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Merged
timkhronos merged 1 commit into from
Jun 28, 2026
Merged

Review driven changes #3

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4 changes: 2 additions & 2 deletions conversion/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1154,7 +1154,7 @@ def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Ca
or "projector." in name or "pre_mm_projector_norm" in name \
or "image_newline" in name or "view_seperator" in name \
or "patch_embed" in name or "patch_embedding" in name \
or "patch_merger." in name or "model.connector." in name:
or "patch_merger." in name or "patch_merge_mlp." in name or "model.connector." in name:
return None

return super().filter_tensors(item)
Expand Down Expand Up @@ -1201,7 +1201,7 @@ def set_gguf_parameters(self):
self.gguf_writer.add_embedding_length(n_embd)
logger.info(f"gguf: embedding length = {n_embd}")

if (n_ff := self.find_hparam(["prefix_dense_intermediate_size", "intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None:
if (n_ff := self.find_hparam(["prefix_dense_intermediate_size", "dense_intermediate_size", "intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None:
self.gguf_writer.add_feed_forward_length(n_ff)
logger.info(f"gguf: feed forward length = {n_ff}")

Expand Down
15 changes: 1 addition & 14 deletions conversion/minimax.py
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Expand Up @@ -62,14 +62,9 @@ class MiniMaxM3Model(TextModel):
_experts_cache: dict[int, dict[str, Tensor]] = {}

def set_gguf_parameters(self):
# dense layers use dense_intermediate_size, experts use intermediate_size. Base
# writes feed_forward_length from intermediate_size, so swap in the dense width
# and emit the expert width separately.
expert_ff = self.find_hparam(["intermediate_size"])
self.hparams["intermediate_size"] = self.find_hparam(["dense_intermediate_size"])
super().set_gguf_parameters()

self.gguf_writer.add_expert_feed_forward_length(expert_ff)
self.gguf_writer.add_expert_feed_forward_length(self.find_hparam(["intermediate_size"]))
self.gguf_writer.add_rope_dimension_count(self.find_hparam(["rotary_dim"]))
self.gguf_writer.add_expert_shared_count(self.find_hparam(["n_shared_experts"]))
self.gguf_writer.add_expert_weights_scale(self.find_hparam(["routed_scaling_factor"]))
Expand Down Expand Up @@ -105,14 +100,6 @@ def set_gguf_parameters(self):
self.gguf_writer.add_leading_dense_block_count(n_dense)

def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
# text-only: drop vision, projector, patch-merge tensors
if name.startswith(("vision_tower", "multi_modal_projector", "patch_merge_mlp")):
return

# strip VL wrapper prefix to match tensor_mapping names
if name.startswith("language_model."):
name = name[len("language_model."):]

# Gemma-style (1 + w) RMSNorm: bake the +1 in so llama.cpp can use plain RMSNorm
if name.endswith("norm.weight"):
data_torch = data_torch + 1.0
Expand Down
11 changes: 11 additions & 0 deletions src/llama-graph.cpp
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Expand Up @@ -1408,6 +1408,17 @@ ggml_tensor * llm_graph_context::build_ffn(
cur = ggml_swiglu(ctx0, cur);
cb(cur, "ffn_swiglu", il);
} break;
case LLM_FFN_SWIGLU_OAI:
if (gate && type_gate == LLM_FFN_PAR) {
//Same constants as LLM_FFN_SWIGLU_OAI_MOE
const float alpha = 1.702f;
const float limit = 7.0f;
cur = ggml_swiglu_oai(ctx0, cur, tmp, alpha, limit);
cb(cur, "ffn_swiglu_oai", il);
type_gate = LLM_FFN_SEQ;
} else {
GGML_ABORT("LLM_FFN_SWIGLU_OAI requires a parallel gate");
} break;
case LLM_FFN_GEGLU:
{
cur = ggml_geglu(ctx0, cur);
Expand Down
1 change: 1 addition & 0 deletions src/llama-graph.h
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Expand Up @@ -45,6 +45,7 @@ enum llm_ffn_op_type : int {
LLM_FFN_SWIGLU,
LLM_FFN_GEGLU,
LLM_FFN_REGLU,
LLM_FFN_SWIGLU_OAI,
LLM_FFN_SWIGLU_OAI_MOE,
};

