Refactor: convert_hf_to_gguf.py (#17114)

* move conversion code to a dedicated conversion directory and split the files akin to the src/models architecture

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

conversion/llava.py

@@ -0,0 +1,129 @@
+from __future__ import annotations
+
+import json
+
+from typing import Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, gguf, logger
+
+from .llama import LlamaModel
+
+
+@ModelBase.register(
+    "LlavaForConditionalGeneration", # pixtral
+    "Mistral3ForConditionalGeneration", # mistral small 3.1
+)
+class LlavaVisionModel(MmprojModel):
+    img_break_tok_id = -1
+    use_break_tok = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if self.hparams.get("model_type") == "pixtral":
+            # layer_norm_eps is not in config.json, it is hard-coded in modeling_pixtral.py
+            self.hparams["layer_norm_eps"] = self.hparams.get("layer_norm_eps", 1e-5)
+            if self.use_break_tok:
+                self.img_break_tok_id = self.get_token_id("[IMG_BREAK]")
+        elif self.is_mistral_format:
+            # hparams is already vision config here so norm_eps is only defined in global_config.
+            self.hparams["norm_eps"] = self.global_config.get("norm_eps", None)
+            assert self.hparams["norm_eps"] is not None, "norm_eps not found in params.json"
+            if self.use_break_tok:
+                self.img_break_tok_id = self.find_vparam(["image_break_token_id"])
+
+                # params.json may ship -1 placeholders (Mistral Medium 3.5)
+                # resolve the real id from the bundled tokenizer in that case
+                if self.img_break_tok_id < 0:
+                    self.img_break_tok_id = self.get_mistral_token_id("[IMG_BREAK]")
+        else:
+            raise ValueError(f"Unsupported model type: {self.hparams['model_type']}")
+        logger.info(f"Image break token id: {self.img_break_tok_id}")
+
+    def get_token_id(self, token: str) -> int:
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+            added_tokens_decoder = json.load(f).get('added_tokens_decoder') or {}
+            for id_, token_data in added_tokens_decoder.items():
+                if token_data.get("content") == token:
+                    return int(id_)
+            # fallthrough to tokenizer.json
+        with open(self.dir_model / "tokenizer.json", "r", encoding="utf-8") as f:
+            tokenizer_json = json.load(f)
+            for token_data in tokenizer_json["added_tokens"]:
+                if token_data["content"] == token:
+                    return int(token_data["id"])
+        raise ValueError(f"Token '{token}' not found in tokenizer config.")
+
+    def get_mistral_token_id(self, token: str) -> int:
+        # mistral native format ships tekken.json or a versioned spm tokenizer
+        tekken_file = self.dir_model / "tekken.json"
+        if tekken_file.is_file():
+            with open(tekken_file, "r", encoding="utf-8") as f:
+                data = json.load(f)
+            for entry in data.get("special_tokens", []):
+                if entry.get("token_str") == token:
+                    return int(entry["rank"])
+        tokenizer_json_file = self.dir_model / "tokenizer.json"
+        if tokenizer_json_file.is_file():
+            with open(tokenizer_json_file, "r", encoding="utf-8") as f:
+                data = json.load(f)
+            for entry in data.get("added_tokens", []):
+                if entry.get("content") == token:
+                    return int(entry["id"])
+        raise ValueError(f"Token '{token}' not found in mistral tokenizer files.")
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        if hparams.get("model_type") == "pixtral":
+            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PIXTRAL)
+            self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
+
+            # hidden_act
+            if hparams["hidden_act"] == "silu":
+                self.gguf_writer.add_vision_use_silu(True)
+            elif hparams["hidden_act"] == "gelu":
+                self.gguf_writer.add_vision_use_gelu(True)
+            else:
+                raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}")
+
+            # spatial_merge_size
+            if "spatial_merge_size" in self.global_config:
+                self.gguf_writer.add_vision_spatial_merge_size(self.global_config["spatial_merge_size"])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = (
+            self.hparams["num_attention_heads"] if not self.is_mistral_format else self.find_vparam(["num_attention_heads"])
+        )
+        n_kv_head = n_head
+
+        valid_prefixes = (
+            "multi_modal_projector.",
+            "vision_tower.",
+            "vision_encoder.",
+            "vision_language_adapter.",
+            "patch_merger.",
+            "pre_mm_projector_norm",
+        )
+
+        if any(name.startswith(prefix) for prefix in valid_prefixes):
+            # process vision tensors
+            if name.endswith(("q_proj.weight", "q_proj.bias")) and not self.is_mistral_format:
+                data_torch = LlamaModel.permute(data_torch, n_head, n_head)
+            if name.endswith(("k_proj.weight", "k_proj.bias")) and not self.is_mistral_format:
+                data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
+            yield from super().modify_tensors(data_torch, name, bid)
+            return
+
+        embed_key = "embed_tokens.weight" if not self.is_mistral_format else "tok_embeddings.weight"
+        if self.img_break_tok_id > 0 and embed_key in name:
+            logger.info(f"Extracting [IMG_BREAK] token embedding from {name}")
+            # for pixtral model, we need to extract the [IMG_BREAK] token embedding
+            img_break_embd = data_torch[self.img_break_tok_id]
+            name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK]
+            yield from super().modify_tensors(img_break_embd, name, bid)
+
+        return # skip other tensors