ColossalAI/tests/test_fx/test_comm_size_compute.py

54 lines
1.7 KiB
Python

import torch
from torch.fx import symbolic_trace
from colossalai.fx._compatibility import is_compatible_with_meta
from colossalai.fx.passes.adding_split_node_pass import split_with_split_nodes_pass, uniform_split_pass
from colossalai.fx.passes.meta_info_prop import MetaInfoProp
from colossalai.fx.passes.utils import get_comm_size
from colossalai.testing import clear_cache_before_run
is_compatible = is_compatible_with_meta()
if is_compatible:
from colossalai.fx.profiler import MetaTensor
MODEL_DIM = 16
BATCH_SIZE = 8
PIPELINE_SIZE = 2
class MLP(torch.nn.Module):
def __init__(self, dim: int):
super().__init__()
self.linear1 = torch.nn.Linear(dim, dim)
self.linear2 = torch.nn.Linear(dim, dim)
self.linear3 = torch.nn.Linear(dim, dim)
self.linear4 = torch.nn.Linear(dim, dim)
def forward(self, x):
x = self.linear1(x)
x = self.linear2(x)
x = self.linear3(x)
x = self.linear4(x)
return x
@clear_cache_before_run()
def test_comm_size_compute():
model = MLP(MODEL_DIM)
input_sample = torch.rand(BATCH_SIZE, MODEL_DIM, device='meta')
gm = symbolic_trace(model)
if is_compatible:
input_sample = MetaTensor(input_sample, fake_device=next(gm.parameters()).device)
MetaInfoProp(gm).run(input_sample)
annotated_model = uniform_split_pass(gm, PIPELINE_SIZE)
split_model, split_submodules = split_with_split_nodes_pass(annotated_model)
submodule_list = list(split_model.children())
comm_size = get_comm_size(submodule_list[0], submodule_list[1])
# the shape of tensor send from partition 0 to partition 1 is (8, 16)
assert comm_size == 128
if __name__ == '__main__':
test_comm_size_compute()