Add some more asserts to cuDNN RNN

Author: apaszke

torch/backends/cudnn/rnn.py

@@ -206,6 +206,9 @@ def forward(fn, input, hx, weight, output, hy):
  fn.seq_length, fn.mini_batch, fn.input_size = input.size()
  hidden_size = _hidden_size(fn)
  output_size = _output_size(fn)
+
+ assert hx.is_contiguous()
+ assert cx is None or cx.is_contiguous()
  x = input.contiguous()
  output.resize_(*output_size)
  hy.resize_(*hidden_size)
@@ -319,6 +322,8 @@ def backward_grad(fn, input, hx, weight, output, grad_output, grad_hy, grad_inpu
  hidden_size = _hidden_size(fn)
  output_size = _output_size(fn)
 
+ assert hx.is_contiguous()
+ assert cx is None or cx.is_contiguous()
  x = input.contiguous()
  dy = grad_output.contiguous()
  y = output
@@ -397,6 +402,7 @@ def backward_weight(fn, input, hx, output, weight, grad_weight):
  hx, cx = hx
  else:
  cx = None
+
  if fn.batch_first:
  input = input.transpose(0, 1)
  output = output.transpose(0, 1)
@@ -409,12 +415,12 @@ def backward_weight(fn, input, hx, output, weight, grad_weight):
  if tuple(input.size()) != input_size:
  raise RuntimeError('Expected input size {}, got {}'.format(
  input_size, tuple(input.size())))
- if not fn.train:
- raise RuntimeError('backward_weight can only be called when training!')
  if tuple(hx.size()) != hidden_size:
  raise RuntimeError('Expected input size {}, got {}'.format(
  hidden_size, hx.size()))
 
+ assert hx.is_contiguous()
+ assert cx is None or cx.is_contiguous()
  x = input.contiguous()
  y = output
  dw = fn.weight_buf.new().resize_as_(fn.weight_buf).zero_()