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Create new TestCase classes and clean up W&B tests #304

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69 changes: 69 additions & 0 deletions src/accelerate/test_utils/testing.py
Original file line number Diff line number Diff line change
Expand Up @@ -13,15 +13,84 @@
# limitations under the License.

import asyncio
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, is_tpu_available
from ..utils import gather, is_tensorflow_available


class TempDirTestCase(unittest.TestCase):
"""
A TestCase class that keeps a single `tempfile.TemporaryDirectory` open for the duration of the class, wipes its
data at the start of a test, and then destroyes it at the end of the TestCase.

Useful for when a class or API requires a single constant folder throughout it's use, such as Weights and Biases

The temporary directory location will be stored in `self.tmpdir`
"""

@classmethod
def setUpClass(cls):
"Creates a `tempfile.TemporaryDirectory` and stores it in `cls.tmpdir`"
cls.tmpdir = tempfile.mkdtemp()

@classmethod
def tearDownClass(cls):
"Remove `cls.tmpdir` after test suite has finished"
if os.path.exists(cls.tmpdir):
shutil.rmtree(cls.tmpdir)

def setUp(self):
"Destroy all contents in `self.tmpdir`, but not `self.tmpdir`"
for path in Path(self.tmpdir).glob("**/*"):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(path)


class MockingTestCase(unittest.TestCase):
"""
A TestCase class designed to dynamically add various mockers that should be used in every test, mimicing a behavior
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of a class-wide mock when defining one normally will not do.

Useful when a mock requires specific information available only initialized after `TestCase.setUpClass`, such as
setting an environment variable with that information.

The `add_mocks` function should be ran at the end of a `TestCase`'s `setUp` function, after a call to
`super().setUp()` such as:
```python
def setUp(self):
super().setUp()
mocks = mock.patch.dict(os.environ, {"SOME_ENV_VAR", "SOME_VALUE"})
self.add_mocks(mocks)
```
"""

def add_mocks(self, mocks: Union[mock.Mock, List[mock.Mock]]):
"""
Add custom mocks for tests that should be repeated on each test. Should be called during
`MockingTestCase.setUp`, after `super().setUp()`.

Args:
mocks (`mock.Mock` or list of `mock.Mock`):
Mocks that should be added to the `TestCase` after `TestCase.setUpClass` has been run
"""
self.mocks = mocks if isinstance(mocks, (tuple, list)) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop)


def are_the_same_tensors(tensor):
state = AcceleratorState()
tensor = tensor[None].clone().to(state.device)
Expand Down
43 changes: 13 additions & 30 deletions tests/test_tracking.py
Original file line number Diff line number Diff line change
Expand Up @@ -15,15 +15,14 @@
import logging
import os
import re
import shutil
import tempfile
import unittest
from pathlib import Path
from unittest import mock

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import require_tensorflow
from accelerate.test_utils.testing import MockingTestCase, TempDirTestCase, require_tensorflow
from accelerate.utils import is_tensorflow_available


Expand All @@ -43,13 +42,11 @@ def test_init_trackers(self):
hps = None
project_name = "test_project_with_config"
with tempfile.TemporaryDirectory() as dirpath:
oldpwd = os.getcwd()
os.chdir(dirpath)
accelerator = Accelerator(log_with="tensorboard")
accelerator = Accelerator(log_with="tensorboard", logging_dir=dirpath)
config = {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
accelerator.init_trackers(project_name, config)
accelerator.end_training()
for child in Path(project_name).glob("*/**"):
for child in Path(f"{dirpath}/{project_name}").glob("*/**"):
log = list(filter(lambda x: x.is_file(), child.iterdir()))[0]
# The config log is stored one layer deeper in the logged directory
# And names are randomly generated each time
Expand All @@ -61,7 +58,6 @@ def test_init_trackers(self):
plugin_data = plugin_data_pb2.HParamsPluginData.FromString(proto_bytes)
if plugin_data.HasField("session_start_info"):
hps = dict(plugin_data.session_start_info.hparams)
os.chdir(oldpwd)

self.assertTrue(isinstance(hps, dict))
keys = list(hps.keys())
Expand All @@ -77,16 +73,14 @@ def test_log(self):
step = None
project_name = "test_project_with_log"
with tempfile.TemporaryDirectory() as dirpath:
oldpwd = os.getcwd()
os.chdir(dirpath)
accelerator = Accelerator(log_with="tensorboard")
accelerator = Accelerator(log_with="tensorboard", logging_dir=dirpath)
accelerator.init_trackers(project_name)
values = {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
accelerator.log(values, step=0)
accelerator.end_training()
# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
# Names are randomly generated each time
log = list(filter(lambda x: x.is_file(), Path(project_name).iterdir()))[0]
log = list(filter(lambda x: x.is_file(), Path(f"{dirpath}/{project_name}").iterdir()))[0]
serialized_examples = tf.data.TFRecordDataset(log)
for e in serialized_examples:
event = event_pb2.Event.FromString(e.numpy())
Expand All @@ -99,14 +93,18 @@ def test_log(self):
iteration = value.simple_value
elif value.tag == "my_text/text_summary": # Append /text_summary to the key
my_text = value.tensor.string_val[0].decode()
os.chdir(oldpwd)
self.assertAlmostEqual(total_loss, values["total_loss"])
self.assertEqual(iteration, values["iteration"])
self.assertEqual(my_text, values["my_text"])


@mock.patch.dict(os.environ, {"WANDB_MODE": "offline"})
class WandBTrackingTest(unittest.TestCase):
class WandBTrackingTest(TempDirTestCase, MockingTestCase):
def setUp(self):
super().setUp()
# wandb let's us override where logs are stored to via the WANDB_DIR env var
self.add_mocks(mock.patch.dict(os.environ, {"WANDB_DIR": self.tmpdir}))

@staticmethod
def get_value_from_log(key: str, log: str, key_occurance: int = 0):
"""
Expand All @@ -126,7 +124,7 @@ def test_init_trackers(self):
accelerator.init_trackers(project_name, config)
accelerator.end_training()
# The latest offline log is stored at wandb/latest-run/*.wandb
for child in Path("wandb/latest-run").glob("*"):
for child in Path(f"{self.tmpdir}/wandb/latest-run").glob("*"):
logger.info(child)
if child.is_file() and child.suffix == ".wandb":
with open(child, "rb") as f:
Expand All @@ -148,7 +146,7 @@ def test_log(self):
accelerator.log(values, step=0)
accelerator.end_training()
# The latest offline log is stored at wandb/latest-run/*.wandb
for child in Path("wandb/latest-run").glob("*"):
for child in Path(f"{self.tmpdir}/wandb/latest-run").glob("*"):
if child.is_file() and child.suffix == ".wandb":
with open(child, "rb") as f:
content = f.read()
Expand All @@ -159,18 +157,3 @@ def test_log(self):
self.assertEqual(self.get_value_from_log("iteration", cleaned_log), "1")
self.assertEqual(self.get_value_from_log("my_text", cleaned_log), "some_value")
self.assertEqual(self.get_value_from_log("_step", cleaned_log), "0")

def setUp(self):
os.mkdir(".wandb_tests")
os.chdir(".wandb_tests")

def tearDown(self):
if os.getcwd().endswith(".wandb_tests"):
os.chdir("..")
if os.path.exists(".wandb_tests"):
shutil.rmtree(".wandb_tests")

@classmethod
def setUpClass(cls):
if os.path.exists(".wandb_tests"):
shutil.rmtree(".wandb_tests")