convert-*.py: Add source uuid generation

convert_hf_to_gguf.py

@@ -10,6 +10,8 @@ import json
 import os
 import re
 import sys
+import uuid
+import hashlib
 from enum import IntEnum
 from pathlib import Path
 from hashlib import sha256
@@ -255,6 +257,19 @@ class Model:
 
         return False
 
+    def generate_source_tensors_uuid(self) -> str:
+        uuidv5_sha1 = hashlib.sha1()
+        uuidv5_sha1.update(uuid.UUID('ef001206-dadc-5f6d-a15f-3359e577d4e5').bytes)
+
+        for name, data_torch in self.get_tensors():
+            # we don't need these
+            if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
+                continue
+            data: np.ndarray = data_torch.to(torch.float64).squeeze().numpy()
+            uuidv5_sha1.update(data.tobytes('C'))
+
+        return str(uuid.UUID(bytes=uuidv5_sha1.digest()[:16], version=5))
+
     def prepare_tensors(self):
         max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
 
@@ -381,11 +396,15 @@ class Model:
                 # output in the same directory as the model by default
                 self.fname_out = self.dir_model / f"{fname_default}.gguf"
 
+        # Upon missing source model uuid, generate uuid based on source tensor content
+        if not vocab_only and self.metadata.source_uuid is None:
+            self.metadata.source_uuid = self.generate_source_tensors_uuid()
+            logger.info(f"generating general.source_uuid: {self.metadata.source_uuid}")
+
         # Upon missing model uuid, generate uuid based on tensor content
         if not vocab_only and self.metadata.uuid is None:
             self.metadata.uuid = self.gguf_writer.generate_tensors_uuid()
-            max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
+            logger.info(f"generating general.uuid: {self.metadata.uuid}")
-            logger.info(f"{f'%-{max_name_len}s' % f'generating general.uuid'} {self.metadata.uuid}")
 
         self.set_type()
 
@@ -3468,6 +3487,7 @@ class LazyTorchTensor(gguf.LazyBase):
     _dtype_map: dict[torch.dtype, type] = {
         torch.float16: np.float16,
         torch.float32: np.float32,
+        torch.float64: np.float64,
     }
 
     # used for safetensors slices