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ColossalAI/tests/test_auto_parallel/test_ckpt_solvers/test_linearize.py

151 lines
6.4 KiB

import pytest
import torch
import torchvision.models as tm
from colossalai.fx import ColoTracer
from colossalai.fx._compatibility import is_compatible_with_meta
from colossalai.fx.graph_module import ColoGraphModule
# from colossalai.fx.passes.algorithms import linearize, solver_rotor
# from colossalai.fx.passes.algorithms.operation import (ForwardCheck, ForwardEnable, ForwardNograd, Loss)
from colossalai.fx.passes.meta_info_prop import MetaInfoProp
from colossalai.testing import clear_cache_before_run
if is_compatible_with_meta():
from colossalai.fx.profiler.tensor import MetaTensor
try:
from colossalai.fx.codegen import ActivationCheckpointCodeGen
with_codegen = True
except:
# fall back to older pytorch version
from colossalai.fx.codegen import python_code_with_activation_checkpoint
with_codegen = False
@pytest.mark.skip(reason="TODO: modify the logger")
@pytest.mark.skip("TODO(lyl): refactor all tests.")
@pytest.mark.skipif(not with_codegen, reason="torch version is lower than 1.12.0")
@clear_cache_before_run()
def test_linearize():
MODEL_DICT = {tm.resnet18: [2100, 3000], tm.densenet121: [8100, 17000]}
tracer = ColoTracer()
for M, budgets in MODEL_DICT.items():
for budget in budgets:
model = M()
graph = tracer.trace(model)
graph.set_codegen(ActivationCheckpointCodeGen())
gm = ColoGraphModule(model, graph, model.__class__.__name__)
MetaInfoProp(gm).run(MetaTensor(torch.rand(128, 3, 224, 224, device="meta"), fake_device="cpu"))
node_list = linearize(gm)
gm = solver_rotor(gm, data=torch.rand(128, 3, 224, 224, device="meta"), mem_limit=budget * 1024**2)
op_list = gm.__sequence__.list_operations()
loss_op = next(op for op in op_list if isinstance(op, Loss))
op_list = op_list[: op_list.index(loss_op)]
in_ckpt = False
ckpt_idx = 0
for idx, op in enumerate(op_list):
if in_ckpt:
if isinstance(op, ForwardNograd):
for n in node_list[idx]:
assert hasattr(n, "activation_checkpoint"), f"{n} is not annotated!"
assert (
n.activation_checkpoint[0] == ckpt_idx
), f"{n} ckpt_idx {n.activation_checkpoint[0]} wrong, should be {ckpt_idx}!"
continue
if isinstance(op, ForwardEnable):
for n in node_list[idx]:
assert getattr(n, "activation_checkpoint", None) == None, f"{n} should not be annotated!"
in_ckpt = False
ckpt_idx += 1
continue
if isinstance(op, ForwardCheck):
ckpt_idx += 1
for n in node_list[idx]:
assert hasattr(n, "activation_checkpoint"), f"{n} is not annotated!"
assert (
n.activation_checkpoint[0] == ckpt_idx
), f"{n} ckpt_idx {n.activation_checkpoint[0]} wrong, should be {ckpt_idx}!"
continue
else:
if isinstance(op, ForwardCheck):
in_ckpt = True
for n in node_list[idx]:
assert hasattr(n, "activation_checkpoint"), f"{n} is not annotated!"
assert (
n.activation_checkpoint[0] == ckpt_idx
), f"{n} ckpt_idx {n.activation_checkpoint[0]} wrong, should be {ckpt_idx}!"
del model
del gm
del node_list
@pytest.mark.skip("TODO(lyl): refactor all tests.")
@pytest.mark.skip(reason="torch11 meta tensor not implemented")
@pytest.mark.skipif(with_codegen, reason="torch version is equal to or higher than 1.12.0")
@clear_cache_before_run()
def test_linearize_torch11():
MODEL_DICT = {tm.resnet18: [2100, 3000], tm.densenet121: [8100, 17000]}
tracer = ColoTracer()
for M, budgets in MODEL_DICT.items():
for budget in budgets:
model = M()
graph = tracer.trace(model)
gm = ColoGraphModule(model, graph, model.__class__.__name__)
gm.graph._python_code = python_code_with_activation_checkpoint.__get__(graph)
node_list = linearize(gm)
gm = solver_rotor(gm, data=torch.rand(128, 3, 224, 224, device="meta"), mem_limit=budget * 1024**2)
op_list = gm.__sequence__.list_operations()
loss_op = next(op for op in op_list if isinstance(op, Loss))
op_list = op_list[: op_list.index(loss_op)]
in_ckpt = False
ckpt_idx = 0
for idx, op in enumerate(op_list):
if in_ckpt:
if isinstance(op, ForwardNograd):
for n in node_list[idx]:
assert hasattr(n, "activation_checkpoint"), f"{n} is not annotated!"
assert n.activation_checkpoint == ckpt_idx, f"{n} ckpt_idx wrong, should be {ckpt_idx}!"
continue
if isinstance(op, ForwardEnable):
for n in node_list[idx]:
assert getattr(n, "activation_checkpoint", None) == None, f"{n} should not be annotated!"
in_ckpt = False
ckpt_idx += 1
continue
if isinstance(op, ForwardCheck):
ckpt_idx += 1
for n in node_list[idx]:
assert hasattr(n, "activation_checkpoint"), f"{n} is not annotated!"
assert n.activation_checkpoint == ckpt_idx, f"{n} ckpt_idx wrong, should be {ckpt_idx}!"
continue
else:
if isinstance(op, ForwardCheck):
in_ckpt = True
for n in node_list[idx]:
assert hasattr(n, "activation_checkpoint"), f"{n} is not annotated!"
assert n.activation_checkpoint == ckpt_idx, f"{n} ckpt_idx wrong, should be {ckpt_idx}!"
del model
del gm
del node_list
if __name__ == "__main__":
test_linearize()