[legacy] clean up legacy code (#4743)

* [legacy] remove outdated codes of pipeline (#4692)

* [legacy] remove cli of benchmark and update optim (#4690)

* [legacy] remove cli of benchmark and update optim

* [doc] fix cli doc test

* [legacy] fix engine clip grad norm

* [legacy] remove outdated colo tensor (#4694)

* [legacy] remove outdated colo tensor

* [test] fix test import

* [legacy] move outdated zero to legacy (#4696)

* [legacy] clean up utils (#4700)

* [legacy] clean up utils

* [example] update examples

* [legacy] clean up amp

* [legacy] fix amp module

* [legacy] clean up gpc (#4742)

* [legacy] clean up context

* [legacy] clean core, constants and global vars

* [legacy] refactor initialize

* [example] fix examples ci

* [example] fix examples ci

* [legacy] fix tests

* [example] fix gpt example

* [example] fix examples ci

* [devops] fix ci installation

* [example] fix examples ci

colossalai/legacy/tensor/op_wrapper.py

@@ -0,0 +1,50 @@
+import functools
+from typing import Callable, Dict
+
+# Custom sharded ops
+_COLOSSAL_OPS: Dict[str, Callable] = {}
+
+
+def _register_colo_op(op, func):
+    global _COLOSSAL_OPS
+    _COLOSSAL_OPS[op] = func
+
+
+def colo_op_impl(func):
+    """
+    Provides a way for users to write their own custom operator. This
+    can be used to override existing ColoTensor operators or write a new
+    one not supported by ColoTensor. If the operator in question is covered
+    by ``__torch_function__`` dispatch and has a ColoTensor as any of its
+    parameters, the function provided will be invoked for that operator.
+
+    Example:
+        >>> @colo_op_impl(torch.nn.functional.linear)
+        >>> def my_custom_linear(types, args, kwargs, process_group):
+        >>>   ....
+        >>>
+        >>> input = torch.rand(10, 32)
+        >>> weight = ColoTensor(torch.rand(32, 16))
+        >>> bias = ColoTensor(torch.rand(16))
+        >>> # This will call `my_custom_linear` instead of the default.
+        >>> torch.nn.functional.linear(input, weight, bias)
+
+    The types, args and kwargs parameters are the same parameters that are
+    passed to ``__torch_function__`` dispatch API
+    (https://pytorch.org/docs/stable/notes/extending.html#extending-torch).
+
+    Args:
+        func(Callable): Torch function for which we want to provide a sharded
+            implementation (ex: torch.nn.functional.linear)
+    """
+
+    def decorator_sharded_func(wrapped_func):
+        _register_colo_op(func, wrapped_func)
+
+        @functools.wraps(wrapped_func)
+        def wrapper(*args, **kwargs):
+            return wrapped_func(*args, **kwargs)
+
+        return wrapper
+
+    return decorator_sharded_func