model : add GroveMoE support (#15510)

* add GroveMoE support

* remove constexpr that fails on certain compilers

* revert crude scalar div implementation, use cast

* build_attn_inp_kv_unified -> build_attn_inp_kv

* fix build_attn

* re-apply ffn_exps regex changes

common/common.h

@@ -738,7 +738,7 @@ const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
 // MoE utils
 //
 
-const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate)_exps";
+const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate)_(ch|)exps";
 
 static std::string llm_ffn_exps_block_regex(int idx) {
     return string_format("blk\\.%d%s", idx, LLM_FFN_EXPS_REGEX);

convert_hf_to_gguf.py

@@ -7995,6 +7995,121 @@ class BailingMoeModel(TextModel):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@ModelBase.register("GroveMoeForCausalLM", "modeling_grove_moe.GroveMoeForCausalLM")
+class GroveMoeModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.GROVEMOE
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if (n_experts := self.hparams.get("num_experts")) is not None:
+            self.gguf_writer.add_expert_count(n_experts)
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
+        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L299
+        self.gguf_writer.add_expert_chunk_feed_forward_length(self.hparams.get("head_dim") or 128)
+        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L298
+        self.gguf_writer.add_experts_per_group(2)
+        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L376
+        self.gguf_writer.add_expert_group_scale(0.05)
+        # YaRN is not enabled by default
+        # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts
+        rope_scaling = self.hparams.get("rope_scaling") or {}
+        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
+            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
+
+    _experts: list[dict[str, Tensor]] | None = None
+    _chunk_experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.endswith(".expert_bias"):
+            # FIXME?: Unused https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L303
+            return []
+
+        # process the experts separately
+        if name.find("chunk_experts") != -1:
+            n_experts = self.hparams["num_experts"] // 2 # see add_experts_per_group
+            assert bid is not None
+
+            if self._chunk_experts is None:
+                self._chunk_experts = [{} for _ in range(self.block_count)]
+
+            self._chunk_experts[bid][name] = data_torch
+
+            if len(self._chunk_experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.chunk_experts.{xid}.{w_name}.weight"
+                        datas.append(self._chunk_experts[bid][ename])
+                        del self._chunk_experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.chunk_experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+        elif name.find("experts") != -1:
+            n_experts = self.hparams["num_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._chunk_experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            chunk_experts = [k for d in self._chunk_experts for k in d.keys()]
+            if len(chunk_experts) > 0:
+                raise ValueError(f"Unprocessed adjugate experts: {chunk_experts}")
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
 @ModelBase.register("ChameleonForConditionalGeneration")
 @ModelBase.register("ChameleonForCausalLM")  # obsolete
 class ChameleonModel(TextModel):

gguf-py/gguf/constants.py

@@ -96,6 +96,7 @@ class Keys:
         FEED_FORWARD_LENGTH               = "{arch}.feed_forward_length"
         EXPERT_FEED_FORWARD_LENGTH        = "{arch}.expert_feed_forward_length"
         EXPERT_SHARED_FEED_FORWARD_LENGTH = "{arch}.expert_shared_feed_forward_length"
+        EXPERT_CHUNK_FEED_FORWARD_LENGTH  = "{arch}.expert_chunk_feed_forward_length"
         USE_PARALLEL_RESIDUAL             = "{arch}.use_parallel_residual"
         TENSOR_DATA_LAYOUT                = "{arch}.tensor_data_layout"
         EXPERT_COUNT                      = "{arch}.expert_count"
@@ -104,6 +105,8 @@ class Keys:
         EXPERT_WEIGHTS_SCALE              = "{arch}.expert_weights_scale"
         EXPERT_WEIGHTS_NORM               = "{arch}.expert_weights_norm"
         EXPERT_GATING_FUNC                = "{arch}.expert_gating_func"
+        EXPERT_GROUP_SCALE                = "{arch}.expert_group_scale"
+        EXPERTS_PER_GROUP                 = "{arch}.experts_per_group"
         MOE_EVERY_N_LAYERS                = "{arch}.moe_every_n_layers"
         NEXTN_PREDICT_LAYERS              = "{arch}.nextn_predict_layers"
         POOLING_TYPE                      = "{arch}.pooling_type"
@@ -401,6 +404,7 @@ class MODEL_ARCH(IntEnum):
     LLADA            = auto()
     LLADA_MOE        = auto()
     SEED_OSS         = auto()
+    GROVEMOE         = auto()
 
