Fix layer order.

convert_grok.py

@@ -126,7 +126,7 @@ def get_weights(fn):
 def quantize_q8_0(tensor: torch.Tensor) -> torch.CharTensor:
     # equivalent to ggml_quantize_q8_0 in ggml.c
     assert tensor.shape[1] % GGML_QK8_0 == 0
-    tensor = tensor.view(-1, GGML_QK8_0)
+    tensor = tensor.reshape(-1, GGML_QK8_0)
     scale = tensor.abs().max(dim=-1, keepdim=True).values / ((1 << 7) - 1)
     tensor = (tensor / scale).round().clamp(min=-128, max=127).char()
     # add scale into each block
@@ -152,7 +152,7 @@ def quantize_q4_0(tensor: torch.Tensor) -> torch.CharTensor:
 def quantize_q4_1(tensor: torch.Tensor) -> torch.CharTensor:
     # equivalent to ggml_quantize_q4_1 in ggml.c
     assert tensor.shape[1] % GGML_QK4_1 == 0
-    tensor = tensor.view(-1, GGML_QK4_1)
+    tensor = tensor.reshape(-1, GGML_QK4_1)
     abs_max_indices = tensor.max(dim=-1, keepdim=True).indices
     max_values = torch.take_along_dim(tensor, abs_max_indices, dim=-1)
     abs_min_indices = tensor.min(dim=-1, keepdim=True).indices
@@ -185,15 +185,13 @@ def maybe_quantize_tensor(tensor, ggml_type):
         raise NotImplementedError(f"Cannot quantize tensor of dtype {tensor.dtype} ({ggml_type})")
 
 
-def get_dtype_and_ggml_type(tensor, ggml_type):
-    if tensor.ndim in (2, 3):
+def get_dtype_and_ggml_type(name, tensor, ggml_type):
+    if tensor.ndim in (2, 3) and "ffn_gate_inp" not in name:
         if tensor.shape[1] % GGML_QK8_0 == 0:
             return np.int8, ggml_type
         else:
             return np.float16, gguf.GGMLQuantizationType.F16
     else:
-        # 1d weight: convert it to float32
-        assert tensor.ndim == 1, tensor
         return np.float32, gguf.GGMLQuantizationType.F32
 
 
@@ -205,7 +203,7 @@ def dump_state_dict(f, ggml_type, input_dir, config):
     for idx, name in enumerate(weight_names):
         weight, scales = get_weights(f"{input_dir}/tensor{idx:05}_000")
         meta_tensor = convert_weight(name, weight, scales, config, device="meta")
-        dtype, tensor_ggml_type = get_dtype_and_ggml_type(meta_tensor, ggml_type)
+        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)
         f.add_tensor_info(
             f"{name}.weight",
@@ -227,7 +225,7 @@ def dump_state_dict(f, ggml_type, input_dir, config):
     for name in weight_names:
         weight, scales = weights.pop(name)
         tensor = convert_weight(name, weight, scales, config)
-        _, tensor_ggml_type = get_dtype_and_ggml_type(tensor, ggml_type)
+        _, tensor_ggml_type = get_dtype_and_ggml_type(name, tensor, ggml_type)
         array = maybe_quantize_tensor(tensor, tensor_ggml_type).numpy()
 
         logging.info(
@@ -317,7 +315,10 @@ def get_weight_names(num_hidden_layers=64):
         gguf.MODEL_TENSOR.FFN_GATE_INP,
     )
 
-    for bid in range(num_hidden_layers):
+    layers = [str(bid) for bid in range(64)]
+    layers.sort()  # Lexicographic sort: 0 < 1 < 10 < 11 ... < 2 < 20 < ...
+
+    for bid in layers[:num_hidden_layers]:
         for key in layer:
             weight_names.append(gguf.TENSOR_NAMES[key].format(bid=bid))
 
@@ -333,7 +334,6 @@ def ffn_size(emb_size, widening_factor):
         return _ffn_size
 
     config = {
-        "vocab_size": 128 * 1024,
         "hidden_act": "gelu",
         "pad_token_id": 0,
         "eos_token_id": 2,
@@ -366,8 +366,7 @@ def ffn_size(emb_size, widening_factor):
 
     f = gguf.GGUFWriter(args.save_path, "grok", endianess=gguf.GGUFEndian.LITTLE)
 
-    f.add_name("grok")
-    f.add_vocab_size(config.vocab_size)
+    f.add_name("grok-1")
     f.add_context_length(config.max_position_embeddings)
     f.add_embedding_length(config.hidden_size)
     f.add_block_count(config.num_hidden_layers)
@@ -389,6 +388,8 @@ def ffn_size(emb_size, widening_factor):
     f.add_token_scores(scores)
     f.add_token_types(toktypes)
 
+    f.add_quantization_version(ggml_type)
+
     dump_state_dict(f, ggml_type, args.input_dir, config)
     f.close()