I am trying to run Universal Transformers for the English to German translation task. I am getting the error that the tf graphs are not the same. I searched and found a similar closed issue but the linked solution to the issue is unavailable.
2021-02-17 10:36:35.448784: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
WARNING:tensorflow:From /home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_gan/python/estimator/tpu_gan_estimator.py:42: The name tf.esti
mator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.
WARNING:tensorflow:From /home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_gan/python/estimator/tpu_gan_estimator.py:42: The name tf.esti
mator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.
INFO:tensorflow:Loading hparams from existing json /home/mila/m/mittalsa/tensor2tensor/t2t_train/translate_ende_wmt32k/universal_transformer-universal_transformer_
base/hparams.json
I0217 10:36:58.888788 139921682896704 hparams_lib.py:64] Loading hparams from existing json /home/mila/m/mittalsa/tensor2tensor/t2t_train/translate_ende_wmt32k/uni
versal_transformer-universal_transformer_base/hparams.json
INFO:tensorflow:Configuring DataParallelism to replicate the model.
I0217 10:36:58.893124 139921682896704 trainer_lib.py:271] Configuring DataParallelism to replicate the model.
INFO:tensorflow:schedule=continuous_train_and_eval
I0217 10:36:58.893256 139921682896704 devices.py:76] schedule=continuous_train_and_eval
INFO:tensorflow:worker_gpu=1
I0217 10:36:58.893335 139921682896704 devices.py:77] worker_gpu=1
INFO:tensorflow:sync=False
I0217 10:36:58.893400 139921682896704 devices.py:78] sync=False
WARNING:tensorflow:Schedule=continuous_train_and_eval. Assuming that training is running on a single machine.
W0217 10:36:58.893464 139921682896704 devices.py:141] Schedule=continuous_train_and_eval. Assuming that training is running on a single machine.
INFO:tensorflow:datashard_devices: ['gpu:0']
I0217 10:36:58.894149 139921682896704 devices.py:170] datashard_devices: ['gpu:0']
INFO:tensorflow:caching_devices: None
I0217 10:36:58.894598 139921682896704 devices.py:171] caching_devices: None
INFO:tensorflow:ps_devices: ['gpu:0']
I0217 10:36:58.894993 139921682896704 devices.py:172] ps_devices: ['gpu:0']
INFO:tensorflow:Using config: {'_model_dir': '/home/mila/m/mittalsa/tensor2tensor/t2t_train/translate_ende_wmt32k/universal_transformer-universal_transformer_base', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': gpu_options {
per_process_gpu_memory_fraction: 0.95
}
}
allow_soft_placement: true
graph_options {
optimizer_options {
global_jit_level: OFF
}
}
isolate_session_state: true
, '_keep_checkpoint_max': 20, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, 'use_tpu': False, 't2t_device_info': {'num_async_replicas': 1}, 'data_parallelism': <tensor2tensor.utils.expert_utils.Parallelism object at 0x7f41a3b9b978>}
I0217 10:36:59.021266 139921682896704 estimator.py:191] Using config: {'_model_dir': '/home/mila/m/mittalsa/tensor2tensor/t2t_train/translate_ende_wmt32k/universal_transformer-universal_transformer_base', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': gpu_options {
per_process_gpu_memory_fraction: 0.95
}
allow_soft_placement: true
graph_options {
optimizer_options {
global_jit_level: OFF
}
}
isolate_session_state: true
, '_keep_checkpoint_max': 20, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, 'use_tpu': False, 't2t_device_info': {'num_async_replicas': 1}, 'data_parallelism': <tensor2tensor.utils.expert_utils.Parallelism object at 0x7f41a3b9b978>}
WARNING:tensorflow:Estimator's model_fn (<function T2TModel.make_estimator_model_fn.<locals>.wrapping_model_fn at 0x7f41a3da70d0>) includes params argument, but params are not passed to Estimator.
W0217 10:36:59.021582 139921682896704 model_fn.py:629] Estimator's model_fn (<function T2TModel.make_estimator_model_fn.<locals>.wrapping_model_fn at 0x7f41a3da70d0>) includes params argument, but params are not passed to Estimator.
WARNING:tensorflow:ValidationMonitor only works with --schedule=train_and_evaluate
W0217 10:36:59.021708 139921682896704 trainer_lib.py:795] ValidationMonitor only works with --schedule=train_and_evaluate
INFO:tensorflow:Not using Distribute Coordinator.
I0217 10:36:59.036123 139921682896704 estimator_training.py:186] Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
I0217 10:36:59.036407 139921682896704 training.py:645] Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 1000 or save_checkpoints_secs None.
I0217 10:36:59.036719 139921682896704 training.py:733] Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 1000 or save_checkpoints_secs None.
WARNING:tensorflow:From /home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
W0217 10:36:59.066916 139921682896704 deprecation.py:339] From /home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
2021-02-17 10:36:59.084372: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-02-17 10:36:59.089130: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-02-17 10:36:59.164164: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:b1:00.0 name: Quadro RTX 8000 computeCapability: 7.5
coreClock: 1.77GHz coreCount: 72 deviceMemorySize: 47.46GiB deviceMemoryBandwidth: 625.94GiB/s
2021-02-17 10:36:59.164344: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-02-17 10:36:59.172861: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-02-17 10:36:59.173078: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-02-17 10:36:59.178167: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-02-17 10:36:59.181463: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-02-17 10:36:59.188911: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-02-17 10:36:59.192324: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2021-02-17 10:36:59.194594: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-02-17 10:36:59.203122: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
INFO:tensorflow:Reading data files from /home/mila/m/mittalsa/tensor2tensor/t2t_data/translate_ende_wmt32k-train*
I0217 10:36:59.222244 139921682896704 problem.py:653] Reading data files from /home/mila/m/mittalsa/tensor2tensor/t2t_data/translate_ende_wmt32k-train*
INFO:tensorflow:partition: 0 num_data_files: 100
I0217 10:36:59.262548 139921682896704 problem.py:679] partition: 0 num_data_files: 100
WARNING:tensorflow:From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/data_generators/problem.py:689: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.
W0217 10:36:59.265164 139921682896704 deprecation.py:339] From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/data_generators/problem.py:689: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.
WARNING:tensorflow:From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/data_reader.py:276: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and:
`tf.data.TFRecordDataset(path)`
W0217 10:36:59.347080 139921682896704 deprecation.py:339] From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/data_reader.py:276: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and:
`tf.data.TFRecordDataset(path)`
WARNING:tensorflow:From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/data_reader.py:38: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0217 10:36:59.483155 139921682896704 deprecation.py:339] From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/data_reader.py:38: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0217 10:36:59.483155 139921682896704 deprecation.py:339] From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/data_reader.py:38: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
WARNING:tensorflow:From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/data_reader.py:234: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0217 10:37:00.108635 139921682896704 deprecation.py:339] From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/data_reader.py:234: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
INFO:tensorflow:Calling model_fn.
I0217 10:37:00.293850 139921682896704 estimator.py:1162] Calling model_fn.
INFO:tensorflow:Setting T2TModel mode to 'train'
I0217 10:37:00.320769 139921682896704 t2t_model.py:2267] Setting T2TModel mode to 'train'
INFO:tensorflow:Using variable initializer: uniform_unit_scaling
I0217 10:37:01.047983 139921682896704 api.py:479] Using variable initializer: uniform_unit_scaling
INFO:tensorflow:Transforming feature 'inputs' with symbol_modality_33510_1024.bottom
I0217 10:37:07.056139 139921682896704 api.py:479] Transforming feature 'inputs' with symbol_modality_33510_1024.bottom
INFO:tensorflow:Transforming feature 'targets' with symbol_modality_33510_1024.targets_bottom
I0217 10:37:08.512664 139921682896704 api.py:479] Transforming feature 'targets' with symbol_modality_33510_1024.targets_bottom
INFO:tensorflow:Building model body
I0217 10:37:08.603129 139921682896704 api.py:479] Building model body
WARNING:tensorflow:From /home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py:201: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
W0217 10:37:09.968957 139921682896704 deprecation.py:537] From /home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py:201: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
I0217 10:37:11.088009 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:11.111042 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:11.127041 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:11.190722 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:11.235233 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:11.259665 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:13.880186 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:13.896136 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:13.911811 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:13.972910 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:14.009448 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:14.025386 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:14.041645 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:14.103353 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:14.140556 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:14.165198 139921682896704 common_layers.py:51] Running in V2 mode, using Keras layers.
