ASSERT(hparams.n_layer_nextn > 0 && "GLM4_MOE MTP requires n_layer_nextn > 0");
GGML_ASSERT(hparams.n_layer_nextn == 1 && "GLM4_MOE MTP currently only supports a single MTP block");
const int64_t n_embd_head = hparams.n_embd_head_v();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
const int il = hparams.n_layer() + cparams.nextn_layer_offset;
GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
"nextn_layer_offset out of range [0, n_layer_nextn)");
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");
GGML_ASSERT(layer.ffn_gate_inp && "MTP block missing ffn_gate_inp");
auto inp = std::make_unique<llm_graph_input_embd_h>(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_inp(), n_tokens);
ggml_set_input(inp->embd);
ggml_tensor * tok_embd;
if (ubatch.token) {
ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
} else {
tok_embd = inp->embd;
}
cb(tok_embd, "mtp_tok_embd", il);
inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
ggml_set_input(inp->h);
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 = build_attn_inp_kv();
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);
auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur,
n_embd_head, n_head, n_head_kv, il);
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);
}
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, "mtp_Qcur", il);
cb(Kcur, "mtp_Kcur", il);
cb(Vcur, "mtp_Vcur", il);
cur = build_attn(inp_attn,
layer.wo, nullptr, layer.wo_s,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
1.0f / sqrtf(float(n_embd_head)), 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.attn_post_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_post_attn_norm", il);
ggml_tensor * routed_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(routed_out, "mtp_ffn_moe_out", il);
ggml_tensor * shared_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(shared_out, "mtp_ffn_shexp_out", il);
cur = ggml_add(ctx0, routed_out, shared_out);
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_MOE 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_MOE 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_glm4_moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v();
@@ -154,8 +318,7 @@ llama_model_glm4_moe::graph::graph(const llama_model & model, const llm_graph_pa
ggml_tensor * inp_out_ids = build_inp_out_ids();
// Only process up to last layer (skip final NextN layer)
// Final layer tensors are loaded but not processed in forward pass
// NextN layers are processed by graph_mtp.
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
@@ -205,7 +368,7 @@ llama_model_glm4_moe::graph::graph(const llama_model & model, const llm_graph_pa
model.layers[il].wo, NULL, model.layers[il].wo_s,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), 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);
}
@@ -265,6 +428,13 @@ llama_model_glm4_moe::graph::graph(const llama_model & model, const llm_graph_pa
cur = inpL;
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;
src/models/models.h
+4
View File
@@ -1412,6 +1412,10 @@ struct llama_model_glm4_moe : 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;
};