dflash: refactor draft model conversion (#25110)

* dflash: refactor draft model conversion

* apply fix for eagle3 convert

conversion/llama.py

@@ -73,7 +73,7 @@ class LlamaModel(TextModel):
             target_num_layers = target_config["num_hidden_layers"]
             target_layers = [2, target_num_layers // 2, target_num_layers - 3]
             logger.info(f"EAGLE-3: target_layers = {target_layers} (target model has {target_num_layers} layers)")
-            self.gguf_writer.add_array(f"{self.gguf_writer.arch}.target_layers", target_layers)
+            self.gguf_writer.add_target_layers(target_layers)
 
             # target_hidden_size: prefer eagle3 config, fallback to target config
             if eagle3_raw_config.get("target_hidden_size") is not None:
@@ -83,12 +83,12 @@ class LlamaModel(TextModel):
                 target_hidden_size = target_config["hidden_size"]
                 src = "target model config"
             logger.info(f"EAGLE-3: target_hidden_size = {target_hidden_size} (from {src})")
-            self.gguf_writer.add_uint32(f"{self.gguf_writer.arch}.target_hidden_size", target_hidden_size)
+            self.gguf_writer.add_target_hidden_size(target_hidden_size)
 
             # norm_before_residual (RedHat-style eagle3 specific)
             norm_before_residual = eagle3_raw_config.get("norm_before_residual", False)
             logger.info(f"EAGLE-3: norm_before_residual = {norm_before_residual}")
-            self.gguf_writer.add_bool(f"{self.gguf_writer.arch}.norm_before_residual", norm_before_residual)
+            self.gguf_writer.add_norm_before_residual(norm_before_residual)
 
     def set_vocab(self):
         # eagle3: use tokenizer from target model if provided

conversion/qwen.py

@@ -643,21 +643,21 @@ class DFlashModel(Qwen3Model):
         super().set_vocab()
         self.dir_model = original_dir
 
+        mask_token_id = self.hparams.get("dflash_config", {}).get("mask_token_id")
+        if mask_token_id is not None:
+            self.gguf_writer.add_mask_token_id(mask_token_id)
+
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
 
         block_size = self.hparams.get("block_size", 16)
-        self.gguf_writer.add_uint32(f"{self.gguf_writer.arch}.block_size", block_size)
+        self.gguf_writer.add_block_size(block_size)
         dflash_config = self.hparams.get("dflash_config", {})
 
         target_layer_ids = dflash_config.get("target_layer_ids", [])
         if target_layer_ids:
             extract_layer_ids = [i + 1 for i in target_layer_ids]
-            self.gguf_writer.add_array(f"{self.gguf_writer.arch}.target_layers", extract_layer_ids)
-
-        mask_token_id = dflash_config.get("mask_token_id", None)
-        if mask_token_id is not None:
-            self.gguf_writer.add_mask_token_id(mask_token_id)
+            self.gguf_writer.add_target_layers(extract_layer_ids)
 
         use_sliding_window = self.hparams.get("use_sliding_window", False)
         sliding_window = self.hparams.get("sliding_window")
@@ -667,13 +667,9 @@ class DFlashModel(Qwen3Model):
             self.gguf_writer.add_sliding_window(sliding_window)
             self.gguf_writer.add_sliding_window_pattern(is_swa)
 
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name == "fc.weight":
-            yield (name, data_torch)
-            return
-        if name == "hidden_norm.weight":
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ENC_OUTPUT_NORM), data_torch)
-            return
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
         if not name.startswith("model."):
             name = "model." + name
-        yield from super().modify_tensors(data_torch, name, bid)
+        return super().filter_tensors((name, gen))

gguf-py/gguf/constants.py

@@ -156,6 +156,7 @@ class Keys:
         DENSE_FEAT_OUT_SIZE               = "{arch}.{dense}_feat_out"
         TARGET_LAYERS                     = "{arch}.target_layers"
         TARGET_HIDDEN_SIZE                = "{arch}.target_hidden_size"
+        BLOCK_SIZE                        = "{arch}.block_size"
         NORM_BEFORE_RESIDUAL              = "{arch}.norm_before_residual"
 
     class Attention:

gguf-py/gguf/gguf_writer.py

@@ -940,6 +940,18 @@ class GGUFWriter:
     def add_sliding_window(self, value: int) -> None:
         self.add_uint32(Keys.Attention.SLIDING_WINDOW.format(arch=self.arch), value)
 
+    def add_block_size(self, value: int) -> None:
+        self.add_uint32(Keys.LLM.BLOCK_SIZE.format(arch=self.arch), value)
+
+    def add_target_layers(self, value: Sequence[int]) -> None:
+        self.add_array(Keys.LLM.TARGET_LAYERS.format(arch=self.arch), value)
+
+    def add_target_hidden_size(self, value: int) -> None:
+        self.add_uint32(Keys.LLM.TARGET_HIDDEN_SIZE.format(arch=self.arch), value)
+
+    def add_norm_before_residual(self, value: bool) -> None:
+        self.add_bool(Keys.LLM.NORM_BEFORE_RESIDUAL.format(arch=self.arch), value)
+
     def add_attention_scale(self, value: float) -> None:
         self.add_float32(Keys.Attention.SCALE.format(arch=self.arch), value)
 

gguf-py/gguf/tensor_mapping.py

@@ -1283,6 +1283,11 @@ class TensorNameMap:
         MODEL_TENSOR.ENC_OUTPUT_NORM: (
             "encoder.final_layer_norm", # t5
             "layer_norm",               # neobert
+            "model.hidden_norm",        # dflash
+        ),
+
+        MODEL_TENSOR.FC: (
+            "model.fc", # dflash
         ),
 
         MODEL_TENSOR.CLS: (