merge not working

Author: LostRuins

.github/workflows/release.yml

@@ -448,7 +448,7 @@ jobs:
         shell: bash
 
     env:
-      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/b380d914-366b-4b77-a74a-05e3c38b3514/intel-oneapi-base-toolkit-2025.0.0.882_offline.exe
+      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/7cd9bba0-7aab-4e30-b3ae-2221006a4a05/intel-oneapi-base-toolkit-2025.1.1.34_offline.exe
       WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel
       ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
     steps:

convert_hf_to_gguf.py

@@ -308,6 +308,7 @@ def prepare_tensors(self):
                             gguf.MODEL_TENSOR.TIME_MIX_LERP_FUSED,
                             gguf.MODEL_TENSOR.POSNET_NORM1,
                             gguf.MODEL_TENSOR.POSNET_NORM2,
+                            gguf.MODEL_TENSOR.V_ENC_EMBD_POS,
                         )
                     )
                     or not new_name.endswith(".weight")
@@ -2092,6 +2093,26 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
         return super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("Llama4ForConditionalGeneration")
+class Llama4VisionModel(VisionModel):
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.LLAMA4)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams["norm_eps"])
+        self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / self.hparams["pixel_shuffle_ratio"]))
+        assert self.hparams["hidden_act"] == "gelu"
+        self.gguf_writer.add_vision_use_gelu(True)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid # unused
+        if "multi_modal_projector" in name or "vision_model" in name:
+            # process vision tensors
+            if "positional_embedding_vlm" in name and ".weight" not in name:
+                name += ".weight"
+            return [(self.map_tensor_name(name), data_torch)]
+        return []
+
+
 @ModelBase.register("Mistral3ForConditionalGeneration")
 class Mistral3Model(LlamaModel):
     model_arch = gguf.MODEL_ARCH.LLAMA

