[misc] update pre-commit and run all files (#4752)

* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format

colossalai/fx/passes/concrete_info_prop.py

@@ -1,5 +1,5 @@
 from dataclasses import asdict
-from typing import Any, Dict, List, NamedTuple, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import torch
 import torch.fx
@@ -85,10 +85,10 @@ class ConcreteInfoProp(torch.fx.Interpreter):
         self._is_proped = True
         result, meta_info = super().run_node(n)
 
-        n.meta = {**n.meta, **asdict(meta_info)}    # extend MetaInfo to `n.meta`
+        n.meta = {**n.meta, **asdict(meta_info)}  # extend MetaInfo to `n.meta`
         # TODO: the attribute node_size should be removed in the future
-        setattr(n, 'node_size', n.meta.get('fwd_mem_tmp', 0) + n.meta.get('fwd_mem_out', 0))
-        n.meta['type'] = type(result)
+        setattr(n, "node_size", n.meta.get("fwd_mem_tmp", 0) + n.meta.get("fwd_mem_out", 0))
+        n.meta["type"] = type(result)
 
         # retain the autograd graph
         for param in self.module.parameters():
@@ -98,7 +98,7 @@ class ConcreteInfoProp(torch.fx.Interpreter):
 
     # Main Node running APIs
     @compatibility(is_backward_compatible=True)
-    def placeholder(self, target: 'Target', args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
+    def placeholder(self, target: "Target", args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
         """
         Execute a ``placeholder`` node. Note that this is stateful:
         ``Interpreter`` maintains an internal iterator over
@@ -119,7 +119,7 @@ class ConcreteInfoProp(torch.fx.Interpreter):
         return super().placeholder(target, args, kwargs), GraphInfo()
 
     @compatibility(is_backward_compatible=True)
-    def get_attr(self, target: 'Target', args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
+    def get_attr(self, target: "Target", args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
         """
         Execute a ``get_attr`` node. Will retrieve an attribute
         value from the ``Module`` hierarchy of ``self.module``.
@@ -138,7 +138,7 @@ class ConcreteInfoProp(torch.fx.Interpreter):
         return super().get_attr(target, args, kwargs), GraphInfo()
 
     @compatibility(is_backward_compatible=True)
-    def call_function(self, target: 'Target', args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
+    def call_function(self, target: "Target", args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
         """
         Execute a ``call_function`` node with meta tensor and return the result and its meta profile.
 
@@ -157,7 +157,7 @@ class ConcreteInfoProp(torch.fx.Interpreter):
         return profile_function(target, self.device)(*args, **kwargs)
 
     @compatibility(is_backward_compatible=True)
-    def call_method(self, target: 'Target', args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
+    def call_method(self, target: "Target", args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
         """
         Execute a ``call_method`` node with meta tensor and return the result and its meta profile.
 
@@ -175,7 +175,7 @@ class ConcreteInfoProp(torch.fx.Interpreter):
         return profile_method(target, self.device)(*args, **kwargs)
 
     @compatibility(is_backward_compatible=True)
-    def call_module(self, target: 'Target', args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
+    def call_module(self, target: "Target", args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
         """
         Execute a ``call_module`` node with meta tensor and return the result and its meta profile.
 
@@ -197,7 +197,7 @@ class ConcreteInfoProp(torch.fx.Interpreter):
         return profile_module(submod, self.device)(*args, **kwargs)
 
     @compatibility(is_backward_compatible=True)
-    def output(self, target: 'Target', args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
+    def output(self, target: "Target", args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
         """
         Execute an ``output`` node. This really just retrieves
         the value referenced by the ``output`` node and returns it.
@@ -228,7 +228,7 @@ class ConcreteInfoProp(torch.fx.Interpreter):
         """
         return self.run(*args)
 
-    def summary(self, unit: str = 'MB') -> str:
+    def summary(self, unit: str = "MB") -> str:
         """
         Summarizes the memory and FLOPs statistics of the `GraphModule` in
         tabular format. Note that this API requires the ``tabulate`` module
@@ -238,9 +238,11 @@ class ConcreteInfoProp(torch.fx.Interpreter):
         try:
             from tabulate import tabulate
         except ImportError:
-            print("`summary` relies on the library `tabulate`, "
-                  "which could not be found on this machine. Run `pip "
-                  "install tabulate` to install the library.")
+            print(
+                "`summary` relies on the library `tabulate`, "
+                "which could not be found on this machine. Run `pip "
+                "install tabulate` to install the library."
+            )
 
         assert self._is_proped, "Please call `interp.run(input)` before calling `interp.summary()`."
 
@@ -249,10 +251,10 @@ class ConcreteInfoProp(torch.fx.Interpreter):
 
         def mem_repr(mem: int) -> str:
             unit_divisor_map = {
-                'kb': 1024,
-                'mb': 1024**2,
-                'gb': 1024**3,
-                'tb': 1024**4,
+                "kb": 1024,
+                "mb": 1024**2,
+                "gb": 1024**3,
+                "tb": 1024**4,
             }
             return f"{mem / unit_divisor_map[unit.lower()]:.2f} {unit.upper()}"
 
@@ -261,30 +263,32 @@ class ConcreteInfoProp(torch.fx.Interpreter):
 
         for node in self.module.graph.nodes:
             node: Node
-            node_summaries.append([
-                node.op,
-                str(node),
-                time_repr(node.meta['fwd_time']),
-                time_repr(node.meta['bwd_time']),
-                node.meta['save_fwd_in'],
-                mem_repr(node.meta['fwd_mem_out']),
-                mem_repr(node.meta['fwd_mem_tmp']),
-                mem_repr(node.meta['bwd_mem_out']),
-                mem_repr(node.meta['bwd_mem_tmp']),
-            ])
+            node_summaries.append(
+                [
+                    node.op,
+                    str(node),
+                    time_repr(node.meta["fwd_time"]),
+                    time_repr(node.meta["bwd_time"]),
+                    node.meta["save_fwd_in"],
+                    mem_repr(node.meta["fwd_mem_out"]),
+                    mem_repr(node.meta["fwd_mem_tmp"]),
+                    mem_repr(node.meta["bwd_mem_out"]),
+                    mem_repr(node.meta["bwd_mem_tmp"]),
+                ]
+            )
 
         # Use the ``tabulate`` library to create a well-formatted table
         # presenting our summary information
         headers: List[str] = [
-            'Op type',
-            'Op',
-            'Forward time',
-            'Backward time',
-            'SAVE_FWD_IN',
-            'FWD_OUT',
-            'FWD_TMP',
-            'BWD_OUT',
-            'BWD_TMP',
+            "Op type",
+            "Op",
+            "Forward time",
+            "Backward time",
+            "SAVE_FWD_IN",
+            "FWD_OUT",
+            "FWD_TMP",
+            "BWD_OUT",
+            "BWD_TMP",
         ]
 
-        return tabulate(node_summaries, headers=headers, stralign='right')
+        return tabulate(node_summaries, headers=headers, stralign="right")