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.
186 lines
6.5 KiB
186 lines
6.5 KiB
import copy
|
|
|
|
import pytest
|
|
import torch
|
|
from torch.fx import GraphModule
|
|
|
|
import colossalai
|
|
from colossalai.fx import ColoTracer
|
|
from colossalai.fx.graph_module import ColoGraphModule
|
|
from colossalai.legacy.core import global_context as gpc
|
|
from colossalai.testing import rerun_if_address_is_in_use, spawn
|
|
|
|
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
|
|
|
|
|
|
class MyNet(torch.nn.Module):
|
|
def __init__(self) -> None:
|
|
super().__init__()
|
|
self.linear0 = torch.nn.Linear(4, 4)
|
|
self.linear1 = torch.nn.Linear(4, 4)
|
|
self.linear2 = torch.nn.Linear(4, 4)
|
|
self.linear3 = torch.nn.Linear(4, 4)
|
|
self.linear4 = torch.nn.Linear(4, 4)
|
|
self.linear5 = torch.nn.Linear(4, 4)
|
|
self.linear6 = torch.nn.Linear(4, 4)
|
|
|
|
def forward(self, x):
|
|
x = self.linear0(x)
|
|
x = self.linear1(x)
|
|
x = self.linear2(x)
|
|
x = self.linear3(x)
|
|
x = self.linear4(x)
|
|
x = self.linear5(x)
|
|
x = self.linear6(x)
|
|
return x
|
|
|
|
|
|
def _is_all_gradient_close(m: torch.nn.Module, gm: GraphModule) -> bool:
|
|
for m_p, gm_p in zip(m.parameters(), gm.parameters()):
|
|
if not torch.allclose(m_p.grad, gm_p.grad):
|
|
return False
|
|
return True
|
|
|
|
|
|
def _test_fwd_and_bwd(model: torch.nn.Module, gm: ColoGraphModule, data: torch.Tensor):
|
|
# test forward
|
|
non_fx_out = model(data)
|
|
fx_out = gm(data)
|
|
assert torch.equal(non_fx_out, fx_out), "fx_out doesn't comply with original output"
|
|
|
|
# test backward
|
|
loss0 = non_fx_out.sum()
|
|
loss0.backward()
|
|
loss1 = fx_out.sum()
|
|
loss1.backward()
|
|
assert _is_all_gradient_close(model, gm), "gm doesn't have the same gradient as original one"
|
|
|
|
|
|
def _run_offload_codegen(rank, world_size, port):
|
|
# launch colossalai to make sure we could execute colossalai.utils.checkpoint currently
|
|
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
|
|
|
# build model and input
|
|
model = MyNet().cuda()
|
|
data = torch.rand(4, 4).cuda()
|
|
|
|
# trace the module and replace codegen
|
|
tracer = ColoTracer(trace_act_ckpt=True)
|
|
graph = tracer.trace(model)
|
|
codegen = ActivationCheckpointCodeGen()
|
|
graph.set_codegen(codegen)
|
|
|
|
# annotate the activation offload part
|
|
# also annotate the activation_checkpoint so we could test both types
|
|
# of input offload
|
|
for node in graph.nodes:
|
|
if node.name == "linear0":
|
|
node.meta["activation_offload"] = [0, True, False]
|
|
if node.name == "linear1":
|
|
node.meta["activation_offload"] = [0, True, False]
|
|
if node.name == "linear2":
|
|
node.meta["activation_offload"] = [1, True, True]
|
|
if node.name == "linear4":
|
|
node.meta["activation_offload"] = [2, False, True]
|
|
if node.name == "linear5":
|
|
node.meta["activation_checkpoint"] = [0]
|
|
node.meta["activation_offload"] = True
|
|
|
|
gm = ColoGraphModule(copy.deepcopy(model), graph)
|
|
gm.recompile()
|
|
|
|
# assert we have all the components
|
|
code = graph.python_code("self").src
|
|
assert (
|
|
"def pack_hook_input(self, x):" in code
|
|
and "def unpack_hook(self, packed):" in code
|
|
