mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
139 lines
6.1 KiB
139 lines
6.1 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
|
|
|
|
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.skipif(not with_codegen, reason="torch version is lower than 1.12.0")
|
|
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(reason="torch11 meta tensor not implemented")
|
|
@pytest.mark.skipif(with_codegen, reason="torch version is equal to or higher than 1.12.0")
|
|
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()
|