more info for preshard (mlc-ai#2027)

* When the pre-sharded version of a certain model is not available, the program will default back to the normal workflow without issuing any alert. Now, when someone attempts to convert to a pre-sharded model but cannot, the program will throw a warning message to inform users that it will revert to the standard model conversion process.

* format fix.

* black reformatted, i did not see any diff.

* black reformatted..

python/mlc_llm/support/preshard.py

@@ -1,4 +1,5 @@
 """Functions for pre-sharding weights"""
+import logging
 from typing import Any, Dict, List
 
 from tvm import IRModule
@@ -8,6 +9,8 @@
 from tvm.runtime import Device
 from tvm.target import Target
 
+logger = logging.getLogger("preshard")
+
 
 def _sharded_param_name(param_name, worker_id):
     return f"{param_name}_shard-{worker_id}"
@@ -93,10 +96,7 @@ def _compile_shard_funcs(mod: IRModule, device: Device):
 
 
 def apply_preshard(
-    quantize_map: Any,
-    named_params: Dict[str, nn.Parameter],
-    tensor_parallel_shards: int,
-    args: Any,
+    quantize_map: Any, named_params: Dict[str, nn.Parameter], tensor_parallel_shards: int, args: Any
 ):
     """Update quantize_map and named_params, create shard functions based on shard strategies."""
     model_config = args.model.config.from_file(args.config)
@@ -107,17 +107,24 @@ def apply_preshard(
     bb = relax.BlockBuilder()
     param_to_shard_func = {}
     shard_func_names = set()
+    has_shard_strategy = False
     for name, param in model.state_dict().items():
         shard_strategy = param.attrs.get("shard_strategy", None)
         if shard_strategy is not None:
+            has_shard_strategy = True
             _update_quantize_map(quantize_map, named_params, name, tensor_parallel_shards)
 
             # create shard functions
             param_to_shard_func[name] = shard_strategy.name
             if shard_strategy.name not in shard_func_names:
                 _create_shard_func(bb, param, tensor_parallel_shards)
                 shard_func_names.add(shard_strategy.name)
-
+    if not has_shard_strategy:
+        logger.warning(
+            "No parameters with 'shard_strategy' found."
+            "At least one parameter must have a 'shard_strategy' for presharding. "
+            "The model will continue to convert weights in a non-presharded manner."
+        )
     mod = bb.finalize()
     vm = _compile_shard_funcs(mod, args.device)