diff options
Diffstat (limited to 'tensorflow/contrib/distribute/python/minimize_loss_test.py')
| -rw-r--r-- | tensorflow/contrib/distribute/python/minimize_loss_test.py | 115 |
1 files changed, 80 insertions, 35 deletions
diff --git a/tensorflow/contrib/distribute/python/minimize_loss_test.py b/tensorflow/contrib/distribute/python/minimize_loss_test.py index e134fe34e1..d2054715f1 100644 --- a/tensorflow/contrib/distribute/python/minimize_loss_test.py +++ b/tensorflow/contrib/distribute/python/minimize_loss_test.py @@ -44,13 +44,16 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): combinations.distributions_and_v1_optimizers(), combinations.combine(mode=["graph"], use_callable_loss=[True, False]) + combinations.combine(mode=["eager"], use_callable_loss=[True]), - combinations.combine(is_tpu=[False])) + - combinations.combine( - distribution=[combinations.tpu_strategy], - optimizer_fn=[combinations.adam_optimizer_v1_fn], - mode=["graph"], - use_callable_loss=[False], - is_tpu=[True])) + combinations.combine(is_tpu=[False])) + combinations.combine( + distribution=[combinations.tpu_strategy], + optimizer_fn=[ + combinations.adam_optimizer_v1_fn, + # TODO(isaprykin): Make Adam v2 work with while_loops + # and TPUs. + ], + mode=["graph"], + use_callable_loss=[False], + is_tpu=[True])) def testTrainNetwork(self, distribution, optimizer_fn, use_callable_loss, is_tpu): with distribution.scope(): @@ -101,7 +104,8 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): distribution=[combinations.tpu_strategy], optimizer_fn=[ combinations.adam_optimizer_v1_fn, - combinations.gradient_descent_optimizer_v1_fn + combinations.gradient_descent_optimizer_v1_fn, + combinations.gradient_descent_optimizer_v2_fn, ], mode=["graph"], is_tpu=[True])) @@ -171,13 +175,28 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): set(created_variables)) @combinations.generate( - combinations.times(combinations.distributions_and_v1_optimizers(), - combinations.combine( - mode=["graph", "eager"], - momentum=[0.8, 0.9, 0.99], - renorm=[False, True]))) + combinations.times( + combinations.combine(momentum=[0.8, 0.9, 0.99], renorm=[False, True]), + combinations.times( + combinations.distributions_and_v1_optimizers(), + combinations.combine( + mode=["graph", "eager"], + is_tpu=[False], + # TODO(isaprykin): Allow False here. Currently subsequent + # towers will re-execute UPDATE_OPS of previous towers. + update_ops_in_cross_tower_mode=[True])) + + combinations.combine( + distribution=[combinations.tpu_strategy_single_iteration], + optimizer_fn=[ + combinations.gradient_descent_optimizer_v1_fn, + combinations.gradient_descent_optimizer_v2_fn + ], + mode=["graph"], + is_tpu=[True], + update_ops_in_cross_tower_mode=[False]))) def testTrainNetworkWithBatchNorm(self, distribution, optimizer_fn, momentum, - renorm): + renorm, is_tpu, + update_ops_in_cross_tower_mode): """Verifies that moving mean updates are reduced across towers.""" with distribution.scope(): num_towers = len(distribution.worker_devices) @@ -185,7 +204,8 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): optimizer_fn, batch_per_epoch=num_towers, momentum=momentum, - renorm=renorm) + renorm=renorm, + update_ops_in_tower_mode=not update_ops_in_cross_tower_mode) # Disable prefetching since that makes the specific input on each device # to be non deterministic, and this test relies on specific input being @@ -196,16 +216,18 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): dataset_fn).make_one_shot_iterator() def run_step(): - return control_flow_ops.group( - distribution.unwrap( - distribution.call_for_each_tower( - model_fn, - iterator.get_next(), - run_concurrently=batchnorm.built)) + - ops.get_collection(ops.GraphKeys.UPDATE_OPS)) + fetches = distribution.unwrap( + distribution.call_for_each_tower( + model_fn, iterator.get_next(), + run_concurrently=batchnorm.built)) + if update_ops_in_cross_tower_mode: + fetches += ops.get_collection(ops.GraphKeys.UPDATE_OPS) + return control_flow_ops.group(fetches) if not context.executing_eagerly(): with self.test_session() as sess: + if is_tpu: + sess.run(tpu.initialize_system()) run_step = sess.make_callable(run_step()) self.evaluate(variables_lib.global_variables_initializer()) @@ -229,22 +251,40 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): expected_moving_mean - averaged_batch_mean(i)) * (1.0 - momentum)) self.assertNear(expected_moving_means[i], moving_means[i], 0.0001) + if is_tpu: + with self.test_session() as sess: + sess.run(tpu.shutdown_system()) + @combinations.generate( combinations.times( combinations.combine( - distribution=[combinations.one_device_strategy, - combinations.mirrored_strategy_with_gpu_and_cpu, - combinations.mirrored_strategy_with_two_gpus], - optimizer_fn=[combinations.gradient_descent_optimizer_v1_fn, - combinations.gradient_descent_optimizer_v2_fn], - loss_reduction=[losses_impl.Reduction.SUM, - losses_impl.Reduction.MEAN, - losses_impl.Reduction.SUM_OVER_BATCH_SIZE, - losses_impl.Reduction.SUM_OVER_NONZERO_WEIGHTS]), - combinations.combine(mode=["graph"], use_callable_loss=[True, False]) - + combinations.combine(mode=["eager"], use_callable_loss=[True]))) + optimizer_fn=[ + combinations.gradient_descent_optimizer_v1_fn, + combinations.gradient_descent_optimizer_v2_fn + ], + loss_reduction=[ + losses_impl.Reduction.SUM, losses_impl.Reduction.MEAN, + losses_impl.Reduction.SUM_OVER_BATCH_SIZE, + losses_impl.Reduction.SUM_OVER_NONZERO_WEIGHTS + ]), + combinations.times( + combinations.combine( + distribution=[ + combinations.one_device_strategy, + combinations.mirrored_strategy_with_gpu_and_cpu, + combinations.mirrored_strategy_with_two_gpus + ], + is_tpu=[False]), + combinations.combine( + mode=["graph"], use_callable_loss=[True, False]) + + combinations.combine(mode=["eager"], use_callable_loss=[True])) + + combinations.combine( + distribution=[combinations.tpu_strategy_single_iteration], + is_tpu=[True], + mode=["graph"], + use_callable_loss=[True, False]))) def testMeanVsSum(self, distribution, optimizer_fn, loss_reduction, - use_callable_loss): + use_callable_loss, is_tpu): with distribution.scope(): all_vars = [] @@ -280,12 +320,13 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): if not context.executing_eagerly(): with self.test_session() as sess: + if is_tpu: + sess.run(tpu.initialize_system()) run_step = sess.make_callable(run_step()) self.evaluate(variables_lib.global_variables_initializer()) run_step() - self.assertEqual(distribution.num_towers, len(all_vars)) v = all_vars[0] self.assertTrue(all([v is vi for vi in all_vars[1:]])) weight = numpy.squeeze(self.evaluate(distribution.fetch(v))) @@ -312,6 +353,10 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): # One of the mean loss reductions. self.assertNear(weight, 2 + 10.6, 0.0001) + if is_tpu: + with self.test_session() as sess: + sess.run(tpu.shutdown_system()) + if __name__ == "__main__": test.main() |