INFO:tensorflow:Transforming body output with symbol_modality_33510_1024.top
I0217 10:37:16.450344 139921682896704 api.py:479] Transforming body output with symbol_modality_33510_1024.top
INFO:tensorflow:Base learning rate: 2.000000
I0217 10:37:20.661118 139921682896704 learning_rate.py:29] Base learning rate: 2.000000
INFO:tensorflow:Trainable Variables Total size: 63744000
I0217 10:37:20.667574 139921682896704 optimize.py:355] Trainable Variables Total size: 63744000
INFO:tensorflow:Non-trainable variables Total size: 5
I0217 10:37:20.669589 139921682896704 optimize.py:355] Non-trainable variables Total size: 5
INFO:tensorflow:Using optimizer adam
I0217 10:37:20.669764 139921682896704 optimize.py:200] Using optimizer adam
INFO:tensorflow:Done calling model_fn.
I0217 10:37:22.981385 139921682896704 estimator.py:1164] Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
I0217 10:37:22.982550 139921682896704 basic_session_run_hooks.py:546] Create CheckpointSaverHook.
Traceback (most recent call last):
File "/home/mila/m/mittalsa/.conda/envs/t2t/bin/t2t-trainer", line 33, in <module>
tf.app.run(main)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/absl/app.py", line 300, in run
_run_main(main, args)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/absl/app.py", line 251, in _run_main
sys.exit(main(argv))
File "/home/mila/m/mittalsa/.conda/envs/t2t/bin/t2t-trainer", line 28, in main
t2t_trainer.main(argv)
File "/home/mila/m/mittalsa/tensor2tensor/tensor2tensor/bin/t2t_trainer.py", line 418, in main
execute_schedule(exp)
File "/home/mila/m/mittalsa/tensor2tensor/tensor2tensor/bin/t2t_trainer.py", line 371, in execute_schedule
getattr(exp, FLAGS.schedule)()
File "/home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/trainer_lib.py", line 468, in continuous_train_and_eval
self._eval_spec)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/training.py", line 505, in train_and_evaluate
return executor.run()
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/training.py", line 646, in run
return self.run_local()
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/training.py", line 747, in run_local
saving_listeners=saving_listeners)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 349, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1175, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1208, in _train_model_default
saving_listeners)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1510, in _train_with_estimator_spec
save_graph_def=self._config.checkpoint_save_graph_def) as mon_sess:
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 604, in MonitoredTrainingSession
stop_grace_period_secs=stop_grace_period_secs)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 1038, in __init__
stop_grace_period_secs=stop_grace_period_secs)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 749, in __init__
self._sess = _RecoverableSession(self._coordinated_creator)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 1231, in __init__
_WrappedSession.__init__(self, self._create_session())
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 1236, in _create_session
return self._sess_creator.create_session()
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 902, in create_session
self.tf_sess = self._session_creator.create_session()
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 660, in create_session
self._scaffold.finalize()
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 232, in finalize
summary.merge_all)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 297, in get_or_default
op = default_constructor()
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/summary/summary.py", line 406, in merge_all
return merge(summary_ops, name=name)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/summary/summary.py", line 370, in merge
with _ops.name_scope(name, 'Merge', inputs):
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 6495, in __enter__
g_from_inputs = _get_graph_from_inputs(self._values)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 6130, in _get_graph_from_inputs
_assert_same_graph(original_graph_element, graph_element)
File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 6065, in _assert_same_graph
(item, original_item, graph, original_graph))
ValueError: Tensor("rec_layer_0/self_attention/multihead_attention/dot_product_attention/attention:0", shape=(), dtype=string, device=/device:GPU:0) must be from the same graph as Tensor("universal_transformer_hparams:0", shape=(), dtype=string) (graphs are FuncGraph(name=universal_transformer_parallel_0_5_universal_transformer_universal_transformer_body_encoder_universal_transformer_basic_foldl_while_body_988_rewritten, id=139918132471568) and <tensorflow.python.framework.ops.Graph object at 0x7f41a3d894e0>).
sarthmit
commented
3 years ago
Description
I am trying to run Universal Transformers for the English to German translation task. I am getting the error that the tf graphs are not the same. I searched and found a similar closed issue but the linked solution to the issue is unavailable.
$ pip freeze | grep tensor
For bugs: reproduction and error logs
Error logs: