def test_Transformer():
args = argparse.Namespace()
B, T, C, V = 4, 3, 6, 5
args.__dict__.update(d_model=C,
d_hidden=C,
n_heads=3,
drop_ratio=0,
n_layers=2,
length_ratio=1.5)
field = MaskedBatchField()
field.vocab = list(range(V))
xs = [
Variable(torch.LongTensor(1, random.randint(1, T)).random_(V))
for i in range(B)
]
ys = [
Variable(torch.LongTensor(1, random.randint(2, T)).random_(V))
for i in range(B)
]
xb = MaskedBatch.fromlist(xs, (True, ))
yb = MaskedBatch.fromlist(ys, (True, ))
model = Transformer(field, field, args)
mb_assert(model, (xs, ys), (xb, yb), B)
def loss(x, y):
b = namedtuple('_batch', ('src', 'trg'))(x, y)
return model.loss(b, reduce=False)
mb_assert(loss, (xs, ys), (xb, yb), B)
def test_embedding():
xs = [
Variable(torch.LongTensor(1, random.randint(1, 3)).random_(5))
for i in range(4)
]
W = Variable(torch.rand(5, 2))
xb = MaskedBatch.fromlist(xs, (True, ))
mb_assert(F.embedding, (xs, W), (xb, W), 4)
def mb_rand(*dims):
dims = [dim for dim in dims if dim != ()]
xs = [
Variable(
torch.rand(
1,
*(random.randint(1, size) if b else size
for b, size in dims[1:]))) for i in range(dims[0])
]
xb = MaskedBatch.fromlist(xs, tuple(b for b, d in dims[1:]))
return xs, xb
def test_Encoder():
args = argparse.Namespace()
args.__dict__.update(d_model=6,
d_hidden=6,
n_heads=3,
drop_ratio=0,
n_layers=2)
field = MaskedBatchField()
field.out = nn.Linear(args.d_model, 5)
xs = [
Variable(torch.LongTensor(1, random.randint(1, 3)).random_(5))
for i in range(4)
]
xb = MaskedBatch.fromlist(xs, (True, ))
mb_assert(Encoder(field, args), (xs, ), (xb, ), 4)
