mirror of https://github.com/hpcaitech/ColossalAI
114 lines
3.8 KiB
Python
114 lines
3.8 KiB
Python
from functools import partial
|
|
|
|
import pytest
|
|
import torch
|
|
import torch.fx
|
|
import torch.multiprocessing as mp
|
|
|
|
import colossalai
|
|
from colossalai.core import global_context as gpc
|
|
from colossalai.fx import ColoTracer
|
|
from colossalai.fx._compatibility import is_compatible_with_meta
|
|
from colossalai.fx.codegen.activation_checkpoint_codegen import CODEGEN_AVAILABLE
|
|
from colossalai.fx.graph_module import ColoGraphModule
|
|
from colossalai.fx.passes.meta_info_prop import MetaInfoProp
|
|
from colossalai.utils import free_port
|
|
from tests.test_autochunk.evoformer.evoformer import evoformer_base
|
|
|
|
if CODEGEN_AVAILABLE and is_compatible_with_meta():
|
|
from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen
|
|
from colossalai.fx.profiler import MetaTensor
|
|
|
|
|
|
def _test_fwd(model: torch.nn.Module, gm: ColoGraphModule, node, pair):
|
|
# for memory test
|
|
# torch.cuda.reset_peak_memory_stats()
|
|
# now_mem = torch.cuda.memory_allocated() / 1024**2
|
|
# with torch.no_grad():
|
|
# node1 = node.clone()
|
|
# pair1 = pair.clone()
|
|
# gm(node1, pair1)
|
|
# new_now_mem = torch.cuda.memory_allocated() / 1024**2
|
|
# new_max_mem = torch.cuda.max_memory_allocated() / 1024**2
|
|
# print(
|
|
# "autochunk now mem:%.2f max mem:%.2f"
|
|
# % (new_now_mem - now_mem, new_max_mem - now_mem)
|
|
# )
|
|
|
|
# test forward
|
|
with torch.no_grad():
|
|
non_fx_out = model(node, pair)
|
|
fx_out = gm(node, pair)
|
|
|
|
assert torch.allclose(non_fx_out[0], fx_out[0],
|
|
atol=1e-4), "fx_out doesn't comply with original output, diff is %.2e" % torch.mean(
|
|
torch.abs(non_fx_out[0] - fx_out[0]))
|
|
assert torch.allclose(non_fx_out[1], fx_out[1],
|
|
atol=1e-4), "fx_out doesn't comply with original output, diff is %.2e" % torch.mean(
|
|
torch.abs(non_fx_out[1] - fx_out[1]))
|
|
|
|
|
|
def _test_autochunk_codegen(rank, msa_len, pair_len, max_memory):
|
|
# launch colossalai
|
|
colossalai.launch(
|
|
config={},
|
|
rank=rank,
|
|
world_size=1,
|
|
host="localhost",
|
|
port=free_port(),
|
|
backend="nccl",
|
|
)
|
|
|
|
# build model and input
|
|
model = evoformer_base().cuda()
|
|
node = torch.randn(1, msa_len, pair_len, 256).cuda()
|
|
pair = torch.randn(1, pair_len, pair_len, 128).cuda()
|
|
|
|
# trace the module and replace codegen
|
|
graph = ColoTracer().trace(
|
|
model,
|
|
meta_args={
|
|
"node": node.to(torch.device("meta")),
|
|
"pair": pair.to(torch.device("meta")),
|
|
},
|
|
)
|
|
gm_prop = torch.fx.symbolic_trace(model) # must use symbolic_trace
|
|
interp = MetaInfoProp(gm_prop)
|
|
interp.propagate(MetaTensor(node, fake_device="cuda:0"), MetaTensor(pair, fake_device="cuda:0"))
|
|
|
|
# now run it twice to get meta info in graph module, not necessary
|
|
gm = torch.fx.GraphModule(model, graph)
|
|
interp = MetaInfoProp(gm)
|
|
interp.propagate(MetaTensor(node, fake_device="cuda:0"), MetaTensor(pair, fake_device="cuda:0"))
|
|
|
|
codegen = AutoChunkCodeGen(gm_prop, max_memory=max_memory)
|
|
graph.set_codegen(codegen)
|
|
gm = ColoGraphModule(model, graph)
|
|
gm.recompile()
|
|
|
|
# assert we have inserted chunk
|
|
code = graph.python_code("self").src
|
|
assert "chunk_size" in code
|
|
# print(code)
|
|
|
|
_test_fwd(model, gm, node, pair)
|
|
gpc.destroy()
|
|
|
|
|
|
@pytest.mark.skipif(not (CODEGEN_AVAILABLE and is_compatible_with_meta()), reason='torch version is lower than 1.12.0')
|
|
@pytest.mark.parametrize("max_memory", [None, 20, 25, 30])
|
|
@pytest.mark.parametrize("msa_len", [32])
|
|
@pytest.mark.parametrize("pair_len", [64])
|
|
def test_autochunk_codegen(msa_len, pair_len, max_memory):
|
|
run_func = partial(
|
|
_test_autochunk_codegen,
|
|
msa_len=msa_len,
|
|
pair_len=pair_len,
|
|
max_memory=max_memory,
|
|
)
|
|
mp.spawn(run_func, nprocs=1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
_test_autochunk_codegen(0, 32, 64, 25)
|