[fx] metainfo_trace as an API. (#1873)

* [fx] metainfo_trace as an API.

* [fx] add return.

colossalai/fx/__init__.py

 from ._compatibility import compatibility, is_compatible_with_meta
 from .graph_module import ColoGraphModule
-from .passes import MetaInfoProp
+from .passes import MetaInfoProp, metainfo_trace
 from .tracer import ColoTracer, meta_trace, symbolic_trace

colossalai/fx/passes/__init__.py

 from .adding_split_node_pass import balanced_split_pass, split_with_split_nodes_pass
-from .shard_1d_pass import column_shard_linear_pass, row_shard_linear_pass
-from .meta_info_prop import MetaInfoProp
 from .concrete_info_prop import ConcreteInfoProp
+from .meta_info_prop import MetaInfoProp, metainfo_trace
+from .shard_1d_pass import column_shard_linear_pass, row_shard_linear_pass

colossalai/fx/passes/meta_info_prop.py

@@ -6,7 +6,7 @@ import torch.fx
 from torch.fx.node import Argument, Node, Target
 from torch.utils._pytree import tree_map
 
-from colossalai.fx._compatibility import compatibility
+from colossalai.fx._compatibility import compatibility, is_compatible_with_meta
 from colossalai.fx.profiler import (
     GraphInfo,
     activation_size,
@@ -315,3 +315,38 @@ class MetaInfoProp(torch.fx.Interpreter):
         ]
 
         return tabulate(node_summaries, headers=headers, stralign='right')
+
+
+def metainfo_trace(gm: torch.fx.GraphModule, *args, verbose: bool = False, unit: str = "MB", **kwargs) -> None:
+    """
+    MetaInfo tracing API
+
+    Given a ``GraphModule`` and a sample input, this API will trace the MetaInfo of a single training cycle,
+    and annotate them on ``gm.graph``.
+
+    Uses:
+        >>> model = ...
+        >>> gm = symbolic_trace(model)
+        >>> args = ...  # sample input to the ``GraphModule``
+        >>> metainfo_trace(gm, *args)
+
+    Args:
+        gm (torch.fx.GraphModule): The ``GraphModule`` to be annotated with MetaInfo.
+        verbose (bool, optional): Whether to show ``MetaInfoProp.summary()`. Defaults to False.
+        unit (str, optional): The unit of memory. Defaults to "MB".
+
+    Returns:
+        torch.fx.GraphModule: The ``GraphModule`` annotated with MetaInfo.
+    """
+    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+    interp = MetaInfoProp(gm.to(device))
+    if is_compatible_with_meta():
+        from colossalai.fx.profiler import MetaTensor
+        args = tree_map(lambda x: MetaTensor(x, fake_device=device), args)
+        kwargs = tree_map(lambda x: MetaTensor(x, fake_device=device), kwargs)
+    interp.propagate(*args, **kwargs)
+    if verbose:
+        interp.summary(unit)
+    gm.to('cpu')
+    del interp
+    return gm