add llava to conversion

convert_hf_to_gguf.py

@@ -66,6 +66,11 @@ class Model:
     dir_model_card: Path
     is_lora: bool
 
+    # for vision model
+    vparams: dict[str, Any] | None = None
+    v_tensor_map: gguf.TensorNameMap
+    v_tensor_names: set[str] | None
+
     # subclasses should define this!
     model_arch: gguf.MODEL_ARCH
 
@@ -210,9 +215,13 @@ class Model:
 
     def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
         new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes)
-        if new_name is None:
+        new_name_vision = self.v_tensor_map.get_name(key=name, try_suffixes=try_suffixes)
+        if new_name is not None:
+            return new_name
+        elif new_name_vision is not None:
+            return new_name_vision
+        else:
             raise ValueError(f"Can not map tensor {name!r}")
-        return new_name
 
     def set_gguf_parameters(self):
         self.gguf_writer.add_block_count(self.block_count)
@@ -452,7 +461,10 @@ class Model:
     @staticmethod
     def load_hparams(dir_model: Path):
         with open(dir_model / "config.json", "r", encoding="utf-8") as f:
-            return json.load(f)
+            hparams = json.load(f)
+            if "text_config" in hparams:
+                hparams = {**hparams, **hparams["text_config"]}
+            return hparams
 
     @classmethod
     def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
@@ -1501,10 +1513,17 @@ class StableLMModel(Model):
                 raise ValueError(f"Unprocessed norms: {norms}")
 
 
-@Model.register("LLaMAForCausalLM", "LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM")
+@Model.register("LLaMAForCausalLM", "LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM", "LlavaForConditionalGeneration")
 class LlamaModel(Model):
     model_arch = gguf.MODEL_ARCH.LLAMA
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if "vision_config" in self.hparams:
+            self.vparams = self.hparams["vision_config"]
+        if self.vparams is not None:
+            self.v_tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.LLAVA_VISION, self.vparams["num_hidden_layers"])
+
     def set_vocab(self):
         try:
             self._set_vocab_sentencepiece()
@@ -1554,6 +1573,17 @@ class LlamaModel(Model):
         if self.hparams.get("vocab_size", 32000) == 49152:
             self.gguf_writer.add_add_bos_token(False)
 
+        # For vision model
+        if self.vparams is not None:
+            self.gguf_writer.add_vision_type("clip")
+            self.gguf_writer.add_vision_image_size(self.vparams["image_size"])
+            self.gguf_writer.add_vision_patch_size(self.vparams["patch_size"])
+            self.gguf_writer.add_vision_clip_architecture("llava")
+            self.gguf_writer.add_vision_clip_block_count(self.vparams["num_hidden_layers"])
+            self.gguf_writer.add_vision_clip_embedding_length(self.vparams["hidden_size"])
+            self.gguf_writer.add_vision_clip_feed_forward_length(self.vparams["intermediate_size"])
+            self.gguf_writer.add_vision_clip_head_count(self.vparams["num_attention_heads"])
+
     @staticmethod
     def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
         if n_head_kv is not None and n_head != n_head_kv:
@@ -1568,6 +1598,9 @@ class LlamaModel(Model):
         n_head = self.hparams["num_attention_heads"]
         n_kv_head = self.hparams.get("num_key_value_heads")
 
+        if name.startswith("language_model"):
+            name = name.replace("language_model.", "")
+
         if name.endswith(("q_proj.weight", "q_proj.bias")):
             data_torch = LlamaModel.permute(data_torch, n_head, n_head)
         if name.endswith(("k_proj.weight", "k_proj.bias")):

gguf-py/gguf/constants.py

@@ -178,6 +178,26 @@ class Keys:
         TYPE       = "adapter.type"
         LORA_ALPHA = "adapter.lora.alpha"
 
+    class Vision:
+        # only support vision.type = "clip" for now
+        TYPE                = "vision.type"
+        IMAGE_SIZE          = "vision.image_size"
+        PATCH_SIZE          = "vision.patch_size"
+        IMAGE_MEAN          = "vision.image_mean"
+        IMAGE_STD           = "vision.image_std"
+
+        class Clip:
+            ARCHITECTURE        = "vision.clip.architecture"
+            CONTEXT_LENGTH      = "vision.clip.context_length"
+            EMBEDDING_LENGTH    = "vision.clip.embedding_length"
+            BLOCK_COUNT         = "vision.clip.block_count"
+            FEED_FORWARD_LENGTH = "vision.clip.feed_forward_length"
+            PROJECTION_TYPE     = "vision.clip.projection_type"
+            PROJECTION_DIM      = "vision.clip.projection_dim"
+            USE_GELU            = "vision.clip.use_gelu"
+            HEAD_COUNT          = "vision.clip.attention.head_count"
+            LAYERNORM_EPS       = "vision.clip.attention.layer_norm_epsilon"
+
 #
 # recommended mapping of model tensor names for storage in gguf
 #
@@ -238,6 +258,8 @@ class MODEL_ARCH(IntEnum):
     GRANITE      = auto()
     GRANITE_MOE  = auto()
     CHAMELEON    = auto()
+    # vision models
+    LLAVA_VISION = auto()
 