 
 class VISION_PROJECTOR_TYPE(IntEnum):
@@ -450,6 +454,9 @@ class MODEL_TENSOR(IntEnum):
     FFN_GATE_SHEXP       = auto()
     FFN_DOWN_SHEXP       = auto()
     FFN_UP_SHEXP         = auto()
+    FFN_GATE_CHEXP       = auto()
+    FFN_DOWN_CHEXP       = auto()
+    FFN_UP_CHEXP         = auto()
     FFN_EXP_PROBS_B      = auto()
     ATTN_Q_NORM          = auto()
     ATTN_K_NORM          = auto()
@@ -738,6 +745,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.LLADA:            "llada",
     MODEL_ARCH.LLADA_MOE:        "llada-moe",
     MODEL_ARCH.SEED_OSS:         "seed_oss",
+    MODEL_ARCH.GROVEMOE:         "grovemoe",
 }
 
 VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
@@ -784,6 +792,9 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.FFN_GATE_SHEXP:            "blk.{bid}.ffn_gate_shexp",
     MODEL_TENSOR.FFN_DOWN_SHEXP:            "blk.{bid}.ffn_down_shexp",
     MODEL_TENSOR.FFN_UP_SHEXP:              "blk.{bid}.ffn_up_shexp",
+    MODEL_TENSOR.FFN_GATE_CHEXP:            "blk.{bid}.ffn_gate_chexps",
+    MODEL_TENSOR.FFN_DOWN_CHEXP:            "blk.{bid}.ffn_down_chexps",
+    MODEL_TENSOR.FFN_UP_CHEXP:              "blk.{bid}.ffn_up_chexps",
     MODEL_TENSOR.FFN_ACT:                   "blk.{bid}.ffn",
     MODEL_TENSOR.FFN_NORM_EXP:              "blk.{bid}.ffn_norm_exps",
     MODEL_TENSOR.FFN_GATE_EXP:              "blk.{bid}.ffn_gate_exps",
@@ -2712,6 +2723,26 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_UP_EXP,
         MODEL_TENSOR.FFN_DOWN_EXP,
     ],
+    MODEL_ARCH.GROVEMOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_CHEXP,
+        MODEL_TENSOR.FFN_DOWN_CHEXP,
+        MODEL_TENSOR.FFN_UP_CHEXP,
+    ],
     # TODO
 }
 

gguf-py/gguf/gguf_writer.py

@@ -670,6 +670,9 @@ class GGUFWriter:
     def add_expert_shared_feed_forward_length(self, length: int) -> None:
         self.add_uint32(Keys.LLM.EXPERT_SHARED_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
 
+    def add_expert_chunk_feed_forward_length(self, length: int) -> None:
+        self.add_uint32(Keys.LLM.EXPERT_CHUNK_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
+
     def add_parallel_residual(self, use: bool) -> None:
         self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
 
@@ -757,6 +760,12 @@ class GGUFWriter:
     def add_expert_gating_func(self, value: ExpertGatingFuncType) -> None:
         self.add_uint32(Keys.LLM.EXPERT_GATING_FUNC.format(arch=self.arch), value.value)
 
+    def add_expert_group_scale(self, value: float) -> None:
+        self.add_float32(Keys.LLM.EXPERT_GROUP_SCALE.format(arch=self.arch), value)
+
+    def add_experts_per_group(self, count: int) -> None:
+        self.add_uint32(Keys.LLM.EXPERTS_PER_GROUP.format(arch=self.arch), count)
+
     def add_moe_every_n_layers(self, value: int) -> None:
         self.add_uint32(Keys.LLM.MOE_EVERY_N_LAYERS.format(arch=self.arch), value)
 

gguf-py/gguf/tensor_mapping.py

@@ -427,6 +427,10 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.shared_mlp.up_proj",             # hunyuan
         ),
 
+        MODEL_TENSOR.FFN_UP_CHEXP: (
+            "model.layers.{bid}.mlp.chunk_experts.up_proj",           # grovemoe
+        ),
+
         # AWQ-activation gate
         MODEL_TENSOR.FFN_ACT: (
             "transformer.blocks.{bid}.ffn.act",  # mpt
@@ -468,6 +472,10 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.shared_mlp.gate_proj",             # hunyuan
         ),
 
+        MODEL_TENSOR.FFN_GATE_CHEXP: (
+            "model.layers.{bid}.mlp.chunk_experts.gate_proj",           # grovemoe
+        ),
+
         # Feed-forward down
         MODEL_TENSOR.FFN_DOWN: (
             "gpt_neox.layers.{bid}.mlp.dense_4h_to_h",                # gptneox
@@ -524,6 +532,10 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.shared_mlp.down_proj",             # hunyuan
         ),
 
