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import timeit
import numpy
import pytest
from thinc.api import LSTM, NumpyOps, Ops, PyTorchLSTM, fix_random_seed, with_padded
from thinc.compat import has_torch
@pytest.fixture(params=[1, 6])
def nI(request):
return request.param
@pytest.fixture(params=[1, 2, 7, 9])
def nO(request):
return request.param
def test_list2padded():
ops = NumpyOps()
seqs = [numpy.zeros((5, 4)), numpy.zeros((8, 4)), numpy.zeros((2, 4))]
padded = ops.list2padded(seqs)
arr = padded.data
size_at_t = padded.size_at_t
assert arr.shape == (8, 3, 4)
assert size_at_t[0] == 3
assert size_at_t[1] == 3
assert size_at_t[2] == 2
assert size_at_t[3] == 2
assert size_at_t[4] == 2
assert size_at_t[5] == 1
assert size_at_t[6] == 1
assert size_at_t[7] == 1
unpadded = ops.padded2list(padded)
assert unpadded[0].shape == (5, 4)
assert unpadded[1].shape == (8, 4)
assert unpadded[2].shape == (2, 4)
@pytest.mark.parametrize("ops", [Ops(), NumpyOps()])
@pytest.mark.parametrize("nO,nI", [(1, 2), (2, 2), (100, 200), (9, 6)])
def test_LSTM_init_with_sizes(ops, nO, nI):
model = with_padded(LSTM(nO, nI, depth=1)).initialize()
for node in model.walk():
model.ops = ops
# Check no unallocated params.
assert node.has_param("LSTM") is not None
assert node.has_param("HC0") is not None
for node in model.walk():
# Check param sizes.
if node.has_param("LSTM"):
params = node.get_param("LSTM")
assert params.shape == (
((nO * 4 * nI)) + (nO * 4) + (nO * 4 * nO + nO * 4),
)
if node.has_param("HC0"):
params = node.get_param("HC0")
assert params.shape == (2, 1, 1, nO)
@pytest.mark.parametrize("ops", [Ops(), NumpyOps()])
def test_LSTM_fwd_bwd_shapes_simple(ops, nO, nI):
nO = 1
nI = 2
X = numpy.asarray([[0.1, 0.1], [-0.1, -0.1], [1.0, 1.0]], dtype="f")
model = with_padded(LSTM(nO, nI)).initialize(X=[X])
for node in model.walk():
node.ops = ops
ys, backprop_ys = model([X], is_train=True)
dXs = backprop_ys(ys)
assert numpy.vstack(dXs).shape == numpy.vstack([X]).shape
@pytest.mark.parametrize("ops", [Ops(), NumpyOps()])
@pytest.mark.parametrize(
"nO,nI,depth,bi,lengths",
[
(1, 1, 1, False, [1]),
(12, 32, 1, False, [3, 1]),
(2, 2, 1, True, [2, 5, 7]),
(2, 2, 2, False, [7, 2, 4]),
(2, 2, 2, True, [1]),
(32, 16, 1, True, [5, 1, 10, 2]),
(32, 16, 2, True, [3, 3, 5]),
(32, 16, 3, True, [9, 2, 4]),
],
)
def test_BiLSTM_fwd_bwd_shapes(ops, nO, nI, depth, bi, lengths):
Xs = [numpy.ones((length, nI), dtype="f") for length in lengths]
model = with_padded(LSTM(nO, nI, depth=depth, bi=bi)).initialize(X=Xs)
for node in model.walk():
node.ops = ops
ys, backprop_ys = model(Xs, is_train=True)
dXs = backprop_ys(ys)
assert numpy.vstack(dXs).shape == numpy.vstack(Xs).shape
def test_LSTM_learns():
fix_random_seed(0)
nO = 2
nI = 2
def sgd(key, weights, gradient):
weights -= 0.001 * gradient
return weights, gradient * 0
model = with_padded(LSTM(nO, nI))
X = [[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]
Y = [[0.2, 0.2], [0.3, 0.3], [0.4, 0.4]]
X = [model.ops.asarray(x, dtype="f").reshape((1, -1)) for x in X]
Y = [model.ops.asarray(y, dtype="f").reshape((1, -1)) for y in Y]
model = model.initialize(X, Y)
Yhs, bp_Yhs = model.begin_update(X)
loss1 = sum([((yh - y) ** 2).sum() for yh, y in zip(Yhs, Y)])
Yhs, bp_Yhs = model.begin_update(X)
dYhs = [yh - y for yh, y in zip(Yhs, Y)]
dXs = bp_Yhs(dYhs)
model.finish_update(sgd)
Yhs, bp_Yhs = model.begin_update(X)
dYhs = [yh - y for yh, y in zip(Yhs, Y)]
dXs = bp_Yhs(dYhs) # noqa: F841
loss2 = sum([((yh - y) ** 2).sum() for yh, y in zip(Yhs, Y)])
assert loss1 > loss2, (loss1, loss2)
@pytest.mark.skip
def test_benchmark_LSTM_fwd():
nO = 128
nI = 128
n_batch = 1000
batch_size = 30
seq_len = 30
lengths = numpy.random.normal(scale=10, loc=30, size=n_batch * batch_size)
lengths = numpy.maximum(lengths, 1)
batches = []
uniform_lengths = False
model = with_padded(LSTM(nO, nI)).initialize()
for batch_lengths in model.ops.minibatch(batch_size, lengths):
batch_lengths = list(batch_lengths)
if uniform_lengths:
seq_len = max(batch_lengths)
batch = [
numpy.asarray(
numpy.random.uniform(0.0, 1.0, (int(seq_len), nI)), dtype="f"
)
for _ in batch_lengths
]
else:
batch = [
numpy.asarray(
numpy.random.uniform(0.0, 1.0, (int(seq_len), nI)), dtype="f"
)
for seq_len in batch_lengths
]
batches.append(batch)
start = timeit.default_timer()
for Xs in batches:
ys, bp_ys = model.begin_update(list(Xs))
# _ = bp_ys(ys)
end = timeit.default_timer()
n_samples = n_batch * batch_size
print(
"--- %i samples in %s seconds (%f samples/s, %.7f s/sample) ---"
% (n_samples, end - start, n_samples / (end - start), (end - start) / n_samples)
)
def test_lstm_init():
model = with_padded(LSTM(2, 2, bi=True)).initialize()
model.initialize()
@pytest.mark.skipif(not has_torch, reason="needs PyTorch")
def test_pytorch_lstm_init():
model = with_padded(PyTorchLSTM(2, 2, depth=0)).initialize()
assert model.name == "with_padded(noop)"
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