ColossalAI/tests/test_fx/test_pipeline_passes.py

58 lines
1.5 KiB
Python

import pytest
import torch
import torch.nn as nn
from torch.fx import symbolic_trace
import colossalai
import colossalai.nn as col_nn
from colossalai.fx.passes.adding_split_node_pass import (
balanced_split_pass,
balanced_split_pass_v2,
split_with_split_nodes_pass,
uniform_split_pass,
)
from colossalai.testing import clear_cache_before_run
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
def pipeline_pass_test_helper(model, data, pass_func):
origin_output = model(data)
symbolic_traced = symbolic_trace(model)
annotated_model = pass_func(symbolic_traced, PIPELINE_SIZE)
split_model, split_submodules = split_with_split_nodes_pass(annotated_model)
output = split_model(data)
assert output.equal(origin_output)
@clear_cache_before_run()
def test_pipeline_passes():
model = MLP(MODEL_DIM)
data = torch.rand(BATCH_SIZE, MODEL_DIM)
pipeline_pass_test_helper(model, data, balanced_split_pass)
pipeline_pass_test_helper(model, data, balanced_split_pass_v2)
pipeline_pass_test_helper(model, data, uniform_split_pass)
if __name__ == '__main__':
test_pipeline_passes()