mostafadehghani / babi-t2t Goto Github PK
View Code? Open in Web Editor NEWThe bAbI question-answering dataset ported into T2T.
License: Apache License 2.0
The bAbI question-answering dataset ported into T2T.
License: Apache License 2.0
Thank you for this repository. May we know the license for this work? Is it MIT license?
Using command:
t2t-decoder \
> --t2t_usr_dir=~/bAbI-T2T/t2t_usr_dir \
> --data_dir=~/babi_data/data \
> --output_dir=~/babi_data/outpu \
> --problem=babi_qa_sentence_task1_10k \
> --model=babi_transformer \
> --hparams_set=universal_transformer_tiny
Ends up with:
INFO:tensorflow:Importing user module t2t_usr_dir from path /home/ubuntu/bAbI-T2T
WARNING:tensorflow:From /home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/utils/trainer_lib.py:165: RunConfig.__init__ (from tensorflow.contrib.learn.python.learn.estimators.run_config) is deprecated and will be removed in a future version.
Instructions for updating:
When switching to tf.estimator.Estimator, use tf.estimator.RunConfig instead.
INFO:tensorflow:schedule=continuous_train_and_eval
INFO:tensorflow:worker_gpu=1
INFO:tensorflow:sync=False
WARNING:tensorflow:Schedule=continuous_train_and_eval. Assuming that training is running on a single machine.
INFO:tensorflow:datashard_devices: ['gpu:0']
INFO:tensorflow:caching_devices: None
INFO:tensorflow:ps_devices: ['gpu:0']
INFO:tensorflow:Using config: {'_num_ps_replicas': 0, '_task_id': 0, 'use_tpu': False, '_tf_config': gpu_options {
per_process_gpu_memory_fraction: 1.0
}
, '_task_type': None, '_tf_random_seed': None, 't2t_device_info': {'num_async_replicas': 1}, '_keep_checkpoint_max': 20, '_model_dir': '/home/ubuntu/babi_data/outpu', '_evaluation_master': '', '_master': '', '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fa978becf60>, '_device_fn': None, '_num_worker_replicas': 0, 'data_parallelism': <tensor2tensor.utils.expert_utils.Parallelism object at 0x7fa985a760b8>, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_keep_checkpoint_every_n_hours': 10000, '_save_summary_steps': 100, '_train_distribute': None, '_is_chief': True, '_log_step_count_steps': 100, '_environment': 'local', '_session_config': gpu_options {
per_process_gpu_memory_fraction: 0.95
}
allow_soft_placement: true
graph_options {
optimizer_options {
}
}
}
WARNING:tensorflow:Estimator's model_fn (<function T2TModel.make_estimator_model_fn.<locals>.wrapping_model_fn at 0x7fa985a81378>) includes params argument, but params are not passed to Estimator.
INFO:tensorflow:Performing local inference from dataset for babi_qa_sentence_task1_10k.