 
 class MODEL_TENSOR(IntEnum):
@@ -345,6 +367,22 @@ class MODEL_TENSOR(IntEnum):
     ENC_FFN_DOWN         = auto()
     ENC_FFN_UP           = auto()
     ENC_OUTPUT_NORM      = auto()
+    # vision
+    V_MMPROJ_A           = auto()
+    V_MMPROJ_B           = auto()
+    V_ENC_EMBD_CLS       = auto()
+    V_ENC_EMBD_PATCH     = auto()
+    V_ENC_EMBD_POS       = auto()
+    V_ENC_ATTN_Q         = auto()
+    V_ENC_ATTN_K         = auto()
+    V_ENC_ATTN_V         = auto()
+    V_ENC_INPUT_NORM     = auto()
+    V_ENC_OUTPUT         = auto()
+    V_ENC_OUTPUT_NORM    = auto()
+    V_ENC_FFN_UP         = auto()
+    V_ENC_FFN_DOWN       = auto()
+    V_PRE_NORM           = auto()
+    V_POST_NORM          = auto()
 
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@@ -397,6 +435,8 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.GRANITE:        "granite",
     MODEL_ARCH.GRANITE_MOE:    "granitemoe",
     MODEL_ARCH.CHAMELEON:      "chameleon",
+    # vision
+    MODEL_ARCH.LLAVA_VISION:   "llava",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -504,6 +544,22 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.ENC_FFN_DOWN:              "enc.blk.{bid}.ffn_down",
     MODEL_TENSOR.ENC_FFN_UP:                "enc.blk.{bid}.ffn_up",
     MODEL_TENSOR.ENC_OUTPUT_NORM:           "enc.output_norm",
+    # vision
+    MODEL_TENSOR.V_MMPROJ_A:                "v.mmproj_a",
+    MODEL_TENSOR.V_MMPROJ_B:                "v.mmproj_b",
+    MODEL_TENSOR.V_ENC_EMBD_CLS:            "v.enc.embd.cls",
+    MODEL_TENSOR.V_ENC_EMBD_PATCH:          "v.enc.embd.patch",
+    MODEL_TENSOR.V_ENC_EMBD_POS:            "v.enc.embd.pos",
+    MODEL_TENSOR.V_ENC_ATTN_Q:              "v.enc.blk.{bid}.attn_q",
+    MODEL_TENSOR.V_ENC_ATTN_K:              "v.enc.blk.{bid}.attn_k",
+    MODEL_TENSOR.V_ENC_ATTN_V:              "v.enc.blk.{bid}.attn_v",
+    MODEL_TENSOR.V_ENC_INPUT_NORM:          "v.enc.blk.{bid}.input_norm",
+    MODEL_TENSOR.V_ENC_OUTPUT:              "v.enc.blk.{bid}.output",
+    MODEL_TENSOR.V_ENC_OUTPUT_NORM:         "v.enc.blk.{bid}.output_norm",
+    MODEL_TENSOR.V_ENC_FFN_UP:              "v.enc.blk.{bid}.ffn_up",
+    MODEL_TENSOR.V_ENC_FFN_DOWN:            "v.enc.blk.{bid}.ffn_down",
+    MODEL_TENSOR.V_PRE_NORM:                "v.pre_norm",
+    MODEL_TENSOR.V_POST_NORM:               "v.post_norm",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -1279,6 +1335,23 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.LLAVA_VISION: [
+        MODEL_TENSOR.V_MMPROJ_A,
+        MODEL_TENSOR.V_MMPROJ_B,
+        MODEL_TENSOR.V_ENC_EMBD_CLS,
+        MODEL_TENSOR.V_ENC_EMBD_PATCH,
+        MODEL_TENSOR.V_ENC_EMBD_POS,
+        MODEL_TENSOR.V_ENC_ATTN_Q,
+        MODEL_TENSOR.V_ENC_ATTN_K,
+        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_FFN_UP,
+        MODEL_TENSOR.V_ENC_FFN_DOWN,
+        MODEL_TENSOR.V_PRE_NORM,
+        MODEL_TENSOR.V_POST_NORM,
+    ],
     # TODO
 }
 

gguf-py/gguf/gguf_writer.py

@@ -814,6 +814,36 @@ class GGUFWriter:
     def add_precompiled_charsmap(self, charsmap: Sequence[bytes]) -> None:
         self.add_array(Keys.Tokenizer.PRECOMPILED_CHARSMAP, charsmap)
 
+    def add_vision_type(self, value: str) -> None:
+        self.add_string(Keys.Vision.TYPE, value)
+
+    def add_vision_image_size(self, value: int) -> None:
+        self.add_uint32(Keys.Vision.IMAGE_SIZE, value)
+
+    def add_vision_patch_size(self, value: int) -> None:
+        self.add_uint32(Keys.Vision.PATCH_SIZE, value)
+
+    def add_vision_clip_architecture(self, value: str) -> None:
+        self.add_string(Keys.Vision.Clip.ARCHITECTURE, value)
+
+    def add_vision_clip_context_length(self, value: int) -> None:
+        self.add_uint32(Keys.Vision.Clip.CONTEXT_LENGTH, value)
+
+    def add_vision_clip_embedding_length(self, value: int) -> None:
+        self.add_uint32(Keys.Vision.Clip.EMBEDDING_LENGTH, value)
+
+    def add_vision_clip_block_count(self, value: int) -> None:
+        self.add_uint32(Keys.Vision.Clip.BLOCK_COUNT, value)
+
+    def add_vision_clip_feed_forward_length(self, value: int) -> None:
+        self.add_uint32(Keys.Vision.Clip.FEED_FORWARD_LENGTH, value)
+
+    def add_vision_clip_head_count(self, value: int) -> None:
+        self.add_uint32(Keys.Vision.Clip.HEAD_COUNT, value)
+
+    def add_vision_clip_layer_norm_epsilon(self, value: float) -> None:
+        self.add_float32(Keys.Vision.Clip.LAYERNORM_EPS, value)
+
     def add_chat_template(self, value: str | Sequence[Mapping[str, str]]) -> None:
         if not isinstance(value, str):
             template_default = None

gguf-py/gguf/tensor_mapping.py

@@ -679,6 +679,66 @@ class TensorNameMap:
         MODEL_TENSOR.ENC_OUTPUT_NORM: (
             "encoder.final_layer_norm", # t5
         ),
+
+        MODEL_TENSOR.V_MMPROJ_A: (
+            "multi_modal_projector.linear_1",
+        ),
+
+        MODEL_TENSOR.V_MMPROJ_B: (
+            "multi_modal_projector.linear_2",
+        ),
+
+        MODEL_TENSOR.V_ENC_EMBD_CLS: (
+            "vision_tower.vision_model.embeddings.class_embedding",
+        ),
+
+        MODEL_TENSOR.V_ENC_EMBD_PATCH: (
+            "vision_tower.vision_model.embeddings.patch_embedding",
+        ),
+
+        MODEL_TENSOR.V_ENC_EMBD_POS: (
+            "vision_tower.vision_model.embeddings.position_embedding",
+        ),
+
+        MODEL_TENSOR.V_ENC_ATTN_Q: (
+            "vision_tower.vision_model.encoder.layers.{bid}.self_attn.q_proj",
+        ),
+
+        MODEL_TENSOR.V_ENC_ATTN_K: (
+            "vision_tower.vision_model.encoder.layers.{bid}.self_attn.k_proj",
+        ),
+
+        MODEL_TENSOR.V_ENC_ATTN_V: (
+            "vision_tower.vision_model.encoder.layers.{bid}.self_attn.v_proj",
+        ),
+
+        MODEL_TENSOR.V_ENC_INPUT_NORM: (
+            "vision_tower.vision_model.encoder.layers.{bid}.layer_norm1",
+        ),
+
+        MODEL_TENSOR.V_ENC_OUTPUT: (
+            "vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj",
+        ),
+
+        MODEL_TENSOR.V_ENC_OUTPUT_NORM: (
+            "vision_tower.vision_model.encoder.layers.{bid}.layer_norm2",
+        ),
+
+        MODEL_TENSOR.V_ENC_FFN_UP: (
+            "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1",
+        ),
+
+        MODEL_TENSOR.V_ENC_FFN_DOWN: (
+            "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2",
+        ),
+
+        MODEL_TENSOR.V_PRE_NORM: (
+            "vision_tower.vision_model.pre_layrnorm",
+        ),
+
+        MODEL_TENSOR.V_POST_NORM: (
+            "vision_tower.vision_model.post_layernorm",
+        ),
     }
 
     # architecture-specific block mappings