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.
ColossalAI/tests/test_utils/test_checkpoint/test_checkpoint_1d.py

79 lines
2.5 KiB

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import pprint
from functools import partial
import colossalai.nn as col_nn
import pytest
import torch
import torch.multiprocessing as mp
import torch.nn as nn
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.initialize import launch
from colossalai.logging import disable_existing_loggers
from colossalai.utils import free_port, is_using_pp
from colossalai.utils.checkpointing import gather_pipeline_parallel_state_dict, load_checkpoint, save_checkpoint
def build_pipeline(model):
from colossalai.builder.pipeline import partition_uniform
pipeline_size = gpc.get_world_size(ParallelMode.PIPELINE)
pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE)
depth = len(model)
start, end = partition_uniform(depth, pipeline_size, 1)[pipeline_rank][0]
layers = []
for i in range(depth):
if start <= i < end:
layers.append(model[i])
else:
layers.append(nn.Identity())
return nn.Sequential(*tuple(layers))
def check_equal(A, B):
assert torch.allclose(A, B, rtol=1e-3, atol=1e-2)
def check_checkpoint_1d(rank, world_size, port):
config = dict(
parallel=dict(pipeline=dict(size=2), tensor=dict(size=4, mode="1d")),
)
disable_existing_loggers()
launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
m1 = nn.Sequential(nn.Linear(4, 8), nn.Linear(8, 4))
sd1 = m1.state_dict()
if gpc.get_global_rank() == 0:
print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd1)}\n")
save_checkpoint("test.pt", 0, m1)
m2 = nn.Sequential(col_nn.Linear(4, 8), col_nn.Linear(8, 4))
if is_using_pp():
m2 = build_pipeline(m2)
load_checkpoint("test.pt", m2)
sd2 = m2.state_dict()
if is_using_pp() and gpc.get_local_rank(ParallelMode.TENSOR) == 0:
sd2 = gather_pipeline_parallel_state_dict(sd2)
print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd2)}\n")
if gpc.get_global_rank() == 0:
for k, v in sd1.items():
assert k in sd2
check_equal(v, sd2[k].to(torch.device("cpu")))
@pytest.mark.dist
def test_checkpoint_1d():
world_size = 8
run_func = partial(check_checkpoint_1d, world_size=world_size, port=free_port())
mp.spawn(run_func, nprocs=world_size)
if __name__ == "__main__":
test_checkpoint_1d()