convert : add Llama4ForCausalLM (#16042)

* convert : add Llama4ForCausalLM

* handle swa

* half working version

* fix use_kq_norm

* fix use_kq_norm

convert_hf_to_gguf.py

@@ -2393,7 +2393,10 @@ class SmolVLMModel(MmprojModel):
         return [] # skip other tensors
 
 
-@ModelBase.register("Llama4ForConditionalGeneration")
+@ModelBase.register(
+    "Llama4ForConditionalGeneration",
+    "Llama4ForCausalLM",
+)
 class Llama4Model(LlamaModel):
     model_arch = gguf.MODEL_ARCH.LLAMA4
     undo_permute = False
@@ -2411,6 +2414,10 @@ class Llama4Model(LlamaModel):
         super().set_gguf_parameters()
         self.gguf_writer.add_interleave_moe_layer_step(self.hparams["interleave_moe_layer_step"])
         self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size_moe"])
+        if "layer_types" in self.hparams:
+            if all(lt == "full_attention" for lt in self.hparams["layer_types"]):
+                # all layers are full attention (for MobileLLM), disable swa
+                self.gguf_writer.add_sliding_window(0)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
         if name.startswith("language_model."):

src/llama-hparams.h

@@ -149,7 +149,7 @@ struct llama_hparams {
     bool causal_attn   = true;
     bool use_alibi     = false;
     bool attn_soft_cap = false;
-    bool use_kq_norm   = true;
+    bool use_kq_norm   = false;
 
     // for Classifiers
     uint32_t n_cls_out = 1;

src/llama-model.cpp

@@ -36,6 +36,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_80M:           return "80M";
         case LLM_TYPE_109M:          return "109M";
         case LLM_TYPE_137M:          return "137M";
+        case LLM_TYPE_140M:          return "140M";
         case LLM_TYPE_160M:          return "160M";
         case LLM_TYPE_190M:          return "190M";
         case LLM_TYPE_220M:          return "220M";
@@ -44,6 +45,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_270M:          return "270M";
         case LLM_TYPE_335M:          return "335M";
         case LLM_TYPE_350M:          return "350M";
+        case LLM_TYPE_360M:          return "360M";
         case LLM_TYPE_410M:          return "410M";
         case LLM_TYPE_450M:          return "450M";
         case LLM_TYPE_475M:          return "475M";
@@ -51,6 +53,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_700M:          return "700M";
         case LLM_TYPE_770M:          return "770M";
         case LLM_TYPE_780M:          return "780M";
+        case LLM_TYPE_950M:          return "950M";
         case LLM_TYPE_0_3B:          return "0.3B";
         case LLM_TYPE_0_5B:          return "0.5B";
         case LLM_TYPE_0_6B:          return "0.6B";
@@ -622,19 +625,32 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
                 ml.get_key(LLM_KV_INTERLEAVE_MOE_LAYER_STEP,   hparams.n_moe_layer_step);
 
+                const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+                if (found_swa && hparams.n_swa == 0) {
+                    hparams.swa_type             = LLAMA_SWA_TYPE_NONE;
+                    hparams.n_no_rope_layer_step = hparams.n_layer; // always use rope
+                } else {
                     hparams.swa_type      = LLAMA_SWA_TYPE_CHUNKED;
-                hparams.n_swa         = 8192; // should this be a gguf kv? currently it's the same for Scout and Maverick
+                    hparams.n_swa         = 8192;
                     hparams.set_swa_pattern(4);   // pattern: 3 chunked - 1 full
+                }
 
                 switch (hparams.n_expert) {
+                    case 0: {
+                        // MobileLLM (no MoE)
+                        switch (hparams.n_embd) {
+                            case 2048: type = LLM_TYPE_140M; break;
+                            case 4096: type = LLM_TYPE_360M; break;
+                            case 6144: type = LLM_TYPE_950M; break;
+                            default:   type = LLM_TYPE_UNKNOWN;
+                        }
+                    } break;
                     case 16:  type = LLM_TYPE_17B_16E; break;
                     case 128: type = LLM_TYPE_17B_128E; break;
                     default:  type = LLM_TYPE_UNKNOWN;
                 }
 
-                if (type == LLM_TYPE_17B_128E) {
+                hparams.use_kq_norm = type != LLM_TYPE_17B_128E;
-                    hparams.use_kq_norm = false;
-                }
             } break;
         case LLM_ARCH_ARCEE:
             {
@@ -2454,9 +2470,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
                     }
 
-                    GGML_ASSERT(hparams.n_moe_layer_step > 0 && "Llama 4 requires n_moe_layer_step > 0");
                     for (int i = 0; i < n_layer; ++i) {
-                        bool is_moe_layer = (i + 1) % hparams.n_moe_layer_step == 0;
+                        bool is_moe_layer = hparams.n_moe_layer_step > 0 && (i + 1) % hparams.n_moe_layer_step == 0;
 
                         auto & layer = layers[i];
 
@@ -6328,6 +6343,14 @@ struct llm_build_llama : public llm_graph_context {
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
 
+                if (hparams.use_kq_norm) {
+                    // Llama4TextL2Norm
+                    Qcur = ggml_rms_norm(ctx0, Qcur, hparams.f_norm_rms_eps);
+                    Kcur = ggml_rms_norm(ctx0, Kcur, hparams.f_norm_rms_eps);
+                    cb(Qcur, "Qcur_normed", il);
+                    cb(Kcur, "Kcur_normed", il);
+                }
+
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
                         Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
@@ -6435,7 +6458,8 @@ struct llm_build_llama_iswa : public llm_graph_context {
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
 
-            const bool use_rope = (il + 1) % hparams.n_no_rope_layer_step != 0;
+            const bool use_rope = hparams.n_no_rope_layer_step > 0 &&
+                                  (il + 1) % hparams.n_no_rope_layer_step != 0;
 
             // norm
             cur = build_norm(inpL,
@@ -18981,7 +19005,11 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             } break;
         case LLM_ARCH_LLAMA4:
             {
+                if (hparams.swa_type == LLAMA_SWA_TYPE_NONE) {
+                    llm = std::make_unique<llm_build_llama>(*this, params);
+                } else {
                     llm = std::make_unique<llm_build_llama_iswa>(*this, params);
+                }
             } break;
         case LLM_ARCH_DECI:
             {

src/llama-model.h

@@ -28,6 +28,7 @@ enum llm_type {
     LLM_TYPE_80M,
     LLM_TYPE_109M,
     LLM_TYPE_137M,
+    LLM_TYPE_140M,
     LLM_TYPE_160M,
     LLM_TYPE_190M,
     LLM_TYPE_220M,
@@ -36,6 +37,7 @@ enum llm_type {
     LLM_TYPE_270M,
     LLM_TYPE_335M,
     LLM_TYPE_350M,
+    LLM_TYPE_360M,
     LLM_TYPE_410M,
     LLM_TYPE_450M,
     LLM_TYPE_475M,
@@ -43,6 +45,7 @@ enum llm_type {
     LLM_TYPE_700M,
     LLM_TYPE_770M,
     LLM_TYPE_780M,
+    LLM_TYPE_950M,
     LLM_TYPE_0_3B,
     LLM_TYPE_0_5B,
     LLM_TYPE_0_6B,