and "def pack_hook_no_input(self, x):" in code
|
|
and "setattr(x, 'offload', True)" in code
|
|
and "setattr(linear3, 'offload', False)" in code
|
|
and "with torch.autograd.graph.saved_tensors_hooks(self.pack_hook_input, self.unpack_hook):" in code
|
|
and "with torch.autograd.graph.save_on_cpu(pin_memory=True):" in code
|
|
and "with torch.autograd.graph.saved_tensors_hooks(self.pack_hook_no_input, self.unpack_hook):" in code
|
|
and "colossalai.utils.activation_checkpoint.checkpoint(self.checkpoint_0, True, linear4, use_reentrant=False)"
|
|
in code
|
|
)
|
|
|
|
_test_fwd_and_bwd(model, gm, data)
|
|
gpc.destroy()
|
|
|
|
|
|
@pytest.mark.skipif(not with_codegen, reason="torch version is lower than 1.12.0")
|
|
@rerun_if_address_is_in_use()
|
|
def test_act_ckpt_codegen():
|
|
spawn(_run_offload_codegen, 1)
|
|
|
|
|
|
def _run_offload_codegen_torch11(rank, world_size, port):
|
|
# launch colossalai to make sure we could execute colossalai.utils.checkpoint currently
|
|
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
|
|
|
# build model and input
|
|
model = MyNet().cuda()
|
|
data = torch.rand(4, 4).cuda()
|
|
|
|
# trace the module and replace codegen
|
|
tracer = ColoTracer(trace_act_ckpt=True)
|
|
graph = tracer.trace(model)
|
|
|
|
# replace a bound method of an object
|
|
graph._python_code = python_code_with_activation_checkpoint.__get__(graph)
|
|
|
|
# annotate the activation offload part
|
|
# also annotate the activation_checkpoint so we could test both types
|
|
# of input offload
|
|
for node in graph.nodes:
|
|
if node.name == "linear0":
|
|
node.meta["activation_offload"] = [0, True, False]
|
|
if node.name == "linear1":
|
|
node.meta["activation_offload"] = [0, True, False]
|
|
if node.name == "linear2":
|
|
node.meta["activation_offload"] = [1, True, True]
|
|
if node.name == "linear4":
|
|
node.meta["activation_offload"] = [2, False, True]
|
|
if node.name == "linear5":
|
|
node.meta["activation_checkpoint"] = [0]
|
|
node.meta["activation_offload"] = True
|
|
|
|
gm = ColoGraphModule(copy.deepcopy(model), graph)
|
|
gm.recompile()
|
|
|
|
# assert we have all the components
|
|
code = graph.python_code("self").src
|
|
assert (
|
|
"def pack_hook_input(self, x):" in code
|
|
and "def unpack_hook(self, packed):" in code
|
|
and "def pack_hook_no_input(self, x):" in code
|
|
and "setattr(x, 'offload', True)" in code
|
|
and "setattr(linear3, 'offload', False)" in code
|
|
and "with torch.autograd.graph.saved_tensors_hooks(self.pack_hook_input, self.unpack_hook):" in code
|
|
and "with torch.autograd.graph.save_on_cpu(pin_memory=True):" in code
|
|
and "with torch.autograd.graph.saved_tensors_hooks(self.pack_hook_no_input, self.unpack_hook):" in code
|
|
and "colossalai.utils.activation_checkpoint.checkpoint(self.checkpoint_0, True, linear4, use_reentrant=False)"
|
|
in code
|
|
)
|
|
|
|
_test_fwd_and_bwd(model, gm, data)
|
|
gpc.destroy()
|
|
|
|
|
|
@pytest.mark.skip(reason="currently torch11 ColoGraphModule is not implemented")
|
|
@rerun_if_address_is_in_use()
|
|
def test_act_ckpt_python_code_torch11():
|
|
spawn(_run_offload_codegen_torch11, 1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
_run_offload_codegen(0)
|