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