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Re-enable multi process pool runner tests

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PiperOrigin-RevId: 339084496
Change-Id: I0085dcc86ddcf8ff237234bc15e50815587442b5
This commit is contained in:
Ran Chen 2020-10-26 11:46:59 -07:00 committed by TensorFlower Gardener
parent 7f532163ff
commit e357988ab3
2 changed files with 39 additions and 45 deletions
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4
tensorflow/python/distribute/multi_process_runner_test.py
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@ -590,13 +590,9 @@ class MultiProcessPoolRunnerTest(test.TestCase):
self.assertAllEqual(result, [1, 1])
def test_global_pool(self):
if multi_process_runner.is_oss():
self.skipTest('TODO(b/170360740): Failing in OSS')
_global_pool.run(fn_that_does_nothing)
def test_nested_pool(self):
if multi_process_runner.is_oss():
self.skipTest('TODO(b/170360740): Failing in OSS')
def fn():
# This runs in sub processes, so they are each using their own

80
tensorflow/python/keras/distribute/collective_all_reduce_strategy_test.py
View File

@ -309,52 +309,50 @@ class LocalCollectiveAllReduceStrategy(
with policy.policy_scope('mixed_float16'):
self._test_mixed_precision(None, None, required_gpus)
# TODO(b/170360740): Timeout in OSS
if not multi_process_runner.is_oss():
@ds_combinations.generate(
combinations.combine(
strategy=[
strategy_combinations.multi_worker_mirrored_2x1_cpu,
strategy_combinations.multi_worker_mirrored_2x1_gpu,
],
mode=['eager']))
class DistributedCollectiveAllReduceStrategyEagerTest(test.TestCase,
parameterized.TestCase):
@ds_combinations.generate(
combinations.combine(
strategy=[
strategy_combinations.multi_worker_mirrored_2x1_cpu,
strategy_combinations.multi_worker_mirrored_2x1_gpu,
],
mode=['eager']))
class DistributedCollectiveAllReduceStrategyEagerTest(test.TestCase,
parameterized.TestCase):
def testFitWithoutStepsPerEpochPartialBatch(self, strategy):
def testFitWithoutStepsPerEpochPartialBatch(self, strategy):
def _model_fn():
x = layers.Input(shape=(1,), name='input')
y = layers.Dense(1, name='dense')(x)
model = training.Model(x, y)
return model
def _model_fn():
x = layers.Input(shape=(1,), name='input')
y = layers.Dense(1, name='dense')(x)
model = training.Model(x, y)
return model
def _get_dataset():
inputs = array_ops.expand_dims_v2(
constant_op.constant(range(10)), axis=1)
targets = array_ops.expand_dims_v2(
constant_op.constant(range(10)), axis=1)
# Make global batch size 12 for 2 replicas and a non-repeated dataset
# with 10 elements so that we have partial batch
dataset = dataset_ops.Dataset.from_tensor_slices(
(inputs, targets)).batch(
12, drop_remainder=False)
return dataset
def _get_dataset():
inputs = array_ops.expand_dims_v2(
constant_op.constant(range(10)), axis=1)
targets = array_ops.expand_dims_v2(
constant_op.constant(range(10)), axis=1)
# Make global batch size 12 for 2 replicas and a non-repeated dataset
# with 10 elements so that we have partial batch
dataset = dataset_ops.Dataset.from_tensor_slices(
(inputs, targets)).batch(
12, drop_remainder=False)
return dataset
with strategy.scope():
optimizer_fn = gradient_descent_keras.SGD
optimizer = optimizer_fn(0.001)
model = _model_fn()
loss = 'mse'
metrics = ['mae']
model.compile(optimizer, loss, metrics=metrics)
dataset = _get_dataset()
kernel_before = model.get_weights()[0][0]
model.fit(dataset, epochs=10)
kernel_after = model.get_weights()[0][0]
self.assertNotEqual(kernel_before, kernel_after)
self.assertGreater(abs(kernel_before - 1), abs(kernel_after - 1))
with strategy.scope():
optimizer_fn = gradient_descent_keras.SGD
optimizer = optimizer_fn(0.001)
model = _model_fn()
loss = 'mse'
metrics = ['mae']
model.compile(optimizer, loss, metrics=metrics)
dataset = _get_dataset()
kernel_before = model.get_weights()[0][0]
model.fit(dataset, epochs=10)
kernel_after = model.get_weights()[0][0]
self.assertNotEqual(kernel_before, kernel_after)
self.assertGreater(abs(kernel_before - 1), abs(kernel_after - 1))
if __name__ == '__main__':