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TypeError: cannot sort _thread.lock objects in Seq2Seq

I'm having problems using buckets in my Tensorflow model. When I run it with buckets = [(100, 100)] , it works fine. When I run it using buckets = [(100, 100), (200, 200)] , it doesn't work at all (stacktrace below).

Interestingly, when you start Tensorflow Seq2Seq the tutorial gives the same problem with an almost identical stacktrace. For testing purposes, a link to the repository is here .

I'm not sure what the problem is, but having multiple bucket always causes it.

This code will not work as standalone, but it is a function in which it crashes - remember that changing buckets from [(100, 100)] to [(100, 100), (200, 200)] causes a crash.

 class MySeq2Seq(object): def __init__(self, source_vocab_size, target_vocab_size, buckets, size, num_layers, batch_size, learning_rate): self.source_vocab_size = source_vocab_size self.target_vocab_size = target_vocab_size self.buckets = buckets self.batch_size = batch_size cell = single_cell = tf.nn.rnn_cell.GRUCell(size) if num_layers > 1: cell = tf.nn.rnn_cell.MultiRNNCell([single_cell] * num_layers) # The seq2seq function: we use embedding for the input and attention def seq2seq_f(encoder_inputs, decoder_inputs, do_decode): return tf.contrib.legacy_seq2seq.embedding_attention_seq2seq( encoder_inputs, decoder_inputs, cell, num_encoder_symbols=source_vocab_size, num_decoder_symbols=target_vocab_size, embedding_size=size, feed_previous=do_decode) # Feeds for inputs self.encoder_inputs = [] self.decoder_inputs = [] self.target_weights = [] for i in range(buckets[-1][0]): self.encoder_inputs.append(tf.placeholder(tf.int32, shape=[None], name="encoder{0}".format(i))) for i in range(buckets[-1][1] + 1): self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[None], name="decoder{0}".format(i))) self.target_weights.append(tf.placeholder(tf.float32, shape=[None], name="weight{0}".format(i))) # Our targets are decoder inputs shifted by one targets = [self.decoder_inputs[i + 1] for i in range(len(self.decoder_inputs) - 1)] self.outputs, self.losses = tf.contrib.legacy_seq2seq.model_with_buckets( self.encoder_inputs, self.decoder_inputs, targets, self.target_weights, [(100, 100)], lambda x, y: seq2seq_f(x, y, False)) # Gradients update operation for training the model params = tf.trainable_variables() self.updates = [] for b in range(len(buckets)): self.updates.append(tf.train.AdamOptimizer(learning_rate).minimize(self.losses[b])) self.saver = tf.train.Saver(tf.global_variables())

Stacktrace:

  Traceback (most recent call last): File "D:/Stuff/IdeaProjects/myproject/src/main.py", line 38, in <module> model = predict.make_model(input_vocab_size, output_vocab_size, buckets, cell_size, model_layers, batch_size, learning_rate) File "D:\Stuff\IdeaProjects\myproject\src\predictor.py", line 88, in make_model size=cell_size, num_layers=model_layers, batch_size=batch_size, learning_rate=learning_rate) File "D:\Stuff\IdeaProjects\myproject\src\predictor.py", line 45, in __init__ lambda x, y: seq2seq_f(x, y, False)) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\contrib\legacy_seq2seq\python\ops\seq2seq.py", line 1206, in model_with_buckets decoder_inputs[:bucket[1]]) File "D:\Stuff\IdeaProjects\myproject\src\predictor.py", line 45, in <lambda> lambda x, y: seq2seq_f(x, y, False)) File "D:\Stuff\IdeaProjects\myproject\src\predictor.py", line 28, in seq2seq_f feed_previous=do_decode) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\contrib\legacy_seq2seq\python\ops\seq2seq.py", line 848, in embedding_attention_seq2seq encoder_cell = copy.deepcopy(cell) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 161, in deepcopy y = copier(memo) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\layers\base.py", line 476, in __deepcopy__ setattr(result, k, copy.deepcopy(v, memo)) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 150, in deepcopy y = copier(x, memo) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 215, in _deepcopy_list append(deepcopy(a, memo)) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 180, in deepcopy y = _reconstruct(x, memo, *rv) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 280, in _reconstruct state = deepcopy(state, memo) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 150, in deepcopy y = copier(x, memo) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 240, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 180, in deepcopy y = _reconstruct(x, memo, *rv) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 280, in _reconstruct state = deepcopy(state, memo) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 150, in deepcopy y = copier(x, memo) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 240, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 180, in deepcopy y = _reconstruct(x, memo, *rv) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 280, in _reconstruct state = deepcopy(state, memo) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 150, in deepcopy y = copier(x, memo) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 240, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 180, in deepcopy y = _reconstruct(x, memo, *rv) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 280, in _reconstruct state = deepcopy(state, memo) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 150, in deepcopy y = copier(x, memo) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 240, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 169, in deepcopy rv = reductor(4) TypeError: can't pickle _thread.lock objects
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nlp tensorflow lstm sequence-to-sequence
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3 answers

The problem is related to the latest changes in seq2seq.py . Add this to your script and this will avoid a deep cell match:

 setattr(tf.contrib.rnn.GRUCell, '__deepcopy__', lambda self, _: self) setattr(tf.contrib.rnn.BasicLSTMCell, '__deepcopy__', lambda self, _: self) setattr(tf.contrib.rnn.MultiRNNCell, '__deepcopy__', lambda self, _: self)
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This solution does not work for me. Any new solution?

These two solutions work for me:

change seq2seq.py to / yourpath / tenorflow / contrib / legacy_seq2seq / python / ops /

 #encoder_cell = copy.deepcopy(cell) encoder_cell = core_rnn_cell.EmbeddingWrapper( cell, #encoder_cell,

or

 for nextBatch in tqdm(batches, desc="Training"): _, step_loss = model.step(...)

fed one bucket per step

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I was getting this error with PySpark when trying to access the global Logger from Python, which worked in Spark.

 TypeError: can't pickle _thread.RLock objects
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