[fx] Fix wrong index in annotation and minimal flops in ckpt solver (#1521)

* [fx] fix wrong variable name in solver rotor

* [fx] fix wrong variable name in solver rotor

* [fx] fix the discretize bug

* [fx] fix the first op in activation checkpoint codegen

* [fx] fix some bugs of ckpt solver

* [fx] modify test_ckpt_torchvision

* [fx] set sequence to __sequence__ attr of GraphModule

* [fx] docstring modification

* [fx] remove performance test

colossalai/fx/passes/algorithms/ckpt_solver_rotor.py

 from typing import List, Set, Tuple, Dict
 import torch
 from torch.fx import GraphModule, Node
+from colossalai.fx.graph_module import ColoGraphModule
 import math
 from .linearize import linearize
 from .utils import *
@@ -131,10 +132,10 @@ def _construct_chain(node_dict: Dict[int, Node], data: torch.Tensor, mem_unit: i
         x_sizes.append(node_dict[key][-1].meta['tensor_meta'].numel *
                        torch.tensor([], dtype=node_dict[key][-1].meta['tensor_meta'].dtype).element_size())
         for node in node_dict[key]:
-            fwd_time[-1] += node.__flops__
+            fwd_time[-1] += max(node.__flops__, 1)
 
             # currently we haven't patched the backward flops count
-            bwd_time[-1] += node.__flops__ * 2
+            bwd_time[-1] += max(node.__flops__ * 2, 2)
 
             xbar_sizes[-1] += node.__activation__
 
@@ -164,16 +165,16 @@ def _annotate_from_sequence(sequence: Sequence, node_dict: Dict[int, Node]) -> G
 
             elif isinstance(op, ForwardEnable):
                 in_ckpt = False
-                for idx in ckpt_region:
-                    for node in node_dict[idx]:
+                for node_idx in ckpt_region:
+                    for node in node_dict[node_idx]:
                         setattr(node, "activation_checkpoint", ckpt_idx)
 
                 ckpt_idx += 1
                 ckpt_region = []
 
             elif isinstance(op, ForwardCheck):
-                for idx in ckpt_region:
-                    for node in node_dict[idx]:
+                for node_idx in ckpt_region:
+                    for node in node_dict[node_idx]:
                         setattr(node, "activation_checkpoint", ckpt_idx)
 
                 ckpt_idx += 1
@@ -185,7 +186,19 @@ def _annotate_from_sequence(sequence: Sequence, node_dict: Dict[int, Node]) -> G
                 ckpt_region.append(idx)
 
 
-def solver_rotor(gm: GraphModule, data: torch.Tensor, mem_limit: int, mem_slots: int = 500) -> GraphModule:
+def solver_rotor(gm: ColoGraphModule, data: torch.Tensor, mem_limit: int, mem_slots: int = 500) -> ColoGraphModule:
+    """solver that automatically find activation checkpoint in rotor's manner
+
+    Args:
+        gm (ColoGraphModule): ColoGraphModule generated by tracing model.
+        data (torch.Tensor): input data.
+        mem_limit (int): memory budget in Byte.
+        mem_slots (int, optional): Number of slots for discretizing memory budget. Defaults to 500.
+
+    Returns:
+        ColoGraphModule: annotated ColoGraphModuled with __sequence__ attribute
+    """
+
     node_dict = linearize(gm)
     mem_unit = mem_limit // mem_slots
     MetaInfoProp(gm).run(data)
@@ -193,4 +206,7 @@ def solver_rotor(gm: GraphModule, data: torch.Tensor, mem_limit: int, mem_slots:
     opt_table = _compute_table(chain, mem_slots)
     sequence = _rec(chain, 0, chain.length, mem_slots - chain.cweight[0], opt_table)
     _annotate_from_sequence(sequence, node_dict)
+
+    # set __sequence__ attribute to GraphModule
+    setattr(gm, "__sequence__", sequence)
     return gm

tests/test_fx/test_ckpt_solvers/test_ckpt_torchvision.py

@@ -62,13 +62,13 @@ def check_backward_consistency(m: torch.nn.Module, gm: GraphModule, solver: Call
 
 def _run_ckpt_solver(rank):
     colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl')
-    MODEL_LIST = [tm.resnet18, tm.densenet121]
+    MODEL_LIST = [tm.densenet121]
 
     torch.backends.cudnn.deterministic = True
 
     tracer = ColoTracer(trace_act_ckpt=False)
 
-    data = torch.rand(2, 3, 32, 32, device='meta')
+    data = torch.rand(8, 3, 224, 224, device='meta')
     for solver in SOLVERS:
         for model_cls in MODEL_LIST:
             m = model_cls(num_classes=5)
@@ -95,13 +95,13 @@ def test_ckpt_solver():
 
 def _run_ckpt_solver_torch11(rank):
     colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl')
-    MODEL_LIST = [tm.resnet18, tm.densenet121]
+    MODEL_LIST = [tm.densenet121]
 
     torch.backends.cudnn.deterministic = True
 
     tracer = ColoTracer(trace_act_ckpt=False)
 
-    data = torch.rand(2, 3, 32, 32, device='meta')
+    data = torch.rand(8, 3, 32, 32, device='meta')
     for solver in SOLVERS:
         for model_cls in MODEL_LIST:
             m = model_cls(num_classes=5)