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This repository has been archived by the owner on Oct 31, 2023. It is now read-only.
I'm trying to run only on CPU. My PyTorch version is: torch (0.2.0.post2).
I used this line to initiate the model:
infersent = torch.load('infersent.allnli.pickle', map_location=lambda storage, loc: storage)
I got this warning:
SentEval/eval_models/models.py:54: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at
every call, possibly greately increasing memory usage. To compact weights again call flatten_parameters().
sent_output = self.enc_lstm(sent_packed)[0] # seqlen x batch x 2*nhid
And an assertion error when I call infersent.encode():
Traceback (most recent call last):
File "infersent_run.py", line 155, in main
results_transfer = se.eval(transfer_tasks)
File "SentEval/senteval.py", line 56, in eval
self.results = {x:self.eval(x) for x in name}
File "SentEval/senteval.py", line 56, in <dictcomp>
self.results = {x:self.eval(x) for x in name}
File "SentEval/senteval.py", line 91, in eval
self.results = self.evaluation.run(self.params, self.batcher)
File "SentEval/binary.py", line 44, in run
embeddings = batcher(params, batch)
File "infersent_run.py", line 89, in batcher
infersent_embed = params.infersent.encode(sentences, bsize=params.batch_size, tokenize=False)
File "SentEval/eval_models/models.py", line 202, in encode
batch = self.forward((batch, lengths[stidx:stidx + bsize]))
File "SentEval/eval_models/models.py", line 54, in forward
sent_output = self.enc_lstm(sent_packed)[0] # seqlen x batch x 2*nhid
File "/home/python2.7/site-packages/torch/nn/modules/module.py", line 224, in __call__
result = self.forward(*input, **kwargs)
File "/home/python2.7/site-packages/torch/nn/modules/rnn.py", line 162, in forward
output, hidden = func(input, self.all_weights, hx)
File "/home/python2.7/site-packages/torch/nn/_functions/rnn.py", line 351, in forward
return func(input, *fargs, **fkwargs)
File "/home/python2.7/site-packages/torch/autograd/function.py", line 284, in _do_forward
flat_output = super(NestedIOFunction, self)._do_forward(*flat_input)
File "/home/python2.7/site-packages/torch/autograd/function.py", line 306, in forward
result = self.forward_extended(*nested_tensors)
File "/home/python2.7/site-packages/torch/nn/_functions/rnn.py", line 293, in forward_extended
cudnn.rnn.forward(self, input, hx, weight, output, hy)
File "/home/python2.7/site-packages/torch/backends/cudnn/rnn.py", line 259, in forward
_copyParams(weight, params)
File "/home/python2.7/site-packages/torch/backends/cudnn/rnn.py", line 186, in _copyParams
assert param_from.type() == param_to.type()
AssertionError
Any idea on why this is happening, and why is it still calling cudnn even though I want to run on CPU?
The text was updated successfully, but these errors were encountered:
Indeed, the need for this line "infersent.use_cuda = False" was removed in a recent commit.
Now you just need to use ".cpu()" or ".cuda()" to switch between CPU/GPU.
If you're on CPU, you may want to try and play with the parameter k in: torch.set_num_threads(k)
In my case, using less CPU cores than my server had made the generation of embeddings faster (from 40 to 70 sentences/s).
Hi,
I got this warning:
Any idea on why this is happening, and why is it still calling cudnn even though I want to run on CPU?
The text was updated successfully, but these errors were encountered: