fix conflicts

convert-llama-h5-to-gguf.py

@@ -23,7 +23,6 @@ def permute(weights: NDArray, n_head: int) -> NDArray:
                    .swapaxes(1, 2)
                    .reshape(weights.shape))
 
-
 def count_model_parts(dir_model: str) -> int:
     num_parts = 0
     for filename in os.listdir(dir_model):
@@ -34,7 +33,6 @@ def count_model_parts(dir_model: str) -> int:
         print("gguf: found " + str(num_parts) + " model parts")
     return num_parts
 
-
 if len(sys.argv) < 3:
     print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
     print("  ftype == 0 -> float32")
@@ -188,7 +186,7 @@ else:
     )
 
 for part_name in part_names:
-    print("gguf: loading model part '" + part_name + "'")
+    print("gguf: loading model part '"+ part_name + "'")
     model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
 
     for name in model_part.keys():
@@ -206,7 +204,7 @@ for part_name in part_names:
 
         # permute these
         if name.endswith(".q_proj.weight") or name.endswith(".k_proj.weight"):
-            data = permute(data, head_count)
+            data = permute(data,head_count)
 
         # map tensor names
         if name.endswith(".weight") and name[:-7] in tensor_map:
@@ -214,7 +212,7 @@ for part_name in part_names:
         elif name.endswith(".bias") and name[:-5] in tensor_map:
             name = tensor_map[name[:-5]] + ".bias"
         else:
-            print("Can not map tensor '" + name + "'")
+            print( "Can not map tensor '" + name + "'" )
             sys.exit()
 
         n_dims = len(data.shape)
@@ -255,60 +253,56 @@ else:
     )
 
 for part_name in part_names:
-    print("gguf: loading model part '" + part_name + "'")
+    print("gguf: loading model part '"+ part_name + "'")
     model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
 
     for name in model_part.keys():
         data = model_part[name]
 
-<< << << < HEAD
-   n_dims = len(data.shape)
-    data_dtype = data.dtype
-== == == =
-   old_dtype = data.dtype
     
-    # we don't need these
-    if name.endswith(".rotary_emb.inv_freq"):
-        continue
->>>>>> > 17800cd80fec468411481dc34a51d42a936442f1
+        old_dtype = data.dtype
 
-   # convert any unsupported data types to float32
-   if data.dtype != torch.float16 and data.dtype != torch.float32:
-        data = data.to(torch.float32)
+        # we don't need these
+        if name.endswith(".rotary_emb.inv_freq"):
+            continue
 
-    data = data.squeeze().numpy()
+        # convert any unsupported data types to float32
+        if data.dtype != torch.float16 and data.dtype != torch.float32:
+            data = data.to(torch.float32)
 
-    # permute these
-    if name.endswith(".q_proj.weight") or name.endswith(".k_proj.weight"):
-        data = permute(data, head_count)
+        data = data.squeeze().numpy()
 
-    # map tensor names
-    if name.endswith(".weight") and name[:-7] in tensor_map:
-        name = tensor_map[name[:-7]] + ".weight"
-    elif name.endswith(".bias") and name[:-5] in tensor_map:
-        name = tensor_map[name[:-5]] + ".bias"
-    else:
-        print("Can not map tensor '" + name + "'" )
-        sys.exit()
+        # permute these
+        if name.endswith(".q_proj.weight") or name.endswith(".k_proj.weight"):
+            data = permute(data, head_count)
 
-    n_dims = len(data.shape)
-    data_dtype = data.dtype
+        # map tensor names
+        if name.endswith(".weight") and name[:-7] in tensor_map:
+            name = tensor_map[name[:-7]] + ".weight"
+        elif name.endswith(".bias") and name[:-5] in tensor_map:
+            name = tensor_map[name[:-5]] + ".bias"
+        else:
+            print( "Can not map tensor '" + name + "'" )
+            sys.exit()
 
-    # if f32 desired, convert any float16 to float32
-    if ftype == 0 and data.dtype == np.float16:
-        data = data.astype(np.float32)
+        n_dims = len(data.shape)
+        data_dtype = data.dtype 
 
-    # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-    if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-        data = data.astype(np.float32)
+        # if f32 desired, convert any float16 to float32
+        if ftype == 0 and data.dtype == np.float16:
+            data = data.astype(np.float32)
 
-    # if f16 desired, convert any float32 2-dim weight tensors to float16
-    if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
-        data = data.astype(np.float16)
+        # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+        if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+            data = data.astype(np.float32)
 
-    print(name + ", shape " + str(len(data.shape)) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+        # if f16 desired, convert any float32 2-dim weight tensors to float16
+        if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+            data = data.astype(np.float16)
 
-    gguf_writer.write_tensor_to_file(data)
+        print( name + ", shape " + str(len(data.shape)) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+
+        gguf_writer.write_tensor_to_file(data)
 
 gguf_writer.close()