+        MODEL_TENSOR.FFN_DOWN_CHEXP: (
+            "model.layers.{bid}.mlp.chunk_experts.down_proj",           # grovemoe
+        ),
+
         MODEL_TENSOR.ATTN_Q_NORM: (
             "language_model.encoder.layers.{bid}.self_attention.q_layernorm",
             "model.layers.{bid}.self_attn.q_layernorm",                       # persimmon

src/llama-arch.cpp

@@ -98,6 +98,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_LLADA,            "llada"            },
     { LLM_ARCH_LLADA_MOE,        "llada-moe"        },
     { LLM_ARCH_SEED_OSS,         "seed_oss"         },
+    { LLM_ARCH_GROVEMOE,         "grovemoe"         },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
 
@@ -125,6 +126,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_FEED_FORWARD_LENGTH,               "%s.feed_forward_length"               },
     { LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        "%s.expert_feed_forward_length"        },
     { LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, "%s.expert_shared_feed_forward_length" },
+    { LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH,  "%s.expert_chunk_feed_forward_length"  },
     { LLM_KV_USE_PARALLEL_RESIDUAL,             "%s.use_parallel_residual"             },
     { LLM_KV_TENSOR_DATA_LAYOUT,                "%s.tensor_data_layout"                },
     { LLM_KV_EXPERT_COUNT,                      "%s.expert_count"                      },
@@ -133,6 +135,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_EXPERT_WEIGHTS_SCALE,              "%s.expert_weights_scale"              },
     { LLM_KV_EXPERT_WEIGHTS_NORM,               "%s.expert_weights_norm"               },
     { LLM_KV_EXPERT_GATING_FUNC,                "%s.expert_gating_func"                },
+    { LLM_KV_EXPERT_GROUP_SCALE,                "%s.expert_group_scale"                },
+    { LLM_KV_EXPERTS_PER_GROUP,                 "%s.experts_per_group"                 },
     { LLM_KV_MOE_EVERY_N_LAYERS,                "%s.moe_every_n_layers"                },
     { LLM_KV_NEXTN_PREDICT_LAYERS,              "%s.nextn_predict_layers"              },
     { LLM_KV_POOLING_TYPE,                      "%s.pooling_type"                      },
@@ -2186,6 +2190,29 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_GROVEMOE,
+        {
+            { 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_Q_NORM,        "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { 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_GATE_CHEXPS,    "blk.%d.ffn_gate_chexps" },
+            { LLM_TENSOR_FFN_DOWN_CHEXPS,    "blk.%d.ffn_down_chexps" },
+            { LLM_TENSOR_FFN_UP_CHEXPS,      "blk.%d.ffn_up_chexps" },
+        },
+    },
     {
         LLM_ARCH_UNKNOWN,
         {
@@ -2318,6 +2345,9 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_FFN_DOWN_EXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
     {LLM_TENSOR_FFN_GATE_EXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
     {LLM_TENSOR_FFN_UP_EXPS,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
+    {LLM_TENSOR_FFN_DOWN_CHEXPS,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
+    {LLM_TENSOR_FFN_GATE_CHEXPS,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
+    {LLM_TENSOR_FFN_UP_CHEXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
     {LLM_TENSOR_FFN_EXP_PROBS_B,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
     // altup / laurel (gemma 3n)
     {LLM_TENSOR_PER_LAYER_TOKEN_EMBD,       {LLM_TENSOR_LAYER_OUTPUT,    GGML_OP_GET_ROWS}},