Expand Down
1 change: 1 addition & 0 deletions src/llama-model.cpp
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Expand Up @@ -799,6 +799,7 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_122B_A10B: return "122B.A10B";
case LLM_TYPE_196B_A11B: return "196B.A11B";
case LLM_TYPE_230B_A10B: return "230B.A10B";
case LLM_TYPE_428B_A23B: return "428B.A23B";
case LLM_TYPE_235B_A22B: return "235B.A22B";
case LLM_TYPE_300B_A47B: return "300B.A47B";
case LLM_TYPE_310B_A15B: return "310B.A15B";
Expand Down
1 change: 1 addition & 0 deletions src/llama-model.h
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Expand Up @@ -133,6 +133,7 @@ enum llm_type {
LLM_TYPE_122B_A10B, // Qwen3.5
LLM_TYPE_196B_A11B, // Step3.5-Flash
LLM_TYPE_230B_A10B, // Minimax M2
LLM_TYPE_428B_A23B, // Minimax M3
LLM_TYPE_235B_A22B,
LLM_TYPE_300B_A47B, // Ernie MoE big
LLM_TYPE_310B_A15B, // /MiMo-V2-Flash
Expand Down
43 changes: 19 additions & 24 deletions src/models/minimax-m3.cpp
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Expand Up @@ -16,15 +16,18 @@ void llama_model_minimax_m3::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head, false);
ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size, false);
ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k, false);
ml.get_key(LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE, hparams.indexer_block_size, false);
ml.get_key(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, hparams.indexer_local_blocks, false);
msa_p = { (int) hparams.indexer_block_size, (int) hparams.indexer_top_k, (int) hparams.indexer_local_blocks };

type = LLM_TYPE_UNKNOWN;
switch (hparams.n_layer()) {
case 60: type = LLM_TYPE_428B_A23B; break;
default: type = LLM_TYPE_UNKNOWN;
}
}

void llama_model_minimax_m3::load_arch_tensors(llama_model_loader &) {
Expand Down Expand Up @@ -389,10 +392,6 @@ llama_model_minimax_m3::graph::graph(const llama_model & model, const llm_graph_
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
// partial rotary: head_dim != n_rot, so don't assert n_embd_head == n_rot

// swigluoai params, shared by dense and expert FFNs
const float swiglu_alpha = 1.702f;
const float swiglu_limit = 7.0f;

ggml_tensor * cur;
ggml_tensor * inpL;

Expand Down Expand Up @@ -452,16 +451,8 @@ llama_model_minimax_m3::graph::graph(const llama_model & model, const llm_graph_
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);

ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head, n_head, n_head_kv, il);

// per-head QK RMSNorm (weights already include Gemma's +1)
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
Expand Down Expand Up @@ -574,10 +565,12 @@ llama_model_minimax_m3::graph::graph(const llama_model & model, const llm_graph_

if ((uint32_t) il < hparams.n_layer_dense_lead) {
// leading dense FFN (swigluoai)
ggml_tensor * g = build_lora_mm(model.layers[il].ffn_gate, cur);
ggml_tensor * u = build_lora_mm(model.layers[il].ffn_up, cur);
g = ggml_swiglu_oai(ctx0, g, u, swiglu_alpha, swiglu_limit);
cur = build_lora_mm(model.layers[il].ffn_down, g);
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_SWIGLU_OAI, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
} else {
// routed experts (swigluoai MoE)
Expand All @@ -595,10 +588,12 @@ llama_model_minimax_m3::graph::graph(const llama_model & model, const llm_graph_
cb(moe_out, "ffn_moe_out", il);

// shared expert (swigluoai)
ggml_tensor * sg = build_lora_mm(model.layers[il].ffn_gate_shexp, cur);
ggml_tensor * su = build_lora_mm(model.layers[il].ffn_up_shexp, cur);
sg = ggml_swiglu_oai(ctx0, sg, su, swiglu_alpha, swiglu_limit);
ggml_tensor * ffn_shexp = build_lora_mm(model.layers[il].ffn_down_shexp, sg);
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_SWIGLU_OAI, LLM_FFN_PAR, il);
cb(ffn_shexp, "ffn_shexp", il);

cur = ggml_add(ctx0, moe_out, ffn_shexp);
Expand Down
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