diff --git a/colossalai/fx/codegen/__init__.py b/colossalai/fx/codegen/__init__.py
new file mode 100644
index 000000000..9ade90e3f
--- /dev/null
+++ b/colossalai/fx/codegen/__init__.py
@@ -0,0 +1,3 @@
+from .activation_checkpoint_codegen import ActivationCheckpointCodeGen
+
+__all__ = ['ActivationCheckpointCodeGen']
\ No newline at end of file
diff --git a/colossalai/fx/codegen/activation_checkpoint_codegen.py b/colossalai/fx/codegen/activation_checkpoint_codegen.py
new file mode 100644
index 000000000..d31ddaed2
--- /dev/null
+++ b/colossalai/fx/codegen/activation_checkpoint_codegen.py
@@ -0,0 +1,364 @@
+import torch
+from typing import List, Callable, Any, Tuple, Dict
+from torch.fx.node import Node, Argument, map_arg, _type_repr, _get_qualified_name
+from torch.fx.graph import _Namespace, PythonCode, _custom_builtins, _is_from_torch, _format_target, magic_methods, CodeGen, _origin_type_map
+
+__all__ = ['ActivationCheckpointCodeGen']
+
+
+class ActivationCheckpointCodeGen(CodeGen):
+
+    def find_input_and_output_nodes(self, nodes: List[Node]):
+        """
+        Find the input and output node names which are not found in the given list of nodes.
+        """
+        input_nodes = []
+        output_nodes = []
+
+        # if a node has an input node which is not in the node list
+        # we treat that input node as the input of the checkpoint function
+        for node in nodes:
+            for input_node in node._input_nodes.keys():
+                node_repr = repr(input_node)
+                if input_node not in nodes and node_repr not in input_nodes:
+                    input_nodes.append(node_repr)
+
+        # if a node has a user node which is not in the node list
+        # we treat that user node as the node receiving the current node output
+        for node in nodes:
+            for output_node in node.users.keys():
+                node_repr = repr(node)
+                if output_node not in nodes and node_repr not in output_nodes:
+                    output_nodes.append(node_repr)
+
+        return input_nodes, output_nodes
+
+    def find_ckpt_regions(self, nodes: List[Node]):
+        """
+        Find the checkpoint regions given a list of consecutive nodes. The outputs will be list
+        of tuples, each tuple is in the form of (start_index, end_index).
+        """
+        ckpt_nodes = []
+        ckpt_regions = []
+        start = -1
+        end = -1
+        current_region = None
+
+        for idx, node in enumerate(nodes):
+            if hasattr(node, 'activation_checkpoint'):
+                act_ckpt_label = node.activation_checkpoint
+
+                # this activation checkpoint label is not set yet
+                # meaning this is the first node of the activation ckpt region
+                if current_region is None:
+                    current_region = act_ckpt_label
+                    start = idx
+
+                # if activation checkpoint has changed
+                # we restart the tracking
+                # e.g. node ckpt states = [ckpt1, ckpt2, ckpt2, ckpt2]
+                if act_ckpt_label != current_region:
+                    assert start != -1
+                    ckpt_regions.append((start, idx - 1))
+                    current_region = act_ckpt_label
+                    start = idx
+                    end = -1
+            elif current_region is not None and not hasattr(node, 'activation_checkpoint'):
+                # used to check the case below
+                # node ckpt states = [ckpt, ckpt, non-ckpt]
+                end = idx - 1
+                assert start != -1 and end != -1
+                ckpt_regions.append((start, end))
+                start = end = -1
+                current_region = None
+            else:
+                pass
+        return ckpt_regions
+
+    def gen_ckpt_fn_def(self, label, free_vars: List[str]) -> str:
+        """
+        Generate the checkpoint function definition
+        """
+        return f"def checkpoint_{label}({', '.join(free_vars)}):"
+
+    def gen_ckpt_output(self, output_vars: List[str]) -> str:
+        """
+        Generate the return statement for checkpoint region
+        """
+        return f"return {', '.join(output_vars)}"
+
+    def gen_ckpt_usage(self, label, input_vars, output_vars):
+        """
+        Generate the checkpoint function call code text
+        """
+        outputs = ', '.join(output_vars)
+        inputs = ', '.join(input_vars)
+        return f'{outputs} = torch.utils.checkpoint.checkpoint(checkpoint_{label}, {inputs})'
+
+    def _gen_python_code(self, nodes, root_module: str, namespace: _Namespace) -> PythonCode:
+        free_vars: List[str] = []
+        body: List[str] = []
+        globals_: Dict[str, Any] = {}
+        wrapped_fns: Dict[str, None] = {}
+
+        # Wrap string in list to pass by reference
+        maybe_return_annotation: List[str] = ['']
+
+        def add_global(name_hint: str, obj: Any):
+            """Add an obj to be tracked as a global.
+
+            We call this for names that reference objects external to the
+            Graph, like functions or types.
+
+            Returns: the global name that should be used to reference 'obj' in generated source.
+            """
+            if _is_from_torch(obj) and obj != torch.device:    # to support registering torch.device
+                # HACK: workaround for how torch custom ops are registered. We
+                # can't import them like normal modules so they must retain their
+                # fully qualified name.
+                return _get_qualified_name(obj)
+
+            # normalize the name hint to get a proper identifier
+            global_name = namespace.create_name(name_hint, obj)
+
+            if global_name in globals_:
+                assert globals_[global_name] is obj
+                return global_name
+            globals_[global_name] = obj
+            return global_name
+
+        # Pre-fill the globals table with registered builtins.
+        for name, (_, obj) in _custom_builtins.items():
+            add_global(name, obj)
+
+        def type_repr(o: Any):
+            if o == ():
+                # Empty tuple is used for empty tuple type annotation Tuple[()]
+                return '()'
+
+            typename = _type_repr(o)
+
+            if hasattr(o, '__origin__'):
+                # This is a generic type, e.g. typing.List[torch.Tensor]
+                origin_type = _origin_type_map.get(o.__origin__, o.__origin__)
+                origin_typename = add_global(_type_repr(origin_type), origin_type)
+
+                if hasattr(o, '__args__'):
+                    # Assign global names for each of the inner type variables.
+                    args = [type_repr(arg) for arg in o.__args__]
+
+                    if len(args) == 0:
+                        # Bare type, such as `typing.Tuple` with no subscript
+                        # This code-path used in Python < 3.9
+                        return origin_typename
+
+                    return f'{origin_typename}[{",".join(args)}]'
+                else:
+                    # Bare type, such as `typing.Tuple` with no subscript
+                    # This code-path used in Python 3.9+
+                    return origin_typename
+
+            # Common case: this is a regular module name like 'foo.bar.baz'
+            return add_global(typename, o)
+
+        def _format_args(args: Tuple[Argument, ...], kwargs: Dict[str, Argument]) -> str:
+
+            def _get_repr(arg):
+                # Handle NamedTuples (if it has `_fields`) via add_global.
+                if isinstance(arg, tuple) and hasattr(arg, '_fields'):
+                    qualified_name = _get_qualified_name(type(arg))
+                    global_name = add_global(qualified_name, type(arg))
+                    return f"{global_name}{repr(tuple(arg))}"
+                return repr(arg)
+
+            args_s = ', '.join(_get_repr(a) for a in args)
+            kwargs_s = ', '.join(f'{k} = {_get_repr(v)}' for k, v in kwargs.items())
+            if args_s and kwargs_s:
+                return f'{args_s}, {kwargs_s}'
+            return args_s or kwargs_s
+
+        # Run through reverse nodes and record the first instance of a use
+        # of a given node. This represents the *last* use of the node in the
+        # execution order of the program, which we will use to free unused
+        # values
+        node_to_last_use: Dict[Node, Node] = {}
+        user_to_last_uses: Dict[Node, List[Node]] = {}
+
+        def register_last_uses(n: Node, user: Node):
+            if n not in node_to_last_use:
+                node_to_last_use[n] = user
+                user_to_last_uses.setdefault(user, []).append(n)
+
+        for node in reversed(nodes):
+            map_arg(node.args, lambda n: register_last_uses(n, node))
+            map_arg(node.kwargs, lambda n: register_last_uses(n, node))
+
+        def delete_unused_values(user: Node):
+            """
+            Delete values after their last use. This ensures that values that are
+            not used in the remainder of the code are freed and the memory usage
+            of the code is optimal.
+            """
+            if user.op == 'placeholder':
+                return
+            if user.op == 'output':
+                body.append('\n')
+                return
+            nodes_to_delete = user_to_last_uses.get(user, [])
+            if len(nodes_to_delete):
+                to_delete_str = ' = '.join([repr(n) for n in nodes_to_delete] + ['None'])
+                body.append(f';  {to_delete_str}\n')
+            else:
+                body.append('\n')
+
+        def emit_node(node: Node):
+            maybe_type_annotation = '' if node.type is None else f' : {type_repr(node.type)}'
+            if node.op == 'placeholder':
+                assert isinstance(node.target, str)
+                maybe_default_arg = '' if not node.args else f' = {repr(node.args[0])}'
+                free_vars.append(f'{node.target}{maybe_type_annotation}{maybe_default_arg}')
+                raw_name = node.target.replace('*', '')
+                if raw_name != repr(node):
+                    body.append(f'{repr(node)} = {raw_name}\n')
+                return
+            elif node.op == 'call_method':
+                assert isinstance(node.target, str)
+                body.append(f'{repr(node)}{maybe_type_annotation} = {_format_target(repr(node.args[0]), node.target)}'
+                            f'({_format_args(node.args[1:], node.kwargs)})')
+                return
+            elif node.op == 'call_function':
+                assert callable(node.target)
+                # pretty print operators
+                if node.target.__module__ == '_operator' and node.target.__name__ in magic_methods:
+                    assert isinstance(node.args, tuple)
+                    body.append(f'{repr(node)}{maybe_type_annotation} = '
+                                f'{magic_methods[node.target.__name__].format(*(repr(a) for a in node.args))}')
+                    return
+
+                # pretty print inplace operators; required for jit.script to work properly
+                # not currently supported in normal FX graphs, but generated by torchdynamo
+                if node.target.__module__ == '_operator' and node.target.__name__ in inplace_methods:
+                    body.append(f'{inplace_methods[node.target.__name__].format(*(repr(a) for a in node.args))};  '
+                                f'{repr(node)}{maybe_type_annotation} = {repr(node.args[0])}')
+                    return
+
+                qualified_name = _get_qualified_name(node.target)
+                global_name = add_global(qualified_name, node.target)
+                # special case for getattr: node.args could be 2-argument or 3-argument
+                # 2-argument: attribute access; 3-argument: fall through to attrib function call with default value
+                if global_name == 'getattr' and \
+                   isinstance(node.args, tuple) and \
+                   isinstance(node.args[1], str) and \
+                   node.args[1].isidentifier() and \
+                   len(node.args) == 2:
+                    body.append(
+                        f'{repr(node)}{maybe_type_annotation} = {_format_target(repr(node.args[0]), node.args[1])}')
+                    return
+                body.append(
+                    f'{repr(node)}{maybe_type_annotation} = {global_name}({_format_args(node.args, node.kwargs)})')
+                if node.meta.get('is_wrapped', False):
+                    wrapped_fns.setdefault(global_name)
+                return
+            elif node.op == 'call_module':
+                assert isinstance(node.target, str)
+                body.append(f'{repr(node)}{maybe_type_annotation} = '
+                            f'{_format_target(root_module, node.target)}({_format_args(node.args, node.kwargs)})')
+                return
+            elif node.op == 'get_attr':
+                assert isinstance(node.target, str)
+                body.append(f'{repr(node)}{maybe_type_annotation} = {_format_target(root_module, node.target)}')
+                return
+            elif node.op == 'output':
+                if node.type is not None:
+                    maybe_return_annotation[0] = f" -> {type_repr(node.type)}"
+                body.append(self.generate_output(node.args[0]))
+                return
+            raise NotImplementedError(f'node: {node.op} {node.target}')
+
+        #########################################
+        # Modified for activation checkpointing #
+        #########################################
+        # find the activation checkpoint regions
+        ckpt_regions = self.find_ckpt_regions(nodes)
+        start_idx = [item[0] for item in ckpt_regions]
+        end_idx = [item[1] for item in ckpt_regions]
+        input_vars = []
+        output_vars = []
+        within_ckpt_region = False
+
+        node_list = list(nodes)
+
+        # find the input and output var names for each region
+        for idx, (start, end) in enumerate(ckpt_regions):
+            ckpt_node_list = node_list[start:end + 1]
+            inputs, outputs = self.find_input_and_output_nodes(ckpt_node_list)
+            input_vars.append(inputs)
+            output_vars.append(outputs)
+
+        # append code text to body
+        for idx, node in enumerate(node_list):
+            # if this is the first node of the ckpt region
+            # append the ckpt function defition
+            if idx in start_idx:
+                label = start_idx.index(idx)
+                ckpt_fn_def = self.gen_ckpt_fn_def(label, input_vars[label])
+                body.append(f'{ckpt_fn_def}\n')
+                within_ckpt_region = True
+
+            # NOTE: emit_node does not emit a string with newline. It depends
+            # on delete_unused_values to append one
+            emit_node(node)
+
+            # add indentation to the emmited node
+            if within_ckpt_region:
+                body[-1] = '    ' + body[-1]
+
+            # delete unused values
+            delete_unused_values(node)
+
+            if idx in end_idx:
+                # if this is the last node of the ckpt region
+                # generate return statement
+                label = end_idx.index(idx)
+                return_statement = self.gen_ckpt_output(output_vars[label])
+                return_statement = f'    {return_statement}\n'
+                body.append(return_statement)
+
+                # generate checkpoint function call in a new line
+                usage = self.gen_ckpt_usage(label, input_vars[label], output_vars[label])
+                usage += '\n'
+                body.append(usage)
+                within_ckpt_region = False
+
+        #######################################################
+        # Code Change For Activation Checkpointing Stops Here #
+        #######################################################
+
+        if len(body) == 0:
+            # If the Graph has no non-placeholder nodes, no lines for the body
+            # have been emitted. To continue to have valid Python code, emit a
+            # single pass statement
+            body.append('pass\n')
+
+        if len(wrapped_fns) > 0:
+            wrap_name = add_global('wrap', torch.fx.wrap)
+            wrap_stmts = '\n'.join([f'{wrap_name}("{name}")' for name in wrapped_fns])
+        else:
+            wrap_stmts = ''
+
+        if self._body_transformer:
+            body = self._body_transformer(body)
+
+        for name, value in self.additional_globals():
+            add_global(name, value)
+
+        prologue = self.gen_fn_def(free_vars, maybe_return_annotation[0])
+
+        code = ''.join(body)
+        code = '\n'.join('    ' + line for line in code.split('\n'))
+        fn_code = f"""
+{wrap_stmts}
+
+{prologue}
+{code}"""
+        return PythonCode(fn_code, globals_)
diff --git a/tests/test_fx/test_codegen/test_activation_checkpoint_codegen.py b/tests/test_fx/test_codegen/test_activation_checkpoint_codegen.py
new file mode 100644
index 000000000..90639a13c
--- /dev/null
+++ b/tests/test_fx/test_codegen/test_activation_checkpoint_codegen.py
@@ -0,0 +1,69 @@
+import torch
+import pytest
+from torch.utils.checkpoint import checkpoint
+from torch.fx import GraphModule
+from colossalai.fx import ColoTracer
+
+try:
+    from colossalai.fx.codegen import ActivationCheckpointCodeGen
+except:
+    pass
+
+
+class MLP(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.linear1 = torch.nn.Linear(4, 4)
+        self.linear2 = torch.nn.Linear(4, 4)
+
+    def forward(self, x):
+        return self.linear1(x), self.linear1(x)
+
+
+class MyModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.mlp1 = MLP()
+        self.mlp2 = MLP()
+        self.linear3 = torch.nn.Linear(4, 4)
+
+    def forward(self, x):
+        y1, y2 = checkpoint(self.mlp1, x)
+        y3, y4 = checkpoint(self.mlp2, x)
+        return y1 + y2 + y3 + y4
+
+
+@pytest.mark.skip("torch 1.12 is required")
+def test_act_ckpt_codegen():
+    # build model and run forward
+    model = MyModule()
+    data = torch.rand(4, 4)
+    non_fx_out = model(data)
+
+    # trace the module and replace codegen
+    tracer = ColoTracer(trace_act_ckpt=True)
+    graph = tracer.trace(model)
+    codegen = ActivationCheckpointCodeGen()
+    graph.set_codegen(codegen)
+
+    # check ops are annotated with ckpt
+    ckpt_nodes = ['mlp1_linear1', 'mlp1_linear1_1', 'mlp2_linear1', 'mlp2_linear1_1']
+    for node in graph.nodes:
+        if node.name in ckpt_nodes:
+            assert hasattr(node, 'activation_checkpoint')
+
+    # assert checkpoint function will be generated
+    code = graph.python_code('self').src
+    assert 'checkpoint_0' in code and 'checkpoint_1' in code
+
+    # recompile and verify the outputs are consistent
+    gm = GraphModule(model, graph)
+    gm.recompile()
+    fx_out = gm(data)
+    assert torch.equal(non_fx_out, fx_out)
+
+
+if __name__ == '__main__':
+    test_act_ckpt_codegen()