mirror of
https://github.com/facebookresearch/fairseq.git
synced 2024-10-26 17:32:57 +03:00
a48f235636
Summary: Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1357 Reviewed By: alexeib Differential Revision: D24377772 fbshipit-source-id: 51581af041d42d62166b33a35a1a4228b1a76f0c
135 lines
4.3 KiB
Python
135 lines
4.3 KiB
Python
# 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()
|