torch type mismatch error

With python3.6, pytorch0.4.1, cuda9.0, I got the following error when I run train.py with timit example:

$ python train.py examples/timit/seq2seq_config.json
Traceback (most recent call last):
  File "train.py", line 146, in <module>
    run(config)
  File "train.py", line 104, in run
    run_state = run_epoch(model, optimizer, train_ldr, *run_state)
  File "train.py", line 29, in run_epoch
    loss = model.loss(batch)
  File "/path/to/speech/models/seq2seq.py", line 57, in loss
    out, alis = self.forward_impl(x, y)
  File "/path/to/speech/models/seq2seq.py", line 68, in forward_impl
    out, alis = self.decode(x, y)
  File "/path/to/speech/models/seq2seq.py", line 103, in decode
    hx = self.dec_rnn(ix.squeeze(dim=1), hx)
  File "/path/to/lib64/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/path/to/lib64/python3.6/site-packages/torch/nn/modules/rnn.py", line 794, in forward
    self.bias_ih, self.bias_hh,
  File "/path/to/lib64/python3.6/site-packages/torch/nn/_functions/rnn.py", line 53, in GRUCell
    gh = F.linear(hidden, w_hh)
  File "/path/to/lib64/python3.6/site-packages/torch/nn/functional.py", line 1026, in linear
    output = input.matmul(weight.t())
RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #2 'mat2'

If I add torch.set_default_tensor_type('torch.cuda.FloatTensor') in main function, error becomes:

Traceback (most recent call last):
  File "train.py", line 148, in <module>
    run(config)
  File "train.py", line 110, in run
    dev_loss, dev_cer = eval_dev(model, dev_ldr, preproc)
  File "train.py", line 57, in eval_dev
    preds = model.infer(batch)
  File "/path/to/speech/models/seq2seq.py", line 176, in infer
    _, argmaxs = self.infer_decode(x, y, end_tok, max_len)
  File "/path/to/speech/models/seq2seq.py", line 155, in infer_decode
    if torch.sum(y.data == end_tok) == y.numel():
RuntimeError: Expected object of type torch.cuda.LongTensor but found type torch.LongTensor for argument #2 'other'

Do you have idea to solve this?

[pkyoung]

I managed to execute train.py, but not yet confirmed training was successful or not. The quick (and dirty) remedy to above error was:

diff --git a/speech/models/seq2seq.py b/speech/models/seq2seq.py
index b2881e3..65e3a38 100644
--- a/speech/models/seq2seq.py
+++ b/speech/models/seq2seq.py
@@ -87,7 +87,7 @@ class Seq2Seq(model.Model):

         hx = torch.zeros((x.shape[0], x.shape[2]), requires_grad=False)
         if self.is_cuda:
-            hx.cuda()
+            hx = hx.cuda()
         ax = None; sx = None;
         for t in range(y.size()[1] - 1):
             sample = (out and self.scheduled_sampling)
@@ -119,7 +119,7 @@ class Seq2Seq(model.Model):
         if state is None:
             hx = torch.zeros((x.shape[0], x.shape[2]), requires_grad=False)
             if self.is_cuda:
-                hx.cuda()
+                hx = hx.cuda()
             ax = None; sx = None;
         else:
             hx, ax, sx = state
@@ -164,7 +164,7 @@ class Seq2Seq(model.Model):
         Infer a likely output. No beam search yet.
         """
         x, y = self.collate(*batch)
-        end_tok = y.data[0, -1] # TODO
+        end_tok = y.data[0, -1].cuda() # TODO
         t = y
         if self.is_cuda:
             x = x.cuda()
@@ -172,7 +172,7 @@ class Seq2Seq(model.Model):
         x = self.encode(x)

         # needs to be the start token, TODO
-        y = t[:, 0:1]
+        y = t[:, 0:1].cuda()
         _, argmaxs = self.infer_decode(x, y, end_tok, max_len)
         argmaxs = argmaxs.cpu().data.numpy()
         return [seq.tolist() for seq in argmaxs]

And there was also error in train.py

diff --git a/train.py b/train.py
index a04eb6c..6141ba0 100644
--- a/train.py
+++ b/train.py
@@ -10,6 +10,7 @@ import torch
 import torch.nn as nn
 import torch.optim
 import tqdm
+import copy

 import speech
 import speech.loader as loader
@@ -30,7 +31,7 @@ def run_epoch(model, optimizer, train_ldr, it, avg_loss):
         loss.backward()

         grad_norm = nn.utils.clip_grad_norm(model.parameters(), 200)
-        loss = loss.data[0]
+        loss = loss.item()

         optimizer.step()
         prev_end_t = end_t
@@ -54,11 +55,13 @@ def eval_dev(model, ldr, preproc):
     model.set_eval()

     for batch in tqdm.tqdm(ldr):
-        preds = model.infer(batch)
-        loss = model.loss(batch)
-        losses.append(loss.data[0])
+        batch_ = copy.deepcopy(batch)
+        preds = model.infer(batch_)
+        batch_ = copy.deepcopy(batch)
+        loss = model.loss(batch_)
+        losses.append(loss.item())
         all_preds.extend(preds)
-        all_labels.extend(batch[1])
+        all_labels.extend(list(batch)[1])

     model.set_train()

[NAM-hj]

After these, I got these warnings while training.

WARNING: Forward backward likelihood mismatch 0.000084
WARNING: Forward backward likelihood mismatch 0.000092
WARNING: Forward backward likelihood mismatch 0.000046