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mimo2: address MTP review feedback (#26228)

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Co-authored-by: tnhnyc <115956684+tnhnyc@users.noreply.github.com>
This commit is contained in:
Tunahan
2026-07-30 05:55:58 +02:00
committed by GitHub
parent 3018a11e79
commit 64d528be72
4 changed files with 191 additions and 22 deletions
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3
gguf-py/gguf/constants.py
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@@ -4440,8 +4440,11 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_EXP_PROBS_B,
MODEL_TENSOR.LAYER_OUT_NORM,
MODEL_TENSOR.NEXTN_EH_PROJ,
MODEL_TENSOR.NEXTN_EMBED_TOKENS,
MODEL_TENSOR.NEXTN_ENORM,
MODEL_TENSOR.NEXTN_HNORM,
MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
],
MODEL_ARCH.STEP35: [
MODEL_TENSOR.TOKEN_EMBD,

4
src/llama-model.cpp
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@@ -2243,7 +2243,9 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
filter = [&](uint32_t il) { return il >= hparams.n_layer(); };
}
if ((arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_HY_V3 || arch == LLM_ARCH_GLM_DSA) && hparams.n_layer_nextn > 0) {
if ((arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_HY_V3 || arch == LLM_ARCH_GLM_DSA ||
arch == LLM_ARCH_MIMO2) &&
hparams.n_layer_nextn > 0) {
if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP) {
filter = [&](uint32_t il) { return il >= hparams.n_layer(); };
} else {

202
src/models/mimo2.cpp
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@@ -25,9 +25,13 @@ void llama_model_mimo2::load_arch_hparams(llama_model_loader & ml) {
}
}
void llama_model_mimo2::load_arch_tensors(llama_model_loader &) {
void llama_model_mimo2::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
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 mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
@@ -40,41 +44,46 @@ void llama_model_mimo2::load_arch_tensors(llama_model_loader &) {
uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
uint32_t n_head = hparams.n_head(i);
// NextN/MTP layers (the last n_nextn blocks) are preserved but disabled pending support
const bool is_nextn = i >= n_layer;
const int skip = is_nextn ? TENSOR_SKIP : 0;
const int flags = is_nextn ? mtp_flags : 0;
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, skip);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_v * n_head, n_embd }, skip);
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, 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, n_embd }, flags);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, skip);
layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, TENSOR_NOT_REQUIRED | skip);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, TENSOR_NOT_REQUIRED | flags);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, skip);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
// non-MoE branch
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | flags);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, TENSOR_NOT_REQUIRED | flags);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | flags);
// MoE branch
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 | skip);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED | flags);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | flags);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED | flags);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | flags);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | flags);
if (is_nextn) {
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, skip);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), {n_embd}, skip);
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), {n_embd}, skip);
layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, skip);
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);
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);
layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | flags);
}
}
}
std::unique_ptr<llm_graph_context> llama_model_mimo2::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);
}
@@ -89,6 +98,8 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
ggml_tensor * inp_out_ids = build_inp_out_ids();
const float v_scale = hparams.f_attn_value_scale;
const bool emit_h_nextn = cparams.embeddings_nextn;
const bool crop_last_layer = inp_out_ids && (!emit_h_nextn || cparams.embeddings_nextn_masked);
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
@@ -168,7 +179,7 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
}
}
if (il == n_layer - 1 && inp_out_ids) {
if (il == n_layer - 1 && crop_last_layer) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
@@ -218,6 +229,15 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
cur = inpL;
if (emit_h_nextn) {
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
if (!cparams.embeddings_nextn_masked && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
}
}
cur = build_norm(cur,
model.output_norm, NULL,
LLM_NORM_RMS, -1);
@@ -233,3 +253,143 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
ggml_build_forward_expand(gf, cur);
}
// Mirrors MiMo's appended NextN block: normalize and fuse token and hidden inputs, run the decoder block,
// expose its pre-head-norm state to the next draft step, then apply the shared output norm and LM head.
// Converted checkpoints may store that shared norm as layer_out_norm, so it remains in the fallback chain.
llama_model_mimo2::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
: llm_graph_context(params) {
GGML_ASSERT(hparams.n_layer_nextn > 0 && "MIMO2 MTP requires n_layer_nextn > 0");
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 && "MIMO2 MTP block missing nextn.eh_proj");
GGML_ASSERT(layer.nextn.enorm && "MIMO2 MTP block missing nextn.enorm");
GGML_ASSERT(layer.nextn.hnorm && "MIMO2 MTP block missing nextn.hnorm");
GGML_ASSERT(layer.wqkv && "MIMO2 MTP requires fused attn_qkv");
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);
const float v_scale = hparams.f_attn_value_scale;
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();
ggml_tensor * inp_out_ids = build_inp_out_ids();
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, 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 * qkv = build_lora_mm(layer.wqkv, cur, layer.wqkv_s);
cb(qkv, "mtp_wqkv", il);
const size_t row_k = ggml_row_size(qkv->type, n_embd_head_k);
const size_t row_v = ggml_row_size(qkv->type, n_embd_head_v);
const size_t row_full = qkv->nb[1];
const size_t k_off = row_k * n_head_l;
const size_t v_off = k_off + row_k * n_head_kv_l;
ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_l, n_tokens, row_k, row_full, 0);
ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_kv_l, n_tokens, row_k, row_full, k_off);
ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, n_embd_head_v, n_head_kv_l, n_tokens, row_v, row_full, v_off);
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, 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, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
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, layer.attn_sinks, nullptr,
1.0f / sqrtf(float(n_embd_head_k)), il);
cb(cur, "mtp_attn_out", il);
if (v_scale) {
cur = ggml_scale(ctx0, cur, v_scale);
cb(cur, "mtp_attn_out_scaled", 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_ASSERT(layer.ffn_gate && layer.ffn_down && layer.ffn_up && "MIMO2 MTP requires dense FFN tensors");
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);
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "mtp_post_ffn", il);
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
? layer.nextn.shared_head_norm
: (layer.layer_out_norm ? layer.layer_out_norm : model.output_norm);
GGML_ASSERT(head_norm_w && "MIMO2 MTP missing head norm fallback");
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_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s;
GGML_ASSERT(head_w && "MIMO2 MTP missing LM head fallback");
cur = build_lora_mm(head_w, cur, head_s);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}

4
src/models/models.h
View File

@@ -2127,6 +2127,10 @@ struct llama_model_mimo2 : 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;
};