From 13c75c21eb5eb0785007b5f3b7be3d1a5a61af14 Mon Sep 17 00:00:00 2001
From: Ashish <1856117+ashishdatta@users.noreply.github.com>
Date: Sun, 14 Apr 2024 14:28:12 -0700
Subject: [PATCH] Proper check for None type for new_name to avoid crash;
 formatting; revert change to base class `write_tensors()`

---
 convert-hf-to-gguf.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py
index 4b03adad5..3a73cce97 100755
--- a/convert-hf-to-gguf.py
+++ b/convert-hf-to-gguf.py
@@ -164,7 +164,7 @@ class Model(ABC):
                 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:
+            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}, {old_dtype} --> {data.dtype}")
@@ -1264,6 +1264,7 @@ class StableLMModel(Model):
             print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
 
             self.gguf_writer.add_tensor(new_name, data)
+
     def _stack_qk_norm(self, block_count, name, tensor_map, n_head, norms, n_dims, layer_name="q_layernorm"):
         for bid in range(block_count):
             datas = []
@@ -1275,15 +1276,15 @@ class StableLMModel(Model):
             data_dtype = data.dtype
             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()
             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)
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
 
             print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")