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import unittest
from transformers import is_tf_available
from .utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import create_optimizer, GradientAccumulator
@require_tf
class OptimizationFTest(unittest.TestCase):
def assertListAlmostEqual(self, list1, list2, tol):
self.assertEqual(len(list1), len(list2))
for a, b in zip(list1, list2):
self.assertAlmostEqual(a, b, delta=tol)
def testGradientAccumulator(self):
accumulator = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0])])
accumulator([tf.constant([-2.0, 1.0])])
accumulator([tf.constant([-1.0, 2.0])])
with self.assertRaises(ValueError):
accumulator([tf.constant([1.0, 1.0]), tf.constant([2.0, 2.0])])
self.assertEqual(accumulator.step, 3)
self.assertEqual(len(accumulator.gradients), 1)
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist(), [-2.0, 5.0], tol=1e-2)
accumulator.reset()
self.assertEqual(accumulator.step, 0)
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist(), [0.0, 0.0], tol=1e-2)
def testGradientAccumulatorDistributionStrategy(self):
context._context = None
ops.enable_eager_execution_internal()
physical_devices = tf.config.experimental.list_physical_devices("CPU")
tf.config.experimental.set_virtual_device_configuration(
physical_devices[0],
[tf.config.experimental.VirtualDeviceConfiguration(), tf.config.experimental.VirtualDeviceConfiguration()],
)
devices = tf.config.experimental.list_logical_devices(device_type="CPU")
strategy = tf.distribute.MirroredStrategy(devices=[device.name for device in devices])
with strategy.scope():
accumulator = GradientAccumulator()
variable = tf.Variable([4.0, 3.0])
optimizer = create_optimizer(5e-5, 10, 5)
gradient_placeholder = tf.Variable([0.0, 0.0], trainable=False)
def accumulate_on_replica(gradient):
accumulator([gradient])
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients, [variable])), 1.0)
@tf.function
def accumulate(grad1, grad2):
with strategy.scope():
gradient_placeholder.values[0].assign(grad1)
gradient_placeholder.values[1].assign(grad2)
strategy.experimental_run_v2(accumulate_on_replica, args=(gradient_placeholder,))
@tf.function
def apply_grad():
with strategy.scope():
strategy.experimental_run_v2(apply_on_replica)
accumulate([1.0, 2.0], [-1.0, 1.0])
accumulate([3.0, -1.0], [-1.0, -1.0])
accumulate([-2.0, 2.0], [3.0, -2.0])
self.assertEqual(accumulator.step, 3)
self.assertListAlmostEqual(accumulator._gradients[0].values[0].value().numpy().tolist(), [2.0, 3.0], tol=1e-2)
self.assertListAlmostEqual(accumulator._gradients[0].values[1].value().numpy().tolist(), [1.0, -2.0], tol=1e-2)
apply_grad()
self.assertListAlmostEqual(variable.value().numpy().tolist(), [4.0, 3.0], tol=1e-2)
accumulator.reset()
self.assertEqual(accumulator.step, 0)
self.assertListAlmostEqual(accumulator._gradients[0].values[0].value().numpy().tolist(), [0.0, 0.0], tol=1e-2)
self.assertListAlmostEqual(accumulator._gradients[0].values[1].value().numpy().tolist(), [0.0, 0.0], tol=1e-2)
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