src/llama-arch.h

@@ -102,6 +102,7 @@ enum llm_arch {
     LLM_ARCH_LLADA,
     LLM_ARCH_LLADA_MOE,
     LLM_ARCH_SEED_OSS,
+    LLM_ARCH_GROVEMOE,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -129,6 +130,7 @@ enum llm_kv {
     LLM_KV_FEED_FORWARD_LENGTH,
     LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
     LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH,
+    LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH,
     LLM_KV_USE_PARALLEL_RESIDUAL,
     LLM_KV_TENSOR_DATA_LAYOUT,
     LLM_KV_EXPERT_COUNT,
@@ -137,6 +139,8 @@ enum llm_kv {
     LLM_KV_EXPERT_WEIGHTS_SCALE,
     LLM_KV_EXPERT_WEIGHTS_NORM,
     LLM_KV_EXPERT_GATING_FUNC,
+    LLM_KV_EXPERT_GROUP_SCALE,
+    LLM_KV_EXPERTS_PER_GROUP,
     LLM_KV_MOE_EVERY_N_LAYERS,
     LLM_KV_NEXTN_PREDICT_LAYERS,
     LLM_KV_POOLING_TYPE,
@@ -301,6 +305,9 @@ enum llm_tensor {
     LLM_TENSOR_FFN_DOWN_SHEXP,
     LLM_TENSOR_FFN_GATE_SHEXP,
     LLM_TENSOR_FFN_UP_SHEXP,
+    LLM_TENSOR_FFN_DOWN_CHEXPS,
+    LLM_TENSOR_FFN_GATE_CHEXPS,
+    LLM_TENSOR_FFN_UP_CHEXPS,
     LLM_TENSOR_FFN_EXP_PROBS_B,
     LLM_TENSOR_ATTN_Q_NORM,
     LLM_TENSOR_ATTN_K_NORM,

src/llama-graph.cpp

@@ -923,13 +923,26 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
         selection_probs = logits;
     }
 
+    if (arch == LLM_ARCH_GROVEMOE) {
+        selection_probs = ggml_sigmoid(ctx0, logits); // [n_expert, n_tokens]
+        cb(selection_probs, "ffn_moe_probs_biased", il);
+    }
+
     // select experts
     ggml_tensor * selected_experts = ggml_top_k(ctx0, selection_probs, n_expert_used); // [n_expert_used, n_tokens]
     cb(selected_experts->src[0], "ffn_moe_argsort", il);
     cb(selected_experts, "ffn_moe_topk", il);
 
-    ggml_tensor * weights = ggml_get_rows(ctx0,
-            ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens]
+    if (arch == LLM_ARCH_GROVEMOE && n_expert != hparams.n_expert) {
+        // TODO: Use scalar div instead when/if implemented
+        ggml_tensor * f_sel = ggml_cast(ctx0, selected_experts, GGML_TYPE_F32);
+        selected_experts = ggml_cast(ctx0, ggml_scale(ctx0, f_sel, 1.0f / float(hparams.n_group_experts)), GGML_TYPE_I32);
+        probs = ggml_reshape_3d(ctx0, probs, 1, hparams.n_expert, n_tokens);
+    } else {
+        probs = ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens);
+    }
+
+    ggml_tensor * weights = ggml_get_rows(ctx0, probs, selected_experts); // [1, n_expert_used, n_tokens]
     cb(weights, "ffn_moe_weights", il);
 
 

src/llama-hparams.h

@@ -69,10 +69,13 @@ struct llama_hparams {
     uint32_t n_lora_kv          = 0;
     uint32_t n_ff_exp           = 0;
     uint32_t n_ff_shexp         = 0;
+    uint32_t n_ff_chexp         = 0;
     uint32_t n_expert_shared    = 0;
     uint32_t n_norm_groups      = 0;
+    uint32_t n_group_experts    = 0;
 
-    float    expert_weights_scale = 0.0;
+    float    expert_group_scale   = 0.05f;
+    float    expert_weights_scale = 0.0f;
     bool     expert_weights_norm  = false;
     uint32_t expert_gating_func   = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE;
     uint32_t moe_every_n_layers   = 0;

src/llama-model.cpp

@@ -2009,6 +2009,19 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_GROVEMOE:
+            {
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH,  hparams.n_ff_chexp);
+                ml.get_key(LLM_KV_EXPERT_GROUP_SCALE,                hparams.expert_group_scale);
+                ml.get_key(LLM_KV_EXPERTS_PER_GROUP,                 hparams.n_group_experts);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 48: type = LLM_TYPE_30B_A3B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         default: throw std::runtime_error("unsupported model architecture");
     }
 
@@ -5840,6 +5853,53 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
                     }
                 } break;
+            case LLM_ARCH_GROVEMOE:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for GROVEMOE");
+                    GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for GROVEMOE");
+                    GGML_ASSERT(hparams.n_group_experts > 0 && "n_group_experts must be > 0 for GROVEMOE");
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+                        // MoE branch
+                        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+                        const int64_t n_ff_chexp = hparams.n_ff_chexp ? hparams.n_ff_chexp : n_embd_head_k;
+                        const int64_t n_chunk_expert = n_expert / hparams.n_group_experts;
+
+                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+
+                        layer.ffn_gate_chexps = create_tensor(tn(LLM_TENSOR_FFN_GATE_CHEXPS, "weight", i), {  n_embd, n_ff_chexp, n_chunk_expert}, 0);
+                        layer.ffn_down_chexps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_CHEXPS, "weight", i), {n_ff_chexp,   n_embd, n_chunk_expert}, 0);
+                        layer.ffn_up_chexps   = create_tensor(tn(LLM_TENSOR_FFN_UP_CHEXPS,   "weight", i), {  n_embd, n_ff_chexp, n_chunk_expert}, 0);
+                    }
+                } break;
             default:
                 throw std::runtime_error("unknown architecture");
         }
@@ -6179,6 +6239,13 @@ void llama_model::print_info() const {
         LLAMA_LOG_INFO("%s: expert_gating_func   = %s\n",     __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
     }
 
