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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import collections
import os
import shutil
import tempfile
import unittest
import numpy as np
import torch
from scripts.average_checkpoints import average_checkpoints
from torch import nn
class ModelWithSharedParameter(nn.Module):
def __init__(self):
super(ModelWithSharedParameter, self).__init__()
self.embedding = nn.Embedding(1000, 200)
self.FC1 = nn.Linear(200, 200)
self.FC2 = nn.Linear(200, 200)
# tie weight in FC2 to FC1
self.FC2.weight = nn.Parameter(self.FC1.weight)
self.FC2.bias = nn.Parameter(self.FC1.bias)
self.relu = nn.ReLU()
def forward(self, input):
return self.FC2(self.ReLU(self.FC1(input))) + self.FC1(input)
class TestAverageCheckpoints(unittest.TestCase):
def test_average_checkpoints(self):
params_0 = collections.OrderedDict(
[
("a", torch.DoubleTensor([100.0])),
("b", torch.FloatTensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])),
("c", torch.IntTensor([7, 8, 9])),
]
)
params_1 = collections.OrderedDict(
[
("a", torch.DoubleTensor([1.0])),
("b", torch.FloatTensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])),
("c", torch.IntTensor([2, 2, 2])),
]
)
params_avg = collections.OrderedDict(
[
("a", torch.DoubleTensor([50.5])),
("b", torch.FloatTensor([[1.0, 1.5, 2.0], [2.5, 3.0, 3.5]])),
# We expect truncation for integer division
("c", torch.IntTensor([4, 5, 5])),
]
)
fd_0, path_0 = tempfile.mkstemp()
fd_1, path_1 = tempfile.mkstemp()
torch.save(collections.OrderedDict([("model", params_0)]), path_0)
torch.save(collections.OrderedDict([("model", params_1)]), path_1)
output = average_checkpoints([path_0, path_1])["model"]
os.close(fd_0)
os.remove(path_0)
os.close(fd_1)
os.remove(path_1)
for (k_expected, v_expected), (k_out, v_out) in zip(
params_avg.items(), output.items()
):
self.assertEqual(
k_expected,
k_out,
"Key mismatch - expected {} but found {}. "
"(Expected list of keys: {} vs actual list of keys: {})".format(
k_expected, k_out, params_avg.keys(), output.keys()
),
)
np.testing.assert_allclose(
v_expected.numpy(),
v_out.numpy(),
err_msg="Tensor value mismatch for key {}".format(k_expected),
)
def test_average_checkpoints_with_shared_parameters(self):
def _construct_model_with_shared_parameters(path, value):
m = ModelWithSharedParameter()
nn.init.constant_(m.FC1.weight, value)
torch.save({"model": m.state_dict()}, path)
return m
tmpdir = tempfile.mkdtemp()
paths = []
path = os.path.join(tmpdir, "m1.pt")
m1 = _construct_model_with_shared_parameters(path, 1.0)
paths.append(path)
path = os.path.join(tmpdir, "m2.pt")
m2 = _construct_model_with_shared_parameters(path, 2.0)
paths.append(path)
path = os.path.join(tmpdir, "m3.pt")
m3 = _construct_model_with_shared_parameters(path, 3.0)
paths.append(path)
new_model = average_checkpoints(paths)
self.assertTrue(
torch.equal(
new_model["model"]["embedding.weight"],
(m1.embedding.weight + m2.embedding.weight + m3.embedding.weight) / 3.0,
)
)
self.assertTrue(
torch.equal(
new_model["model"]["FC1.weight"],
(m1.FC1.weight + m2.FC1.weight + m3.FC1.weight) / 3.0,
)
)
self.assertTrue(
torch.equal(
new_model["model"]["FC2.weight"],
(m1.FC2.weight + m2.FC2.weight + m3.FC2.weight) / 3.0,
)
)
shutil.rmtree(tmpdir)
if __name__ == "__main__":
unittest.main()
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