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Add preliminary MiniMax-M3 support

This commit is contained in:
Jun Yamog 2026-06-14 12:23:20 +00:00
parent 670a3f6f5b
commit 0df00b3b94
10 changed files with 226 additions and 2 deletions
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1
src/CMakeLists.txt
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@ -120,6 +120,7 @@ add_library(llama
graphs/build_openai.cpp
graphs/build_bailingmoe2.cpp
graphs/build_minimaxm2.cpp
graphs/build_minimaxm3.cpp
graphs/build_smollm3.cpp
)

132
src/graphs/build_minimaxm3.cpp Normal file
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@ -0,0 +1,132 @@
#include "../llama-build-context.h"
#include "../llama-model.h"
#include "../llama-context.h"
ggml_cgraph* llm_build_context::build_minimaxm3() {
ggml_cgraph * gf = new_graph_custom();
const int64_t n_embd_head = hparams.n_embd_head_v(0);
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k(0));
constexpr float swiglu_alpha = 1.702f;
constexpr float swiglu_limit = 7.0f;
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
GGML_ASSERT(model.split_mode != LLAMA_SPLIT_MODE_GRAPH && model.split_mode != LLAMA_SPLIT_MODE_ATTN);
ggml_tensor * inpSA = inpL;
cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, 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);
Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, cb, il);
cb(Qcur, "Qcur_normed", il);
Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, cb, il);
cb(Kcur, "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, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
cur = llm_build_kv(ctx0, lctx, kv_self, gf,
model.layers[il].wo, NULL,
Kcur, Vcur, Qcur, KQ_mask,
n_tokens, kv_head, n_kv,
1.0f / sqrtf(float(n_embd_head)), cb, il);
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
cur = llm_build_norm(ctx0, ffn_inp, hparams, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
if ((uint32_t) il < hparams.n_layer_dense_lead) {
ggml_tensor * gate = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_gate, cur);
cb(gate, "ffn_gate", il);
ggml_tensor * up = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_up, cur);
cb(up, "ffn_up", il);
gate = ggml_swiglu_oai(ctx0, gate, up, swiglu_alpha, swiglu_limit);
cb(gate, "ffn_gate_par", il);
cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_down, gate);
cb(cur, "ffn_down", il);
} else {
ggml_tensor * moe_out = llm_build_moe_ffn(ctx0, lctx, cur,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
model.layers[il].ffn_gate_exps,
model.layers[il].ffn_down_exps,
model.layers[il].ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_SWIGLU_OAI_MOE,
hparams.expert_weights_norm,
hparams.expert_weights_scale != 0.0f, hparams.expert_weights_scale,
(llm_expert_gating_func_type) hparams.expert_gating_func,
cb, il, gf);
cb(moe_out, "ffn_moe_out", il);
ggml_tensor * gate = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_gate_shexp, cur);
cb(gate, "ffn_shexp_gate", il);
ggml_tensor * up = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_up_shexp, cur);
cb(up, "ffn_shexp_up", il);
gate = ggml_swiglu_oai(ctx0, gate, up, swiglu_alpha, swiglu_limit);
cb(gate, "ffn_shexp_gate_par", il);
ggml_tensor * ffn_shexp = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_down_shexp, gate);
cb(ffn_shexp, "ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cur = lctx.cvec.apply_to(ctx0, cur, il);
cb(cur, "l_out", il);
inpL = cur;
}
cur = build_output(lctx, ctx0, inpL, model.output, model.output_norm, cb);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}

1
src/llama-arch.cpp
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@ -72,6 +72,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_OPENAI_MOE, "gpt-oss" },
{ LLM_ARCH_BAILINGMOE2, "bailingmoe2" },
{ LLM_ARCH_MINIMAX_M2, "minimax-m2" },
{ LLM_ARCH_MINIMAX_M3, "minimax-m3" },
{ LLM_ARCH_SMOLLM3, "smollm3" },
{ LLM_ARCH_MISTRAL3, "mistral3" },
{ LLM_ARCH_MIMO2, "mimo2" },

1
src/llama-arch.h
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@ -71,6 +71,7 @@ enum llm_arch {
LLM_ARCH_OPENAI_MOE,
LLM_ARCH_BAILINGMOE2,
LLM_ARCH_MINIMAX_M2,
LLM_ARCH_MINIMAX_M3,
LLM_ARCH_SMOLLM3,
LLM_ARCH_MISTRAL3,
LLM_ARCH_MIMO2,

8
src/llama-build-context.cpp
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@ -1149,7 +1149,7 @@ llm_expert_gating_func_type gating_op,
ggml_tensor * par;
if (can_use_fmoe && up_gate_exps) {
if (up_gate_exps_b) {
if (up_gate_exps_b || type_op == LLM_FFN_SWIGLU_OAI_MOE) {
par = ggml_moe_up_gate_ext(ctx, up_gate_exps, nullptr, cur, selected_experts, up_gate_exps_b, nullptr,
type_op == LLM_FFN_SILU ? GGML_UNARY_OP_SILU :
type_op == LLM_FFN_GELU ? GGML_UNARY_OP_GELU : GGML_UNARY_OP_SWIGLU_OAI);
@ -1165,7 +1165,7 @@ llm_expert_gating_func_type gating_op,
GGML_ASSERT(!up_gate_exps && !up_gate_exps_b);
if (can_use_fmoe && lctx.cparams.fused_moe_up_gate && up_exps->type == gate_exps->type) {
if (up_exps_b || gate_exps_b) {
if (up_exps_b || gate_exps_b || type_op == LLM_FFN_SWIGLU_OAI_MOE) {
par = ggml_moe_up_gate_ext(ctx, up_exps, gate_exps, cur, selected_experts, up_exps_b, gate_exps_b,
type_op == LLM_FFN_SILU ? GGML_UNARY_OP_SILU :
type_op == LLM_FFN_GELU ? GGML_UNARY_OP_GELU : GGML_UNARY_OP_SWIGLU_OAI);
@ -2520,6 +2520,10 @@ ggml_cgraph * llm_build_context::llama_build_graph(
{
result = llm.build_minimaxm2();
} break;
case LLM_ARCH_MINIMAX_M3:
{
result = llm.build_minimaxm3();
} break;
case LLM_ARCH_SMOLLM3:
{
result = llm.build_smollm3();

1
src/llama-build-context.h
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@ -312,6 +312,7 @@ struct llm_build_context {
ggml_cgraph * build_bailingmoe2();
ggml_cgraph * build_minimaxm2();
ggml_cgraph * build_minimaxm3();
ggml_cgraph * build_smollm3();

12
src/llama-hparams.cpp
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@ -1280,6 +1280,18 @@ void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_MINIMAX_M3:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
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);
model.type = e_model::MODEL_UNKNOWN;
} break;
case LLM_ARCH_SMOLLM3:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

43
src/llama-load-tensors.cpp
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@ -150,6 +150,7 @@ struct create_tensors_helper : public create_tensors_helper_interface {
bool create_bailingmoe2_tensors(const LLM_TN & tn);
bool create_minimaxm2_tensors(const LLM_TN & tn);
bool create_minimaxm3_tensors(const LLM_TN & tn);
bool create_smollm3_tensors(const LLM_TN & tn);
@ -3601,6 +3602,46 @@ bool create_tensors_helper::create_minimaxm2_tensors(const LLM_TN & tn) {
return use_mmap_buffer;
}
bool create_tensors_helper::create_minimaxm3_tensors(const LLM_TN & tn) {
LOADING_PRELUDE
const int64_t n_expert_shared = hparams.n_expert_shared;
const int64_t n_ff_exp = hparams.n_ff_exp;
create_embd_output(tn, n_embd, n_vocab);
for (int i = 0; i < n_layer; ++i) {
ggml_context* ctx_split = ctx_for_layer_split(i);
auto& layer = model.layers[i];
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_gqa }, 0);
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
layer.attn_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
layer.attn_q_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
layer.attn_k_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
layer.ffn_norm = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
if (i < (int) hparams.n_layer_dense_lead) {
layer.ffn_gate = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
} else {
layer.ffn_gate_inp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
layer.ffn_exp_probs_b = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), { n_expert }, 0);
use_mmap_buffer &= !create_std_ffn_exps(n_embd, tn, i, 0, n_ff_exp);
layer.ffn_gate_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_exp * n_expert_shared }, 0);
layer.ffn_down_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd }, 0);
layer.ffn_up_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_exp * n_expert_shared }, 0);
}
}
return use_mmap_buffer;
}
bool create_tensors_helper::create_smollm3_tensors(const LLM_TN & tn) {
LOADING_PRELUDE
@ -4412,6 +4453,8 @@ bool create_tensors_helper::create_tensors() {
use_mmap_buffer = create_bailingmoe2_tensors(tn); break;
case LLM_ARCH_MINIMAX_M2:
use_mmap_buffer = create_minimaxm2_tensors(tn); break;
case LLM_ARCH_MINIMAX_M3:
use_mmap_buffer = create_minimaxm3_tensors(tn); break;
case LLM_ARCH_SMOLLM3:
use_mmap_buffer = create_smollm3_tensors(tn); break;
case LLM_ARCH_MIMO2:

27
src/llama-model.cpp
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@ -1532,6 +1532,33 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" },
},
},
{
LLM_ARCH_MINIMAX_M3,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
{ LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" },
{ LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
{ LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
},
},
{
LLM_ARCH_SMOLLM3,
{

2
src/llama.cpp
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@ -3071,6 +3071,7 @@ static bool is_model_split_supported(const llama_model & model) {
LLM_ARCH_OPENAI_MOE,
LLM_ARCH_ERNIE4_5_MOE,
LLM_ARCH_MINIMAX_M2,
LLM_ARCH_MINIMAX_M3,
LLM_ARCH_SEED_OSS,
LLM_ARCH_STEP35,
LLM_ARCH_LAGUNA,
@ -7439,6 +7440,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_OPENAI_MOE:
case LLM_ARCH_BAILINGMOE2:
case LLM_ARCH_MINIMAX_M2:
case LLM_ARCH_MINIMAX_M3:
case LLM_ARCH_MIMO2:
case LLM_ARCH_SEED_OSS:
case LLM_ARCH_STEP35: