[autockpt] considering parameter and optimizer weights.

colossalai/auto_parallel/checkpoint/ckpt_solver_base.py

@@ -35,10 +35,11 @@ def __init__(
         free_memory: float = -1.0,
         requires_linearize: bool = False,
         cnode: List[str] = None,
+        optim_multiplier: float = 1.0,
     ):
-        """CheckpointSolver class will integrate information provided by the components
-        and use an existing solver to find a possible optimal strategies combination for
-        target computing graph.
+        """``CheckpointSolverBase`` class will integrate information provided by the components
+        and use an existing solver to find a possible optimal strategies combination for target
+        computing graph.
 
         Existing Solvers:
             Chen's Greedy solver: https://arxiv.org/abs/1604.06174  (CheckpointSolverChen)
@@ -49,9 +50,11 @@ def __init__(
             free_memory (float): Memory constraint for the solution.
             requires_linearize (bool): Whether the graph needs to be linearized.
             cnode (List[str], optional): Common node List, should be the subset of input. Default to None.
+            optim_multiplier (float, optional): The multiplier of extra weight storage for the
+            ``torch.optim.Optimizer``. Default to 1.0.
 
         Warnings:
-            `MetaInfoProp` should be done before constructing the solver. Meta information of the graph is required.
+            Meta information of the graph is required for any ``CheckpointSolver``.
         """
         # super-dainiu: this graph is a temporary graph which can refer to
         # the owning module, but we will return another deepcopy of it after
@@ -61,13 +64,14 @@ def __init__(
         _copy_output(graph, self.graph)
         self.graph.set_codegen(ActivationCheckpointCodeGen())
 
-        # check if `MetaInfoProp` is done
+        # check if has meta information
         if any(len(node.meta) == 0 for node in self.graph.nodes):
             raise RuntimeError(
-                "Nodes meta information hasn't been prepared! Please run MetaInfoProp before constructing the solver!")
+                "Nodes meta information hasn't been prepared! Please extract from graph before constructing the solver!"
+            )
 
-        self.free_memory = free_memory
-        self.parameter_size = _get_param_size(self.graph.owning_module)
+        # parameter memory = parameter size + optimizer extra weight storage
+        self.free_memory = free_memory - _get_param_size(self.graph.owning_module) * (optim_multiplier + 1)
         self.cnode = cnode
         self.requires_linearize = requires_linearize
         if self.requires_linearize:
@@ -97,7 +101,7 @@ def _linearize_graph(self) -> List[List[Node]]:
             the actual 'node' in linearized manner.
 
         Remarks:
-            Do merge the inplace ops into the previous node.
+            Do merge the inplace ops and shape-consistency ops into the previous node.
         """
 
         # Common nodes are type of nodes that could be seen as attributes and remain
@@ -136,7 +140,7 @@ def _is_sink() -> bool:
             """
 
             def _is_inplace(n: Node):
-                """Get the inplace argument from torch.fx.Node
+                """Get the inplace argument from ``torch.fx.Node``
                 """
                 inplace = False
                 if n.op == "call_function":

colossalai/auto_parallel/checkpoint/ckpt_solver_chen.py

@@ -19,9 +19,9 @@ def __init__(self, graph: Graph, cnode: List[str] = None, num_grids: int = 6):
         Note that this algorithm targets at memory optimization only, using techniques in appendix A.
 
         Usage:
-            Assume that we have a `GraphModule`, and we already applied the `MetaInfoProp`
+            Assume that we have a ``GraphModule``, and we have already done the extractions
             to the graph to retrieve all information needed, then we could use the following
-            code to find a solution using `CheckpointSolverChen`:
+            code to find a solution using ``CheckpointSolverChen``:
             >>> solver = CheckpointSolverChen(gm.graph)
             >>> chen_graph = solver.solve()
             >>> gm.graph = chen_graph    # set the graph to a new graph
@@ -74,7 +74,7 @@ def run_chen_greedy(self, b: int = 0) -> Tuple[Set, int]:
     def grid_search(self) -> Set:
         """
         Search ckpt strategy with b = 0, then run the allocation algorithm again with b = √xy.
-        Grid search over [√2/2 b, √2 b] for ckpt_opt over num_grids as in appendix A.
+        Grid search over [√2/2 b, √2 b] for ``ckpt_opt`` over ``num_grids`` as in appendix A.
         """
         _, b_approx = self.run_chen_greedy(0)
         b_min, b_max = math.floor(b_approx / math.sqrt(2)), math.ceil(b_approx * math.sqrt(2))

colossalai/auto_parallel/checkpoint/ckpt_solver_rotor.py

@@ -23,15 +23,20 @@
 
 class CheckpointSolverRotor(CheckpointSolverBase):
 
-    def __init__(self, graph: Graph, free_memory: float = -1, cnode: List[str] = None, memory_slots: int = 500):
+    def __init__(self,
+                 graph: Graph,
+                 free_memory: float = -1,
+                 cnode: List[str] = None,
+                 memory_slots: int = 500,
+                 optim_multiplier: float = 1.0):
         """This is the simple implementation of dynamic programming algorithm rotor
         in https://hal.inria.fr/hal-02352969. Some code are adapted from
         https://gitlab.inria.fr/hiepacs/rotor.
 
         Usage:
-            Assume that we have a `GraphModule`, and we already applied the `MetaInfoProp`
+            Assume that we have a ``GraphModule``, and we have already done the extractions
             to the graph to retrieve all information needed, then we could use the following
-            code to find a solution using `CheckpointSolverRotor`:
+            code to find a solution using ``CheckpointSolverRotor``:
             >>> solver = CheckpointSolverRotor(gm.graph, free_memory=torch.cuda.mem_get_info(device=0)[0])
             >>> rotor_graph = solver.solve(force_python=True)   # otherwise use C solver
             >>> gm.graph = rotor_graph    # set the graph to a new graph
@@ -42,6 +47,8 @@ def __init__(self, graph: Graph, free_memory: float = -1, cnode: List[str] = Non
                 Use ``torch.cuda.mem_get_info(device=0)[0]`` to estimate the free_memory. Defaults to -1.
             cnode (List[str], optional): Common node List, should be the subset of input. Defaults to None.
             memory_slots (int, optional): Number of slots for discretizing memory budget. Defaults to 500.
+            optim_multiplier (float, optional): The multiplier of extra weight storage for the
+            ``torch.optim.Optimizer``. Default to 1.0.
         """
         super().__init__(graph, free_memory, True, cnode)
         self.memory_slots = memory_slots
@@ -298,8 +305,8 @@ def _backtrack(chain: Chain, lhs: int, rhs: int, budget: int, cost_table: List[A
             lhs (int): The left index of the interval to backtrack.
             rhs (int): The right index of the interval to backtrack.
             budget (int): The memory budget for processing this interval.
-            cost_table (List[Any]): See `._compute_table()` for definitions
-            back_ptr (List[Any]): See `._compute_table()` for definitions
+            cost_table (List[Any]): See ``._compute_table()`` for definitions
+            back_ptr (List[Any]): See ``._compute_table()`` for definitions
 
         Raises:
             ValueError: Can not process the chain.
@@ -340,7 +347,7 @@ def _backtrack(chain: Chain, lhs: int, rhs: int, budget: int, cost_table: List[A
 
     @staticmethod
     def _annotate_from_sequence(sequence: Sequence, node_list: List[List[Node]]):
-        """Annotate the nodes in the node_list with activation checkpoint from the sequence.
+        """Annotate the nodes in the ``node_list`` with activation checkpoint from the sequence.
 
         Args:
             sequence (Sequence): The sequence of executing nodes with activation checkpoint annotations.