Write tensors in layer order.

convert_grok.py

@@ -197,12 +197,20 @@ def get_dtype_and_ggml_type(name, tensor, ggml_type):
 
 
 def dump_state_dict(f, ggml_type, input_dir, config):
-    weight_names = get_weight_names(config.num_hidden_layers)
     weights = {}
 
-    # First operate on meta tensors to find shapes and dtypes for GGUF header.
-    for idx, name in enumerate(weight_names):
-        weight, scales = get_weights(f"{input_dir}/tensor{idx:05}_000")
+    # Load weights in file order (mmap'ed).
+    for idx, name in enumerate(get_weight_names(config.num_hidden_layers)):
+        weights[name] = get_weights(f"{input_dir}/tensor{idx:05}_000")
+
+    logging.debug("Loaded %i files", len(weights))
+
+    # But write in layer order.
+    weight_names = get_weight_names(config.num_hidden_layers, lexicographic=False)
+
+    # Operate on meta tensors to find shapes and dtypes for GGUF header.
+    for name in weight_names:
+        weight, scales = weights[name]
         meta_tensor = convert_weight(name, weight, scales, config, device="meta")
         dtype, tensor_ggml_type = get_dtype_and_ggml_type(name, meta_tensor, ggml_type)
         quantized_meta_tensor = maybe_quantize_tensor(meta_tensor, tensor_ggml_type)
@@ -213,8 +221,6 @@ def dump_state_dict(f, ggml_type, input_dir, config):
             quantized_meta_tensor.nbytes,
             tensor_ggml_type,
         )
-        weights[name] = weight, scales
-    logging.debug("Loaded %i files", len(weight_names))
 
     f.write_header_to_file()
     f.write_kv_data_to_file()
@@ -244,7 +250,7 @@ def dump_state_dict(f, ggml_type, input_dir, config):
     except NameError:
         pass
 
-    if len(tensor_info) != len(weight_names):
+    if weights:
         logging.warning("Not all tensors are converted")
 
 
@@ -293,8 +299,10 @@ def extract_vocabulary_from_model(vocab):
     return tokens, scores, toktypes
 
 
-def get_weight_names(num_hidden_layers=64):
-    """Return Grok-1 weight names, in the order in which they are in the tensor#####_000 files."""
+def get_weight_names(num_hidden_layers=64, lexicographic=True):
+    """Return Grok-1 weight names.
+
+    If `lexicographic` is set, the order is as in the tensor#####_000 files."""
 
     weight_names = [
         gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.TOKEN_EMBD],
@@ -317,7 +325,10 @@ def get_weight_names(num_hidden_layers=64):
     )
 
     layers = [str(bid) for bid in range(64)]
-    layers.sort()  # Lexicographic sort: 0 < 1 < 10 < 11 ... < 2 < 20 < ...
+
+    if lexicographic:
+        # Lexicographic sort: 0 < 1 < 10 < 11 ... < 2 < 20 < ...
+        layers.sort()
 
     for bid in layers[:num_hidden_layers]:
         for key in layer: