2020 the huggingface team 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 orglicenseslicense2 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 2020 the huggingface team 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 unittest

from transformers import is_tf_available
from transformers.testing_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 GradientAccumulator, create_optimizer


@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.list_physical_devices("CPU")
        if len(physical_devices) == 1:
            tf.config.set_logical_device_configuration(
                physical_devices[0], [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]
            )
        devices = tf.config.list_logical_devices(device_type="CPU")
        strategy = tf.distribute.MirroredStrategy(devices=devices[:2])

        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])))

        @tf.function
        def accumulate(grad1, grad2):
            with strategy.scope():
                local_variables = strategy.experimental_local_results(gradient_placeholder)
                local_variables[0].assign(grad1)
                local_variables[1].assign(grad2)
                strategy.run(accumulate_on_replica, args=(gradient_placeholder,))

        @tf.function
        def apply_grad():
            with strategy.scope():
                strategy.run(apply_on_replica)

        def _check_local_values(grad1, grad2):
            values = strategy.experimental_local_results(accumulator._gradients[0])
            self.assertListAlmostEqual(values[0].value(), grad1, tol=1e-2)
            self.assertListAlmostEqual(values[1].value(), grad2, tol=1e-2)

        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)
        _check_local_values([2.0, 3.0], [1.0, -2.0])
        apply_grad()
        self.assertListAlmostEqual(variable.value(), [4.0, 3.0], tol=1e-2)
        accumulator.reset()
        self.assertEqual(accumulator.step, 0)
        _check_local_values([0.0, 0.0], [0.0, 0.0])