convert-hf-to-gguf.py: print --> logger.debug or ValueError()

ggerganov · Apr 18, 2024 · 9e4cf37 · 9e4cf37
1 parent 70d4f42
commit 9e4cf37
Showing 1 changed file with 9 additions and 13 deletions.
diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py
@@ -1236,8 +1236,7 @@ def write_tensors(self):
             # map tensor names
             new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
             if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
+                raise ValueError(f"Can not map tensor {name!r}")
 
             n_dims = len(data.shape)
             data_dtype = data.dtype
@@ -1254,7 +1253,7 @@ def write_tensors(self):
             if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and not new_name.endswith("_norm.weight") and n_dims == 2:
                 data = data.astype(np.float16)
 
-            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+            logger.debug(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
 
             self.gguf_writer.add_tensor(new_name, data)
 
@@ -1270,16 +1269,15 @@ def _stack_qk_norm(self, block_count, name, tensor_map, n_head, norms, n_dims, l
             merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight"
             new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias"))
             if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
+                raise ValueError(f"Can not map tensor {name!r}")
             if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")):
                 data = data.astype(np.float32)
 
             # if f16 desired, convert any float32 2-dim weight tensors to float16
             if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and not new_name.endswith("_norm.weight") and n_dims == 2:
                 data = data.astype(np.float16)
 
-            print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")
+            logger.debug(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")
 
             self.gguf_writer.add_tensor(new_name, data)
 
@@ -1827,19 +1825,17 @@ def write_tensors(self):
 
                             new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias"))
                             if new_name is None:
-                                print(f"Can not map tensor {name!r}")
-                                sys.exit()
+                                raise ValueError(f"Can not map tensor {name!r}")
 
-                            print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")
+                            logger.debug(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")
 
                             self.gguf_writer.add_tensor(new_name, data)
                 continue
 
             # map tensor names
             new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
             if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
+                raise ValueError(f"Can not map tensor {name!r}")
 
             n_dims = len(data.shape)
             data_dtype = data.dtype
@@ -1856,7 +1852,7 @@ def write_tensors(self):
             if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
                 data = data.astype(np.float16)
 
-            print(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}")
+            logger.debug(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}")
 
             self.gguf_writer.add_tensor(new_name, data)
 
@@ -2439,7 +2435,7 @@ def write_tensors(self):
             # lm_head is not used in llama.cpp, while autoawq will include this tensor in model
             # To prevent errors, skip loading lm_head.weight.
             if name == "lm_head.weight":
-                print(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
+                logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
                 continue
 
             old_dtype = data_torch.dtype