docs/multimodal.md

@@ -74,4 +74,7 @@ NOTE: some models may require large context window, for example: `-c 8192`
 (tool_name) -hf ggml-org/InternVL3-2B-Instruct-GGUF
 (tool_name) -hf ggml-org/InternVL3-8B-Instruct-GGUF
 (tool_name) -hf ggml-org/InternVL3-14B-Instruct-GGUF
+
+# Llama 4 Scout
+(tool_name) -hf ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF
 ```

ggml/include/ggml-opt.h

@@ -128,6 +128,8 @@ extern "C" {
     // set gradients to zero, initilize loss, and optionally reset the optimizer
     GGML_API void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer);
 
+    GGML_API bool ggml_opt_static_graphs(ggml_opt_context_t opt_ctx); // whether the graphs are allocated_statically
+
     // get underlying tensors that store data
     // if not using static graphs these pointers become invalid with the next call to ggml_opt_alloc
     GGML_API struct ggml_tensor * ggml_opt_inputs(  ggml_opt_context_t opt_ctx); // forward graph input tensor

ggml/src/ggml-opt.cpp

@@ -576,6 +576,10 @@ void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer) {
     }
 }
 
+bool ggml_opt_static_graphs(ggml_opt_context_t opt_ctx) {
+    return opt_ctx->static_graphs;
+}
+
 struct ggml_tensor * ggml_opt_inputs(ggml_opt_context_t opt_ctx) {
     return opt_ctx->inputs;
 }
@@ -842,6 +846,7 @@ void ggml_opt_epoch(
         int64_t                 idata_split,
         ggml_opt_epoch_callback callback_train,
         ggml_opt_epoch_callback callback_eval) {
+    GGML_ASSERT(ggml_opt_static_graphs(opt_ctx) && "ggml_opt_epoch requires static graphs");
     struct ggml_tensor * inputs = ggml_opt_inputs(opt_ctx);
     struct ggml_tensor * labels = ggml_opt_labels(opt_ctx);
     struct ggml_tensor * data   = ggml_opt_dataset_data(dataset);

ggml/src/ggml.c

@@ -65,12 +65,17 @@
 // precomputed f32 table for f16 (256 KB) (ggml-impl.h)
 float ggml_table_f32_f16[1 << 16];
 
-#if (defined(__linux__) || defined(__APPLE__) || defined(__FreeBSD__) || defined(__NetBSD__) || defined(__OpenBSD__)) && \
-    (!defined(TARGET_OS_TV) && !defined(TARGET_OS_WATCH))
+#if defined(__linux__) || \
+    defined(__FreeBSD__) || defined(__NetBSD__) || defined(__OpenBSD__) || \
+    (defined(__APPLE__) && !TARGET_OS_TV && !TARGET_OS_WATCH)
+
 #include <unistd.h>
 #include <sys/types.h>
 #include <sys/stat.h>
 #include <sys/wait.h>
+#if defined(__linux__)
+#include <sys/prctl.h>
+#endif
 
 #if defined(__ANDROID__)
 #include <unwind.h>
@@ -134,10 +139,36 @@ static void ggml_print_backtrace(void) {
     if (GGML_NO_BACKTRACE) {
         return;
     }
-    char attach[32];
-    snprintf(attach, sizeof(attach), "attach %d", getpid());
-    int pid = fork();
-    if (pid == 0) {
+#if defined(__linux__)
+    FILE * f = fopen("/proc/self/status", "r");
+    size_t size = 0;
+    char * line = NULL;
+    ssize_t length = 0;
+    while ((length = getline(&line, &size, f)) > 0) {
+        if (!strncmp(line, "TracerPid:", sizeof("TracerPid:") - 1) &&
+            (length != sizeof("TracerPid:\t0\n") - 1 || line[length - 2] != '0')) {
+            // Already being debugged, and the breakpoint is the later abort()
+            free(line);
+            fclose(f);
+            return;
+        }
+    }
+    free(line);
+    fclose(f);
+    int lock[2] = { -1, -1 };
+    (void) !pipe(lock); // Don't start gdb until after PR_SET_PTRACER
+#endif
+    const int parent_pid = getpid();
+    const int child_pid = fork();
+    if (child_pid < 0) { // error
+        return;
+    } else if (child_pid == 0) { // child
+        char attach[32];
+        snprintf(attach, sizeof(attach), "attach %d", parent_pid);
+#if defined(__linux__)
+        close(lock[1]);
+        (void) !read(lock[0], lock, 1);
+#endif
         // try gdb
         execlp("gdb", "gdb", "--batch",
             "-ex", "set style enabled on",
@@ -150,18 +181,18 @@ static void ggml_print_backtrace(void) {
         execlp("lldb", "lldb", "--batch",
             "-o", "bt",
             "-o", "quit",
-            "-p", attach,
+            "-p", &attach[sizeof("attach ") - 1],
             (char *) NULL);
-        exit(EXIT_FAILURE);
-    } else {
-        int wstatus;
-        waitpid(pid, &wstatus, 0);
-        if (WIFEXITED(wstatus)) {
-            if (WEXITSTATUS(wstatus) == EXIT_FAILURE) {
-                // gdb failed, fallback to backtrace_symbols
