def test_input_shape_and_dtype(
rnn_model: RNNModel,
batch_prev_tkids: torch.Tensor,
batch_next_tkids: torch.Tensor,
):
r"""Input tensors must be long tensors and have the same shape.
Same shape is required since we are using teacher forcing.
"""
try:
rnn_model = rnn_model.eval()
rnn_model.loss_fn(
batch_prev_tkids=batch_prev_tkids,
batch_next_tkids=batch_next_tkids,
)
except Exception:
assert False
def test_back_propagation_path(
rnn_model: RNNModel,
batch_prev_tkids: torch.Tensor,
batch_next_tkids: torch.Tensor,
):
r"""Gradients with respect to loss must get back propagated."""
# Make sure model has no gradients.
rnn_model = rnn_model.train()
rnn_model.zero_grad()
rnn_model.loss_fn(
batch_prev_tkids=batch_prev_tkids,
batch_next_tkids=batch_next_tkids,
).backward()
assert hasattr(rnn_model.emb.weight.grad, 'grad')
assert hasattr(rnn_model.pre_hid[0].weight.grad, 'grad')
assert hasattr(rnn_model.hid.weight_ih_l0.grad, 'grad')
assert hasattr(rnn_model.post_hid[-1].weight.grad, 'grad')
def test_return_shape_and_dtype(
rnn_model: RNNModel,
batch_prev_tkids: torch.Tensor,
batch_next_tkids: torch.Tensor,
):
r"""Return float tensor with 0 dimension."""
rnn_model = rnn_model.eval()
loss = rnn_model.loss_fn(
batch_prev_tkids=batch_prev_tkids,
batch_next_tkids=batch_next_tkids,
)
# 0 dimension tensor.
assert loss.shape == torch.Size([])
# Return float tensor.
assert loss.dtype == torch.float