INFO:tensorflow:Reading data files from /home/ubuntu/babi_data/data/babi_qa_en-10k_qa1_single-supporting-fact-dev*
INFO:tensorflow:partition: 0 num_data_files: 1
INFO:tensorflow:Tensor("ExpandDims_1:0", shape=(1, 12, 1), dtype=int64, device=/device:CPU:0)
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Setting T2TModel mode to 'infer'
INFO:tensorflow:Setting hparams.relu_dropout to 0.0
INFO:tensorflow:Setting hparams.attention_dropout to 0.0
INFO:tensorflow:Setting hparams.symbol_dropout to 0.0
INFO:tensorflow:Setting hparams.layer_prepostprocess_dropout to 0.0
INFO:tensorflow:Setting hparams.dropout to 0.0
Traceback (most recent call last):
File "/home/ubuntu/.local/bin/t2t-decoder", line 16, in <module>
tf.app.run()
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "/home/ubuntu/.local/bin/t2t-decoder", line 12, in main
t2t_decoder.main(argv)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/bin/t2t_decoder.py", line 190, in main
decode(estimator, hp, decode_hp)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/bin/t2t_decoder.py", line 103, in decode
dataset_split="test" if FLAGS.eval_use_test_set else None)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/utils/decoding.py", line 184, in decode_from_dataset
for num_predictions, prediction in enumerate(predictions):
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 533, in predict
features, None, model_fn_lib.ModeKeys.PREDICT, self.config)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 1107, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/utils/t2t_model.py", line 1155, in wrapping_model_fn
use_tpu=use_tpu)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/utils/t2t_model.py", line 1200, in estimator_model_fn
return model.estimator_spec_predict(features, use_tpu=use_tpu)
TypeError: estimator_spec_predict() got an unexpected keyword argument 'use_tpu'
Adding use_tpu to https://github.com/MostafaDehghani/bAbI-T2T/blob/master/t2t_usr_dir/babi_transformer.py#L37 in a manner of:
def estimator_spec_predict(self, features, use_tpu=False):
Generates another error of:
INFO:tensorflow:Importing user module t2t_usr_dir from path /home/ubuntu/bAbI-T2T
WARNING:tensorflow:From /home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/utils/trainer_lib.py:165: RunConfig.__init__ (from tensorflow.contrib.learn.python.learn.estimators.run_config) is deprecated and will be removed in a future version.
Instructions for updating:
When switching to tf.estimator.Estimator, use tf.estimator.RunConfig instead.
INFO:tensorflow:schedule=continuous_train_and_eval
INFO:tensorflow:worker_gpu=1
INFO:tensorflow:sync=False
WARNING:tensorflow:Schedule=continuous_train_and_eval. Assuming that training is running on a single machine.
INFO:tensorflow:datashard_devices: ['gpu:0']
INFO:tensorflow:caching_devices: None
INFO:tensorflow:ps_devices: ['gpu:0']
INFO:tensorflow:Using config: {'_save_summary_steps': 100, '_evaluation_master': '', '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f85d36ec8d0>, '_task_type': None, '_num_worker_replicas': 0, '_is_chief': True, '_environment': 'local', '_session_config': gpu_options {
per_process_gpu_memory_fraction: 0.95
}
allow_soft_placement: true
graph_options {
optimizer_options {
}
}
, '_tf_random_seed': None, '_num_ps_replicas': 0, '_keep_checkpoint_max': 20, '_tf_config': gpu_options {
per_process_gpu_memory_fraction: 1.0
}
, 'data_parallelism': <tensor2tensor.utils.expert_utils.Parallelism object at 0x7f85eb9222e8>, '_keep_checkpoint_every_n_hours': 10000, '_save_checkpoints_secs': None, '_device_fn': None, '_log_step_count_steps': 100, '_master': '', '_model_dir': '/home/ubuntu/babi_data/outpu', 't2t_device_info': {'num_async_replicas': 1}, '_save_checkpoints_steps': 1000, '_task_id': 0, 'use_tpu': False, '_train_distribute': None}
WARNING:tensorflow:Estimator's model_fn (<function T2TModel.make_estimator_model_fn.<locals>.wrapping_model_fn at 0x7f85dc766840>) includes params argument, but params are not passed to Estimator.
INFO:tensorflow:Performing local inference from dataset for babi_qa_sentence_task1_10k.
INFO:tensorflow:Reading data files from /home/ubuntu/babi_data/data/babi_qa_en-10k_qa1_single-supporting-fact-dev*
INFO:tensorflow:partition: 0 num_data_files: 1
INFO:tensorflow:Tensor("ExpandDims_1:0", shape=(1, 12, 1), dtype=int64, device=/device:CPU:0)
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Setting T2TModel mode to 'infer'
INFO:tensorflow:Setting hparams.symbol_dropout to 0.0
INFO:tensorflow:Setting hparams.dropout to 0.0
INFO:tensorflow:Setting hparams.attention_dropout to 0.0
INFO:tensorflow:Setting hparams.layer_prepostprocess_dropout to 0.0
INFO:tensorflow:Setting hparams.relu_dropout to 0.0
INFO:tensorflow:Greedy Decoding
Traceback (most recent call last):
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1589, in _create_c_op
c_op = c_api.TF_FinishOperation(op_desc)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimension must be 5 but is 4 for 'babi_transformer/body/parallel_0/body/encoder/layer_0/self_attention/multihead_attention/split_heads/transpose' (op: 'Transpose') with input shapes: [?,71,12,4,32], [4].
