@@ -1436,6 +1436,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
14361436 ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);
14371437 } break;
14381438 case LLM_ARCH_BAMBA:
1439+ case LLM_ARCH_GRANITE_MOE_HYBRID:
14391440 {
14401441 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
14411442 ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale, /* required */ false);
@@ -1477,6 +1478,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
14771478 // TODO: Add llm type label (not sure this is useful)
14781479 default: type = LLM_TYPE_UNKNOWN;
14791480 }
1481+
1482+ // For Granite MoE Shared
1483+ ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);
14801484 } break;
14811485 case LLM_ARCH_CHAMELEON:
14821486 {
@@ -3089,6 +3093,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
30893093 }
30903094 } break;
30913095 case LLM_ARCH_BAMBA:
3096+ case LLM_ARCH_GRANITE_MOE_HYBRID:
30923097 {
30933098 // mamba2 Mixer SSM params
30943099 // NOTE: int64_t for tensor dimensions
@@ -3155,14 +3160,31 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
31553160 }
31563161
31573162 // feed forward (w/ optional biases)
3158- layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
3159- layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
3160- layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
3161- layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
3162- layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
3163- layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
3164- layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
3165- layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
3163+ if (n_expert > 0) {
3164+ // MoE FFN
3165+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
3166+ layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
3167+ layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
3168+ layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, TENSOR_NOT_REQUIRED);
3169+ layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0);
3170+ layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
3171+
3172+ // For Granite MoE Shared
3173+ if (hparams.n_ff_shexp > 0) {
3174+ layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
3175+ layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
3176+ layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
3177+ }
3178+ } else {
3179+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
3180+ layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
3181+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
3182+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
3183+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
3184+ layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
3185+ layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
3186+ layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
3187+ }
31663188 }
31673189 } break;
31683190 case LLM_ARCH_XVERSE:
@@ -4611,7 +4633,9 @@ void llama_model::print_info() const {
46114633
46124634 if (arch == LLM_ARCH_MINICPM ||
46134635 arch == LLM_ARCH_GRANITE ||
4614- arch == LLM_ARCH_GRANITE_MOE) {
4636+ arch == LLM_ARCH_GRANITE_MOE ||
4637+ arch == LLM_ARCH_GRANITE_MOE_HYBRID ||
4638+ arch == LLM_ARCH_BAMBA) {
46154639 LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
46164640 LLAMA_LOG_INFO("%s: f_residual_scale = %f\n", __func__, hparams.f_residual_scale);
46174641 LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
@@ -14042,6 +14066,12 @@ llm_graph_result_ptr llama_model::build_graph(
1404214066 {
1404314067 llm = std::make_unique<llm_build_granite>(*this, params, gf);
1404414068 } break;
14069+ case LLM_ARCH_GRANITE_MOE_HYBRID:
14070+ {
14071+ llm = std::make_unique<llm_build_hybrid_mamba>(*this, params, gf,
14072+ /* use_mamba2 */ true,
14073+ /* use_rope */ false);
14074+ } break;
1404514075 case LLM_ARCH_BAMBA:
1404614076 {
1404714077 llm = std::make_unique<llm_build_hybrid_mamba>(
@@ -14197,6 +14227,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
1419714227 case LLM_ARCH_GLM4:
1419814228 case LLM_ARCH_GRANITE:
1419914229 case LLM_ARCH_GRANITE_MOE:
14230+ case LLM_ARCH_GRANITE_MOE_HYBRID:
1420014231 case LLM_ARCH_BAMBA:
1420114232 case LLM_ARCH_CHAMELEON:
1420214233 case LLM_ARCH_BAILINGMOE:
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