fix: warn user when using outdated compiled binary

aphrodite/_custom_ops.py

@@ -1,13 +1,14 @@
 import contextlib
+import functools
 from typing import List, Optional, Tuple, Type
 
 import torch
+from loguru import logger
 
 try:
     import aphrodite._C
 except ImportError as e:
-    from loguru import logger
-    logger.warning("Failed to import from vllm._C with %r", e)
+    logger.warning(f"Failed to import from aphrodite._C with {e}")
 
 with contextlib.suppress(ImportError):
     import aphrodite._moe_C
@@ -22,6 +23,25 @@ def is_custom_op_supported(op_name: str) -> bool:
     return op is not None
 
 
+def hint_on_error(fn):
+
+    @functools.wraps(fn)
+    def wrapper(*args, **kwargs):
+        try:
+            return fn(*args, **kwargs)
+        except AttributeError as e:
+            msg = (
+                f"Error in calling custom op {fn.__name__}: {e}\n"
+                f"Possibly you have built or installed an obsolete version of aphrodite.\n"
+                f"Please try a clean build and install of aphrodite,"
+                f"or remove old built files such as aphrodite/*.so and build/ ."
+            )
+            logger.error(msg)
+            raise e
+
+    return wrapper
+
+
 # activation ops
 def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
     torch.ops._C.silu_and_mul(out, x)
@@ -475,3 +495,24 @@ def dispatch_bgmv_low_level(
         h_out,
         y_offset,
     )
+
+
+# TODO: remove this later
+names_and_values = globals()
+names_and_values_to_update = {}
+# prepare variables to avoid dict size change during iteration
+k, v, arg = None, None, None
+fn_type = type(lambda x: x)
+for k, v in names_and_values.items():
+    # find functions that are defined in this file and have torch.Tensor
+    # in their annotations. `arg == "torch.Tensor"` is used to handle
+    # the case when users use `import __annotations__` to turn type
+    # hints into strings.
+    if isinstance(v, fn_type) \
+        and v.__code__.co_filename == __file__ \
+        and any(arg is torch.Tensor or arg == "torch.Tensor"
+                   for arg in v.__annotations__.values()):
+        names_and_values_to_update[k] = hint_on_error(v)
+
+names_and_values.update(names_and_values_to_update)
+del names_and_values_to_update, names_and_values, v, k, fn_type