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/ubuntu/.local/bin/t2t-decoder", line 16, in <module>
tf.app.run()
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "/home/ubuntu/.local/bin/t2t-decoder", line 12, in main
t2t_decoder.main(argv)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/bin/t2t_decoder.py", line 190, in main
decode(estimator, hp, decode_hp)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/bin/t2t_decoder.py", line 103, in decode
dataset_split="test" if FLAGS.eval_use_test_set else None)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/utils/decoding.py", line 184, in decode_from_dataset
for num_predictions, prediction in enumerate(predictions):
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 533, in predict
features, None, model_fn_lib.ModeKeys.PREDICT, self.config)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 1107, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/utils/t2t_model.py", line 1155, in wrapping_model_fn
use_tpu=use_tpu)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/utils/t2t_model.py", line 1200, in estimator_model_fn
return model.estimator_spec_predict(features, use_tpu=use_tpu)
File "/home/ubuntu/bAbI-T2T/t2t_usr_dir/babi_transformer.py", line 43, in estimator_spec_predict
alpha=decode_hparams.alpha, decode_length=decode_hparams.extra_length)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/utils/t2t_model.py", line 593, in infer
results = self._greedy_infer(features, decode_length, use_tpu)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/models/transformer.py", line 226, in _greedy_infer
self._fast_decode(features, decode_length))
File "/home/ubuntu/bAbI-T2T/t2t_usr_dir/babi_transformer.py", line 425, in _fast_decode
features["target_space_id"], hparams)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/utils/expert_utils.py", line 231, in __call__
outputs.append(fns[i](*my_args[i], **my_kwargs[i]))
File "/home/ubuntu/bAbI-T2T/t2t_usr_dir/babi_transformer.py", line 600, in encode
save_weights_to=self.attention_weights)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/models/transformer.py", line 1220, in transformer_encoder
vars_3d=hparams.get("attention_variables_3d"))
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/layers/common_attention.py", line 2926, in multihead_attention
q = split_heads(q, num_heads)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/utils/expert_utils.py", line 58, in decorated
return f(*args, **kwargs)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/layers/common_attention.py", line 1109, in split_heads
return tf.transpose(split_last_dimension(x, num_heads), [0, 2, 1, 3])
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py", line 1408, in transpose
ret = transpose_fn(a, perm, name=name)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py", line 8636, in transpose
"Transpose", x=x, perm=perm, name=name)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 3414, in create_op
op_def=op_def)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1756, in __init__
control_input_ops)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1592, in _create_c_op
raise ValueError(str(e))
ValueError: Dimension must be 5 but is 4 for 'babi_transformer/body/parallel_0/body/encoder/layer_0/self_attention/multihead_attention/split_heads/transpose' (op: 'Transpose') with input shapes: [?,71,12,4,32], [4].
Configuration:
tensor2tensor (1.6.6)
tensorflow-gpu (1.9.0)
bAbI-T2T repo up to date
I’m just trying to run the example in the readme but it’s giving the error. How can we fix that? Thank you.
