def test_input_shape_and_dtype(
rnn_model: RNNModel,
batch_prev_tkids: torch.Tensor,
):
r"""Input must be long tensor."""
try:
rnn_model = rnn_model.eval()
rnn_model.pred(batch_prev_tkids)
except Exception:
assert False
def test_value_range(
rnn_model: RNNModel,
batch_prev_tkids: torch.Tensor,
):
r"""Return values are probabilities."""
rnn_model = rnn_model.eval()
out = rnn_model.pred(batch_prev_tkids)
# Probabilities are values within range [0, 1].
assert torch.all(0 <= out).item()
assert torch.all(out <= 1).item()
# Sum of the probabilities equals to 1.
accum_out = out.sum(dim=-1)
assert torch.allclose(accum_out, torch.ones_like(accum_out))
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
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_return_shape_and_dtype(
rnn_model: RNNModel,
batch_prev_tkids: torch.Tensor,
):
r"""Return float tensor with correct shape."""
rnn_model = rnn_model.eval()
out = rnn_model.pred(batch_prev_tkids)
# Output float tensor.
assert out.dtype == torch.float
# Input shape: (B, S).
# Output shape: (B, S, V).
assert out.shape == (
batch_prev_tkids.shape[0],
batch_prev_tkids.shape[1],
rnn_model.emb.num_embeddings,
)