-                ggml_print_backtrace_symbols();
-            }
-        }
+        // gdb failed, fallback to backtrace_symbols
+        ggml_print_backtrace_symbols();
+        _Exit(0);
+    } else { // parent
+#if defined(__linux__)
+        prctl(PR_SET_PTRACER, child_pid);
+        close(lock[1]);
+        close(lock[0]);
+#endif
+        waitpid(child_pid, NULL, 0);
     }
 }
 #else

gguf-py/gguf/constants.py

@@ -482,14 +482,15 @@ class MODEL_TENSOR(IntEnum):
     V_ENC_EMBD_CLS       = auto()
     V_ENC_EMBD_PATCH     = auto()
     V_ENC_EMBD_POS       = auto()
+    V_ENC_INPUT_NORM     = auto()
     V_ENC_ATTN_Q         = auto()
     V_ENC_ATTN_Q_NORM    = auto()
     V_ENC_ATTN_K         = auto()
     V_ENC_ATTN_K_NORM    = auto()
     V_ENC_ATTN_V         = auto()
-    V_ENC_INPUT_NORM     = auto()
-    V_ENC_OUTPUT         = auto()
-    V_ENC_OUTPUT_NORM    = auto()
+    V_ENC_ATTN_O         = auto()
+    V_ENC_ATTN_O_NORM    = auto()
+    V_ENC_POST_ATTN_NORM = auto()
     V_ENC_FFN_UP         = auto()
     V_ENC_FFN_GATE       = auto()
     V_ENC_FFN_DOWN       = auto()
@@ -749,8 +750,9 @@ class MODEL_TENSOR(IntEnum):
     MODEL_TENSOR.V_ENC_ATTN_K_NORM:         "v.blk.{bid}.attn_k_norm",
     MODEL_TENSOR.V_ENC_ATTN_V:              "v.blk.{bid}.attn_v",
     MODEL_TENSOR.V_ENC_INPUT_NORM:          "v.blk.{bid}.ln1",
-    MODEL_TENSOR.V_ENC_OUTPUT:              "v.blk.{bid}.attn_out",
-    MODEL_TENSOR.V_ENC_OUTPUT_NORM:         "v.blk.{bid}.ln2",
+    MODEL_TENSOR.V_ENC_ATTN_O:              "v.blk.{bid}.attn_out",
+    MODEL_TENSOR.V_ENC_ATTN_O_NORM:         "v.blk.{bid}.attn_out_norm",
+    MODEL_TENSOR.V_ENC_POST_ATTN_NORM:      "v.blk.{bid}.ln2",
     MODEL_TENSOR.V_ENC_FFN_UP:              "v.blk.{bid}.ffn_up",
     MODEL_TENSOR.V_ENC_FFN_GATE:            "v.blk.{bid}.ffn_gate",
     MODEL_TENSOR.V_ENC_FFN_DOWN:            "v.blk.{bid}.ffn_down",
@@ -785,14 +787,15 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.V_ENC_EMBD_CLS,
         MODEL_TENSOR.V_ENC_EMBD_PATCH,
         MODEL_TENSOR.V_ENC_EMBD_POS,
+        MODEL_TENSOR.V_ENC_INPUT_NORM,
         MODEL_TENSOR.V_ENC_ATTN_Q,
         MODEL_TENSOR.V_ENC_ATTN_Q_NORM,
         MODEL_TENSOR.V_ENC_ATTN_K,
         MODEL_TENSOR.V_ENC_ATTN_K_NORM,
         MODEL_TENSOR.V_ENC_ATTN_V,
-        MODEL_TENSOR.V_ENC_INPUT_NORM,
-        MODEL_TENSOR.V_ENC_OUTPUT,
-        MODEL_TENSOR.V_ENC_OUTPUT_NORM,
+        MODEL_TENSOR.V_ENC_ATTN_O,
+        MODEL_TENSOR.V_ENC_ATTN_O_NORM,
+        MODEL_TENSOR.V_ENC_POST_ATTN_NORM,
         MODEL_TENSOR.V_ENC_FFN_UP,
         MODEL_TENSOR.V_ENC_FFN_GATE,
         MODEL_TENSOR.V_ENC_FFN_DOWN,
@@ -2180,6 +2183,7 @@ class VisionProjectorType:
     GEMMA3 = "gemma3"
     IDEFICS3 = "idefics3"
     PIXTRAL = "pixtral"
+    LLAMA4 = "llama4"
     QWEN2VL = "qwen2vl_merger"
     QWEN25VL = "qwen2.5vl_merger"
     INTERNVL = "internvl"

gguf-py/gguf/tensor_mapping.py

@@ -902,10 +902,12 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_MMPROJ_FC: (
             "model.connector.modality_projection.proj", # SmolVLM
+            "multi_modal_projector.linear_1", # llama 4
         ),
 
         MODEL_TENSOR.V_MMPROJ_MLP: (
             "model.mm_projector.mlp.mlp.{bid}",
+            "vision_model.vision_adapter.mlp.fc{bid}", # llama 4
             "mlp1.{bid}", # InternVL
         ),
 
@@ -915,26 +917,30 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_EMBD_CLS: (
             "vision_tower.vision_model.embeddings.class_embedding",
+            "vision_model.class_embedding", # llama 4
         ),
 
         MODEL_TENSOR.V_ENC_EMBD_PATCH: (
             "vision_tower.vision_model.embeddings.patch_embedding",
             "vpm.embeddings.patch_embedding",
             "model.vision_model.embeddings.patch_embedding", # SmolVLM
             "vision_tower.patch_conv", # pixtral
+            "vision_model.patch_embedding.linear", # llama 4
             "visual.patch_embed.proj", # qwen2vl
         ),
 
         MODEL_TENSOR.V_ENC_EMBD_POS: (
             "vision_tower.vision_model.embeddings.position_embedding",
             "vpm.embeddings.position_embedding",
             "model.vision_model.embeddings.position_embedding", # SmolVLM
+            "vision_model.positional_embedding_vlm", # llama 4
