Autograder [Mon Apr  6 02:26:02 2020]: Received job 11785-s20_hw4p1_10_agadhika@andrew.cmu.edu:580
Autograder [Mon Apr  6 02:26:22 2020]: Success: Autodriver returned normally
Autograder [Mon Apr  6 02:26:22 2020]: Here is the output from the autograder:
---
Autodriver: Job exited with status 0
mkdir -p handin
tar xf handin.tar -C handin
tar xf autograde.tar
#pip3 freeze
AUTOLAB=1 /usr/local/depot/anaconda3/bin/python3 autograde/runner.py --module-path=./handin/
FYour mean NLL for predicting a single word: 5.127223014831543
.
=================================== FAILURES ===================================
_______________________________ test_generation ________________________________

    def test_generation():
        inp = np.load(fixture_path('generation.npy'))
        forward = 10
        n = inp.shape[0]
        t = inp.shape[1]
        pred = np.load(handin_path('generated_logits.npy'))
        assert pred.shape[0] == n
        assert pred.shape[1] == forward
    
        ninp = np.concatenate((inp, pred), axis=1)
        vocab = np.load(fixture_path('vocab.npy'))
        model = Model(vocab.shape[0], args=prediction_args)
        model.load_state_dict(load_from_numpy(read_chunks(fixture_path('model-00000099.tar.npy.{}'))))
        model.eval()
    
        state = model.zero_state(n)
        x = Variable(torch.from_numpy(ninp.T)).long()
>       logits, rnn_hs = model(x, state)

autograde/tests/test_generation.py:229: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/depot/anaconda3/lib/python3.5/site-packages/torch/nn/modules/module.py:210: in __call__
    result = self.forward(*input, **kwargs)
autograde/tests/test_generation.py:183: in forward
    embedded = self.embedding(input)
/usr/local/depot/anaconda3/lib/python3.5/site-packages/torch/nn/modules/module.py:210: in __call__
    result = self.forward(*input, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = DropoutEmbedding (
  (embedding): Embedding(33278, 400)
)
input = Variable containing:
  8881   1419  19288  ...   29011    122  31891
 12851  11568     79  ...   22968     79  19084
 ...19  22968  ...   25821  17549   1423
  1419     79  14658  ...   31353  33005     88
[torch.LongTensor of size 40x128]


    def forward(self, input):
        if self.training and self.p > 0:
            mask = Variable(
                self.embedding.weight.data
                    .new(self.embedding.weight.size(0), 1)
                    .bernoulli_(1. - self.p)
            )  # .expand_as(self.embedding.weight)
            masked_weight = (self.embedding.weight * mask) / (1. - self.p)
        else:
            masked_weight = self.embedding.weight
        padding_idx = self.embedding.padding_idx
        if padding_idx is None:
            padding_idx = -1
    
>       return self.embedding._backend.Embedding.apply(
            input, masked_weight,
            padding_idx, self.embedding.max_norm, self.embedding.norm_type,
            self.embedding.scale_grad_by_freq, self.embedding.sparse
        )
E       AttributeError: type object 'Embedding' has no attribute 'apply'

autograde/tests/test_generation.py:153: AttributeError
Run time:  2.818155527114868
{"scores": {"Prediction": 50.0, "Generation": 0.0}}
#AUTOLAB=1 python3 autograde/runner.py --module-path=./handin/