Don't split MoE weights.

As per ggerganov#7058 (comment).
This helps avoid a memcopy when running.

convert_grok.py

@@ -185,14 +185,14 @@ def maybe_quantize_tensor(tensor, ggml_type):
 
 
 def get_dtype_and_ggml_type(tensor, ggml_type):
-    if tensor.ndim == 2:
+    if tensor.ndim in (2, 3):
         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
+        assert tensor.ndim == 1, tensor
         return np.float32, gguf.GGMLQuantizationType.F32
 
 
@@ -236,15 +236,15 @@ def dump_state_dict(f, weight_names, model_files, ggml_type, config):
             cache.update(state_dict)
         tensor = cache.pop(key)
         _, tensor_ggml_type = get_dtype_and_ggml_type(tensor, ggml_type)
-        tensor = maybe_quantize_tensor(tensor, tensor_ggml_type)
+        array = maybe_quantize_tensor(tensor, tensor_ggml_type).numpy()
 
-        array = tensor.numpy()
         print(
-            f"dumping {key}: {tensor_ggml_type.name}/{array.dtype}, {array.shape}, {array.nbytes} bytes"
+            f"dumping {key}:",
+            f"{tensor_ggml_type.name}/{array.dtype}, {list(tensor.shape)}, {array.nbytes} bytes",
         )
         f.write_tensor_data(array)
 
-        tensor_info.append((key, tensor.shape, tensor_ggml_type.name))
+        tensor_info.append((key, list(tensor.shape), tensor_ggml_type.name))
 
     try:
         print(tabulate(tensor_info, headers=["name", "shape", "dtype"], tablefmt="psql"))
@@ -282,15 +282,10 @@ def convert_weight(tensor_name, weight, scales, experts, dtype=torch.float32, de
     if len(weight.shape) >= 2 and "token_embd" not in tensor_name:
         weight = weight.transpose(-1, -2)
 
-    if tensor_name.endswith("ffn_gate_inp.weight"):
+    if tensor_name.endswith("ffn_gate_inp.weight") or tensor_name.endswith("_exps.weight"):
         result[tensor_name] = weight[experts]  # gather.
     elif "experts" not in tensor_name:
         result[tensor_name] = weight
-    else:
-        # split moe
-        for i, expert in enumerate(experts):
-            key = tensor_name.replace("experts", str(i))
-            result[key] = weight[expert]
 
     return result
 
@@ -328,14 +323,10 @@ def extract_vocabulary_from_model(vocab):
 def get_weight_names(config):
     weight_names = ["token_embd.weight"]
     for i in range(config.num_hidden_layers):
-        for j in range(config.num_experts):
-            weight_names += [
-                f"blk.{i}.ffn_gate.{j}.weight",
-                f"blk.{i}.ffn_down.{j}.weight",
-                f"blk.{i}.ffn_up.{j}.weight",
-            ]
-
         weight_names += [
+            f"blk.{i}.ffn_gate_exps.weight",
+            f"blk.{i}.ffn_down_exps.weight",
+            f"blk.{i}.ffn_up_exps.weight",
             f"blk.{i}.attn_k.weight",
             f"blk.{i}.attn_output.weight",
             f"blk.{i}.attn_q.weight",
@@ -399,9 +390,9 @@ def ffn_size(emb_size, widening_factor):
     ]
     for i in range(config.num_hidden_layers):
         tensor_names += [
-            f"blk.{i}.ffn_gate.experts.weight",
-            f"blk.{i}.ffn_down.experts.weight",
-            f"blk.{i}.ffn_up.experts.weight",
+            f"blk.{i}.ffn_gate_exps.weight",
+            f"blk.{i}.ffn_down_exps.weight",
+            f"blk.{i}.ffn_up_exps.weight",
             f"blk.{i}.attn_k.weight",
             f"blk.{i}.attn_output.weight",
             f"blk.{i}.attn_q.weight",