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c13737d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pickle
import unittest
from collections import UserDict, namedtuple
import torch
from accelerate.test_utils.testing import require_cuda
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
convert_outputs_to_fp32,
extract_model_from_parallel,
find_device,
patch_environment,
send_to_device,
)
ExampleNamedTuple = namedtuple("ExampleNamedTuple", "a b c")
class UtilsTester(unittest.TestCase):
def test_send_to_device(self):
tensor = torch.randn(5, 2)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
result1 = send_to_device(tensor, device)
self.assertTrue(torch.equal(result1.cpu(), tensor))
result2 = send_to_device((tensor, [tensor, tensor], 1), device)
self.assertIsInstance(result2, tuple)
self.assertTrue(torch.equal(result2[0].cpu(), tensor))
self.assertIsInstance(result2[1], list)
self.assertTrue(torch.equal(result2[1][0].cpu(), tensor))
self.assertTrue(torch.equal(result2[1][1].cpu(), tensor))
self.assertEqual(result2[2], 1)
result2 = send_to_device({"a": tensor, "b": [tensor, tensor], "c": 1}, device)
self.assertIsInstance(result2, dict)
self.assertTrue(torch.equal(result2["a"].cpu(), tensor))
self.assertIsInstance(result2["b"], list)
self.assertTrue(torch.equal(result2["b"][0].cpu(), tensor))
self.assertTrue(torch.equal(result2["b"][1].cpu(), tensor))
self.assertEqual(result2["c"], 1)
result3 = send_to_device(ExampleNamedTuple(a=tensor, b=[tensor, tensor], c=1), device)
self.assertIsInstance(result3, ExampleNamedTuple)
self.assertTrue(torch.equal(result3.a.cpu(), tensor))
self.assertIsInstance(result3.b, list)
self.assertTrue(torch.equal(result3.b[0].cpu(), tensor))
self.assertTrue(torch.equal(result3.b[1].cpu(), tensor))
self.assertEqual(result3.c, 1)
result4 = send_to_device(UserDict({"a": tensor, "b": [tensor, tensor], "c": 1}), device)
self.assertIsInstance(result4, UserDict)
self.assertTrue(torch.equal(result4["a"].cpu(), tensor))
self.assertIsInstance(result4["b"], list)
self.assertTrue(torch.equal(result4["b"][0].cpu(), tensor))
self.assertTrue(torch.equal(result4["b"][1].cpu(), tensor))
self.assertEqual(result4["c"], 1)
def test_patch_environment(self):
with patch_environment(aa=1, BB=2):
self.assertEqual(os.environ.get("AA"), "1")
self.assertEqual(os.environ.get("BB"), "2")
self.assertNotIn("AA", os.environ)
self.assertNotIn("BB", os.environ)
def test_can_undo_convert_outputs(self):
model = RegressionModel()
model._original_forward = model.forward
model.forward = convert_outputs_to_fp32(model.forward)
model = extract_model_from_parallel(model)
_ = pickle.dumps(model)
@require_cuda
def test_can_undo_fp16_conversion(self):
model = RegressionModel()
model._original_forward = model.forward
model.forward = torch.cuda.amp.autocast(dtype=torch.float16)(model.forward)
model.forward = convert_outputs_to_fp32(model.forward)
model = extract_model_from_parallel(model)
_ = pickle.dumps(model)
def test_find_device(self):
self.assertEqual(find_device([1, "a", torch.tensor([1, 2, 3])]), torch.device("cpu"))
self.assertEqual(find_device({"a": 1, "b": torch.tensor([1, 2, 3])}), torch.device("cpu"))
self.assertIsNone(find_device([1, "a"]))
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