+    if (arch == LLM_ARCH_GROVEMOE) {
+        LLAMA_LOG_INFO("%s: n_ff_exp             = %d\n",     __func__, hparams.n_ff_exp);
+        LLAMA_LOG_INFO("%s: n_ff_chexp           = %d\n",     __func__, hparams.n_ff_chexp);
+        LLAMA_LOG_INFO("%s: n_group_experts      = %d\n",     __func__, hparams.n_group_experts);
+        LLAMA_LOG_INFO("%s: expert_group_scale   = %.2f\n",   __func__, hparams.expert_group_scale);
+    }
+
     vocab.print_info();
 }
 
@@ -18864,6 +18931,156 @@ struct llm_build_smallthinker : public llm_graph_context{
     }
 };
 
+struct llm_build_grovemoe : public llm_graph_context {
+    llm_build_grovemoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_chunk_expert = n_expert / hparams.n_group_experts;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self_attention
+            {
+                // compute Q and K and RoPE them
+                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);
+
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_normed", il);
+
+                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 = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), 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);
+
+            // MoE branch
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            ggml_tensor * probs = build_lora_mm(model.layers[il].ffn_gate_inp, cur); // [n_expert, n_tokens]
+            cb(probs, "ffn_moe_logits", il);
+
+            ggml_tensor * moe_out =
+                build_moe_ffn(cur,
+                        nullptr,
+                        model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps,
+                        model.layers[il].ffn_down_exps,
+                        nullptr,
+                        n_expert, n_expert_used,
+                        LLM_FFN_SILU, true,
+                        false, 0.0,
+                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                        il, probs);
+            cb(moe_out, "ffn_moe_out", il);
+            cur = moe_out;
+
+            // TODO: Only do the expert selection and weights once
+            moe_out =
+                build_moe_ffn(cur,
+                        nullptr,
+                        model.layers[il].ffn_up_chexps,
+                        model.layers[il].ffn_gate_chexps,
+                        model.layers[il].ffn_down_chexps,
+                        nullptr,
+                        n_chunk_expert, n_expert_used > n_chunk_expert ? n_chunk_expert : n_expert_used,
+                        LLM_FFN_SILU, true,
+                        false, 0.0,
+                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                        il, probs);
+            cb(moe_out, "ffn_adj_moe_out", il);
+
+            cur = ggml_add(ctx0, cur, ggml_scale(ctx0, moe_out, hparams.expert_group_scale));
+            cb(cur, "ffn_final_moe_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+};
+
 llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const {
     llama_memory_i * res;
 
@@ -19390,6 +19607,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
                     llm = std::make_unique<llm_build_smallthinker<false>>(*this, params);
                 }
             } break;
+        case LLM_ARCH_GROVEMOE:
+            {
+                llm = std::make_unique<llm_build_grovemoe>(*this, params);
+            } break;
         default:
             GGML_ABORT("fatal error");
     }
@@ -19595,6 +19816,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_SMALLTHINKER:
         case LLM_ARCH_GLM4_MOE:
         case LLM_ARCH_SEED_OSS:
+        case LLM_ARCH_GROVEMOE:
             return LLAMA_ROPE_TYPE_NEOX;
 
         case LLM_ARCH_QWEN2VL:

src/llama-model.h

@@ -275,6 +275,11 @@ struct llama_layer {
     struct ggml_tensor * ffn_down_shexp     = nullptr;
     struct ggml_tensor * ffn_up_shexp       = nullptr;
 
+    // ff adjugate experts (chexps)
+    struct ggml_tensor * ffn_gate_chexps     = nullptr;
+    struct ggml_tensor * ffn_down_chexps     = nullptr;
+    struct ggml_tensor * ffn_up_chexps       = nullptr;
+
     // ff bias
     struct ggml_tensor * ffn_gate_b = nullptr;
     struct ggml_tensor * ffn_down_b = nullptr; // b2