t2t-trainer \
--t2t_usr_dir=~/bAbI-T2T/t2t_usr_dir \
--tmp_dir=~/babi_data/tmp \
--data_dir=~/babi_data/data \
--output_dir=~/babi_data/output \
--problem=babi_qa_sentence_task1_10k \
--model=babi_transformer \
--hparams_set=transformer_tiny \
--train_steps=100000
AttributeError: 'HParams' object has no attribute 'modality'
Using command:
t2t-datagen --problem=babi_qa_sentence_task15_10k --t2t_usr_dir=t2t_usr_dir --data_dir=t2t_data --tmp_dir=t2t_data/tmp
Ends up with:
Traceback (most recent call last):
File "/home/ubuntu/.local/bin/t2t-datagen", line 27, in <module>
tf.app.run()
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "/home/ubuntu/.local/bin/t2t-datagen", line 23, in main
t2t_datagen.main(argv)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/bin/t2t_datagen.py", line 182, in main
generate_data_for_registered_problem(problem)
File "/home/ubuntu/.local/lib/python3.5/site-packages/tensor2tensor/bin/t2t_datagen.py", line 232, in generate_data_for_registered_problem
problem.generate_data(data_dir, tmp_dir, task_id)
File "/home/ubuntu/bAbI-T2T/t2t_usr_dir/babi_qa.py", line 202, in generate_data
encoder = self.get_or_create_vocab(data_dir, tmp_dir)
AttributeError: 'BabiQaSentenceTask15_10k' object has no attribute 'get_or_create_vocab'
I'm trying to reproduce the bAbI joint training results in the Universal Transformer paper (UT w/o ACT). My scripts are:
t2t-datagen \
--t2t_usr_dir=t2t_usr_dir \
--tmp_dir=babi_data/tmp \
--data_dir=babi_data/data \
--problem=babi_qa_sentence_all_tasks_10k
t2t-trainer \
--t2t_usr_dir=t2t_usr_dir \
--tmp_dir=babi_data/tmp \
--data_dir=babi_data/data \
--output_dir=babi_data/output \
--problem=babi_qa_sentence_all_tasks_10k \
--model=babi_universal_transformer \
--hparams_set=universal_transformer_tiny \
--train_steps=100000
However, I can't reproduce the results, getting test accuracy around 60% (I didn't train for 100000 steps. But the curve seems already plateau). In particular, I'm not sure about three things:
In transformer_base, the default batch_size is 4096:
https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py#L1312
Unlike T2T's BabiQaConcat which inherits from TextProblem, this repo's BabiQaSentence inherits from Problem. There batch_size_means_tokens is set to False. So 4096 means a quite large batch (4096 * 70 * 12 = 3.4M tokens). I got OOM error with a Titan 1080 ti card. So I changed batch_size to 512.
In a 3 Sep commit, you changed default transformer_ffn_type from sepconv to fc.
tensorflow/tensor2tensor@e496897
Should I use sepconv to run the experiments?
T2T code has undergone many changes since this repo was out. Will that impact the results?
What actual batch_size did you use? Did you change any other hparams when running t2t-datagen and t2t-trainer?
It would be very helpful if you could share your flags.txt, flags_t2t.txt and hparams.json files. Attached are mine.
First of all, great work with the Uni Transformer. Then I have some question about the implementation and the hyperparameters:
From the paper and the implementation, I miss how to use the transformer-decoder (if used). Indeed, I have just tried to use the transformer-encoder by concatenating [story, query] and taking the last vector in output from the encoder to predict over the vocabulary. In this way, Uni Transformer and Transformer worked pretty well (Uni Better as u mentioned in the paper). From the implementation, I don't get if you guys use also the decoder. If so how? could you give a bit more details
I couldn't find the hyperparameter used in the experiments, are you going to release it somewhere?
Thanks a lot for your great work, looking forward to hear from you.
Best
Andrea
First of all, great work with the Uni Transformer. Then I have some question about the implementation and the hyperparameters:
From the paper and the implementation, I miss how to use the transformer-decoder (if used). Indeed, I have just tried to use the transformer-encoder by concatenating [story, query] and taking the last vector in output from the encoder to predict over the vocabulary. In this way, Uni Transformer and Transformer worked pretty well (Uni Better as u mentioned in the paper). From the implementation, I don't get if you guys use also the decoder. If so how? could you give a bit more details
I couldn't find the hyperparameter used in the experiments, are you going to release it somewhere?
Thanks a lot for your great work, looking forward to hear from you.
Best
Andrea
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