[fx] fix the false interpretation of algorithm 3 in https://arxiv.org/abs/1604.06174. (#1446)

* [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages

* [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages

* [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages

* [fx] activation checkpointing using Chen strategies.

* [fx] add test for ckpt_solver_chen

* mend

* [fx] add vanilla activation checkpoint search with test on resnet and densenet

* [fx] add vanilla activation checkpoint search with test on resnet and densenet

* [fx] add a namespace code for solver_chen.

* [fx] fix the false interpretation of algorithm 3 in https://arxiv.org/abs/1604.06174.

* [fx] fix lowercase naming conventions.

colossalai/fx/passes/algorithms/ckpt_solver_chen.py

@@ -1,45 +1,71 @@
+from typing import Set, Tuple
 import torch
 from torch.fx import GraphModule
+import math
 
 __all__ = ['chen_greedy', 'chen_sqrtn']
 
 
-def chen_greedy(gm: GraphModule, B: int):
+def chen_greedy(gm: GraphModule) -> GraphModule:
     """
     This is the simple implementation of Algorithm 3 in https://arxiv.org/abs/1604.06174.
+    Note that this algorithm targets at memory optimization only, using techniques in appendix A.
 
     Usage:
-        B = 5 * 1024 * 1024 * 1024  # An approximate memory budget of 5GB
         model = resnet18()
         input_sample = torch.rand(4, 3, 224, 224)
         gm = symbolic_trace(model)
         MetaInfoProp(gm).run(input_sample)
-        gm = chen_greedy(gm, B)
+        gm = chen_greedy(gm)
 
     Args:
         gm (GraphModule): The module to add checkpoints
-        B (int): The approximate memory budget for this module.
     """
-    gm.graph.lint()    # make sure nodes are in topological order
+
+    def grid_search(num_grids: int = 6) -> 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.
+        """
+        _, b_approx = run_chen_greedy(0)
+        b_min, b_max = math.floor(b_approx / math.sqrt(2)), math.ceil(b_approx * math.sqrt(2))
+        b_opt = math.inf
+        for b in range(b_min, b_max, (b_max - b_min) // num_grids):
+            ckpt, b_approx = run_chen_greedy(b)
+            if b_approx < b_opt:
+                b_opt = b_approx
+                ckpt_opt = ckpt
+        return ckpt_opt
+
+    def run_chen_greedy(b: int = 0) -> Tuple[Set, int]:
+        """
+        This is the simple implementation of Algorithm 3 in https://arxiv.org/abs/1604.06174.
+        """
+        ckpt = set()
         temp = 0
         x = 0
-    idx = 0
+        y = 0
-    budget = B
+        for (idx, n) in enumerate(gm.graph.nodes):
-    for n in gm.graph.nodes:
-        B -= getattr(n, 'param_size')
-        assert B > 0, f'The memory budget {budget / 1024 ** 3:.2f} GB is not enough for model parameters of {gm}'
-    for n in gm.graph.nodes:
             temp += getattr(n, 'activation_size')
-        if temp > B:
+            y = max(y, temp)
+            if temp > b:
                 x += getattr(n, 'activation_size')
-            temp = x
+                temp = 0
-            setattr(n, 'activation_checkpoint', str(idx))
+                ckpt.add(idx)
-            idx += 1
+        return ckpt, math.floor(math.sqrt(x * y))
+
+    gm.graph.lint()    # make sure nodes are in topological order
+    ckpt = grid_search(num_grids=6)
+    i = 0
+    for idx, n in enumerate(gm.graph.nodes):
+        if idx in ckpt:
+            setattr(n, 'activation_checkpoint', str(i))
+            i += 1
     gm.recompile()
     return gm
 
 
-def chen_sqrtn(gm: GraphModule):
+def chen_sqrtn(gm: GraphModule) -> GraphModule:
     """
     This is the theoretical optimal strategy in https://arxiv.org/abs/1604.06174.
 

tests/test_fx/test_ckpt_solvers/test_ckpt_torchvision.py

@@ -1,13 +1,13 @@
+from ctypes import Union
 from colossalai.fx.passes.algorithms import chen_greedy, chen_sqrtn
 import torch
 import torchvision.models as tm
 from colossalai.fx import ColoTracer
 from torch.fx import GraphModule
 from colossalai.fx.passes.meta_info_prop import MetaInfoProp
-from functools import partial
 import pytest
 
-SOLVERS = [partial(chen_greedy, B=1024 * 1024 * 64), chen_sqrtn]
+SOLVERS = [chen_greedy, chen_sqrtn]
 
 
 def _is_activation_checkpoint_available(gm: GraphModule):
@@ -16,6 +16,13 @@ def _is_activation_checkpoint_available(gm: GraphModule):
             return True
 
 
+def _is_all_gradient_close(m: torch.nn.Module, gm: GraphModule):
+    for m_p, gm_p in zip(m.parameters(), gm.parameters()):
+        if not torch.allclose(m_p, gm_p):
+            return False
+    return True
+
+
 def test_ckpt_solver():
     MODEL_LIST = [tm.resnet18, tm.densenet121]
 
@@ -23,17 +30,24 @@ def test_ckpt_solver():
 
     tracer = ColoTracer()
     data = torch.rand(1, 3, 224, 224)
+    label = torch.rand(1, 1000)
 
     for solver in SOLVERS:
         for model_cls in MODEL_LIST:
             model = model_cls()
+            criterion = torch.nn.MSELoss()
             graph = tracer.trace(root=model)
             gm = GraphModule(model, graph, model.__class__.__name__)
             MetaInfoProp(gm).run(data)
             gm = solver(gm)
             assert _is_activation_checkpoint_available(
                 gm), f"Solver {solver} did not annotate {model_cls} with any activation checkpoints"
-            assert torch.allclose(gm(data), model(data))
+            loss = criterion(model(data), label)
+            loss.backward()
+            loss = criterion(gm(data), label)
+            loss.backward()
+            assert _is_all_gradient_close(model,
+                                          gm), f'Solver {solver} did not work correctly in backward pass on {model_cls}'
 
 
 if __name__ == '__main__':