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_Q: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.q_proj",
             "vpm.encoder.layers.{bid}.self_attn.q_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.q_proj", # SmolVLM
+            "vision_model.model.layers.{bid}.self_attn.q_proj", # llama4
             "vision_tower.transformer.layers.{bid}.attention.q_proj", # pixtral
             "visual.blocks.{bid}.attn.q", # qwen2vl, generated
         ),
@@ -947,6 +953,7 @@ class TensorNameMap:
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.k_proj",
             "vpm.encoder.layers.{bid}.self_attn.k_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.k_proj", # SmolVLM
+            "vision_model.model.layers.{bid}.self_attn.k_proj", # llama4
             "vision_tower.transformer.layers.{bid}.attention.k_proj", # pixtral
             "visual.blocks.{bid}.attn.k", # qwen2vl, generated
         ),
@@ -959,6 +966,7 @@ class TensorNameMap:
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.v_proj",
             "vpm.encoder.layers.{bid}.self_attn.v_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.v_proj", # SmolVLM
+            "vision_model.model.layers.{bid}.self_attn.v_proj", # llama4
             "vision_tower.transformer.layers.{bid}.attention.v_proj", # pixtral
             "visual.blocks.{bid}.attn.v", # qwen2vl, generated
         ),
@@ -969,23 +977,26 @@ class TensorNameMap:
             "vpm.encoder.layers.{bid}.layer_norm1",
             "model.vision_model.encoder.layers.{bid}.layer_norm1", # SmolVLM
             "vision_tower.transformer.layers.{bid}.attention_norm", # pixtral
+            "vision_model.model.layers.{bid}.input_layernorm", # llama4
             "visual.blocks.{bid}.norm1", # qwen2vl
         ),
 
-        MODEL_TENSOR.V_ENC_OUTPUT: (
+        MODEL_TENSOR.V_ENC_ATTN_O: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj",
             "vision_tower.vision_model.encoder.layers.{bid}.attn.proj", # InternVL
             "vpm.encoder.layers.{bid}.self_attn.out_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.out_proj", # SmolVLM
+            "vision_model.model.layers.{bid}.self_attn.o_proj", # llama4
             "vision_tower.transformer.layers.{bid}.attention.o_proj", # pixtral
             "visual.blocks.{bid}.attn.proj", # qwen2vl
         ),
 
-        MODEL_TENSOR.V_ENC_OUTPUT_NORM: (
+        MODEL_TENSOR.V_ENC_POST_ATTN_NORM: (
             "vision_tower.vision_model.encoder.layers.{bid}.layer_norm2",
             "vision_tower.vision_model.encoder.layers.{bid}.norm2", # InternVL
             "vpm.encoder.layers.{bid}.layer_norm2",
             "model.vision_model.encoder.layers.{bid}.layer_norm2", # SmolVLM
+            "vision_model.model.layers.{bid}.post_attention_layernorm", # llama4
             "vision_tower.transformer.layers.{bid}.ffn_norm", # pixtral
             "visual.blocks.{bid}.norm2", # qwen2vl
         ),
@@ -995,6 +1006,7 @@ class TensorNameMap:
             "vpm.encoder.layers.{bid}.mlp.fc1",
             "model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3
             "vision_tower.transformer.layers.{bid}.feed_forward.up_proj", # pixtral
+            "vision_model.model.layers.{bid}.mlp.fc1", # llama4
             "visual.blocks.{bid}.mlp.fc1", # qwen2vl
             "visual.blocks.{bid}.mlp.up_proj", # qwen2.5vl
         ),
@@ -1009,6 +1021,7 @@ class TensorNameMap:
             "vpm.encoder.layers.{bid}.mlp.fc2",
             "model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3
             "vision_tower.transformer.layers.{bid}.feed_forward.down_proj", # pixtral
+            "vision_model.model.layers.{bid}.mlp.fc2", # llama4
             "visual.blocks.{bid}.mlp.fc2", # qwen2vl
             "visual.blocks.{bid}.mlp.down_proj", # qwen2.5vl
         ),
@@ -1024,11 +1037,13 @@ class TensorNameMap:
         MODEL_TENSOR.V_PRE_NORM: (
             "vision_tower.vision_model.pre_layrnorm",
             "vision_tower.ln_pre", # pixtral
+            "vision_model.layernorm_pre", # llama4
         ),
 
         MODEL_TENSOR.V_POST_NORM: (
             "vision_tower.vision_model.post_layernorm",
             "model.vision_model.post_layernorm", # SmolVLM
+            "vision_model.layernorm_post", # llama4
             "visual.merger.ln_q", # qwen2vl
         ),