Add LLaDA-7b-MoE diffusion model

common/arg.cpp

@@ -1704,7 +1704,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.system_prompt = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_DIFFUSION}));
     add_opt(common_arg(
         {"--no-perf"},
         string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),

convert_hf_to_gguf.py

@@ -888,6 +888,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "a1e163ecab2e718a4c829d1148b6e86824ec36163bb71941c3dca9cd5ac25756":
             # ref: https://huggingface.co/JetBrains/Mellum-4b-base
             res = "mellum"
+        if chkhsh == "9b1be57e70d20d9501b2b3186e792d81181ae36ada3903c26f9fea418cf87206":
+            # ref: https://huggingface.co/inclusionAI/LLaDA-MoE-7B-A1B-Base
+            res = "llada-moe"
 
         if res is None:
             logger.warning("\n")
@@ -8239,6 +8242,81 @@ def prepare_tensors(self):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@ModelBase.register("LLaDAMoEModel", "LLaDAMoEModelLM")
+class LLaDAMoEModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.LLADA_MOE
+
+    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 (expert_intermediate_size := self.hparams.get("expert_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size)
+
+        # number of experts used per token (top-k)
+        if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
+            self.gguf_writer.add_expert_used_count(n_experts_used)
+
+        mask_token_id = 156895
+        if self.hparams.get("mask_token_id") is not None:
+            mask_token_id = self.hparams["mask_token_id"]
+
+        self.gguf_writer.add_mask_token_id(mask_token_id)
+        self.gguf_writer.add_causal_attention(False)
+        self.gguf_writer.add_diffusion_shift_logits(False)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    # Copied from: Qwen2MoeModel
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if 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)]
+
+    # Copied from: Qwen2MoeModel
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        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("HunYuanDenseV1ForCausalLM")
 class HunYuanModel(TextModel):
     model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE

convert_hf_to_gguf_update.py

@@ -139,6 +139,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "lfm2",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"},
     {"name": "exaone4",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", },
     {"name": "mellum",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", },
+    {"name": "llada-moe",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/LLaDA-MoE-7B-A1B-Base", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions

examples/diffusion/diffusion-cli.cpp

@@ -510,19 +510,27 @@ static void diffusion_generate(llama_context *          ctx,
     n_generated = params.max_length;
 }
 
-static std::string format_input_text(const std::string & prompt, bool use_chat_template, llama_model * model) {
+static std::string format_input_text(const std::string & prompt, const std::string & system_prompt, bool use_chat_template, llama_model * model) {
     if (!use_chat_template) {
         return prompt;
     }
 
     auto chat_templates = common_chat_templates_init(model, "");
-
     common_chat_templates_inputs inputs;
-    common_chat_msg              user_msg;
-    user_msg.role                = "user";
-    user_msg.content             = prompt;
-    inputs.add_generation_prompt = true;
+    common_chat_msg system_msg;
+
+    if (!system_prompt.empty()) {
+        system_msg.role = "system";
+        system_msg.content = system_prompt;
+        inputs.messages.push_back(system_msg);
+    }
+
+    common_chat_msg user_msg;
+    user_msg.role = "user";
+    user_msg.content = prompt;
+
     inputs.messages.push_back(user_msg);
+    inputs.add_generation_prompt = true;
 
     auto result = common_chat_templates_apply(chat_templates.get(), inputs);
 
@@ -579,7 +587,8 @@ int main(int argc, char ** argv) {
     llama_set_n_threads(ctx, params.cpuparams.n_threads, params.cpuparams_batch.n_threads);
 
     const llama_vocab * vocab            = llama_model_get_vocab(model);
-    std::string         formatted_prompt = format_input_text(params.prompt, params.enable_chat_template, model);
+
+    std::string         formatted_prompt = format_input_text(params.prompt, params.system_prompt, params.enable_chat_template, model);
 
     std::vector<llama_token> input_tokens = common_tokenize(vocab,
                                                             formatted_prompt,
@@ -596,6 +605,7 @@ int main(int argc, char ** argv) {
     }
 
     llama_token mask_token_id = llama_vocab_mask(vocab);
+
     GGML_ASSERT(mask_token_id != LLAMA_TOKEN_NULL);
 
     bool visual_mode = params.diffusion.visual_mode;

gguf-py/gguf/constants.py

@@ -399,6 +399,7 @@ class MODEL_ARCH(IntEnum):
     DREAM            = auto()
     SMALLTHINKER     = auto()
     LLADA            = auto()
+    LLADA_MOE        = auto()
     SEED_OSS         = auto()
 
 
@@ -735,6 +736,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.DREAM:            "dream",
     MODEL_ARCH.SMALLTHINKER:     "smallthinker",
     MODEL_ARCH.LLADA:            "llada",
+    MODEL_ARCH.LLADA_MOE:        "llada-moe",
     MODEL_ARCH.SEED_OSS:         "seed_oss",
 }
 
@@ -2693,7 +2695,23 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
     ],
-    # TODO
+    MODEL_ARCH.LLADA_MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+    ]
 }
 
 # tensors that will not be serialized

src/llama-arch.cpp

@@ -96,6 +96,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_DREAM,            "dream"            },
     { LLM_ARCH_SMALLTHINKER,     "smallthinker"     },
     { LLM_ARCH_LLADA,            "llada"            },
+    { LLM_ARCH_LLADA_MOE,        "llada-moe"        },
     { LLM_ARCH_SEED_OSS,         "seed_oss"         },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
@@ -2147,6 +2148,26 @@ 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_LLADA_MOE,
+        {
+            { 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_ARCH_SEED_OSS,
         {
@@ -2427,6 +2448,7 @@ bool llm_arch_is_diffusion(const llm_arch & arch) {
     switch (arch) {
         case LLM_ARCH_DREAM:
         case LLM_ARCH_LLADA:
+        case LLM_ARCH_LLADA_MOE:
             return true;
         default:
             return false;

src/llama-arch.h

@@ -100,6 +100,7 @@ enum llm_arch {
     LLM_ARCH_DREAM,
     LLM_ARCH_SMALLTHINKER,
     LLM_ARCH_LLADA,
+    LLM_ARCH_LLADA_MOE,
     LLM_ARCH_SEED_OSS,
     LLM_ARCH_UNKNOWN,
 };