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import pytest
import torch
from stanza.models.constituency.transformer_tree_stack import TransformerTreeStack
pytestmark = [pytest.mark.pipeline, pytest.mark.travis]
def test_initial_state():
"""
Test that the initial state has the expected shapes
"""
ts = TransformerTreeStack(3, 5, 0.0)
initial = ts.initial_state()
assert len(initial) == 1
assert initial.value.output.shape == torch.Size([5])
assert initial.value.key_stack.shape == torch.Size([1, 5])
assert initial.value.value_stack.shape == torch.Size([1, 5])
def test_output():
"""
Test that you can get an expected output shape from the TTS
"""
ts = TransformerTreeStack(3, 5, 0.0)
initial = ts.initial_state()
out = ts.output(initial)
assert out.shape == torch.Size([5])
assert torch.allclose(initial.value.output, out)
def test_push_state_single():
"""
Test that stacks are being updated correctly when using a single stack
Values of the attention are not verified, though
"""
ts = TransformerTreeStack(3, 5, 0.0)
initial = ts.initial_state()
rand_input = torch.randn(1, 3)
stacks = ts.push_states([initial], ["A"], rand_input)
stacks = ts.push_states(stacks, ["B"], rand_input)
assert len(stacks) == 1
assert len(stacks[0]) == 3
assert stacks[0].value.value == "B"
assert stacks[0].pop().value.value == "A"
assert stacks[0].pop().pop().value.value is None
def test_push_state_same_length():
"""
Test that stacks are being updated correctly when using 3 stacks of the same length
Values of the attention are not verified, though
"""
ts = TransformerTreeStack(3, 5, 0.0)
initial = ts.initial_state()
rand_input = torch.randn(3, 3)
stacks = ts.push_states([initial, initial, initial], ["A", "A", "A"], rand_input)
stacks = ts.push_states(stacks, ["B", "B", "B"], rand_input)
stacks = ts.push_states(stacks, ["C", "C", "C"], rand_input)
assert len(stacks) == 3
for s in stacks:
assert len(s) == 4
assert s.value.key_stack.shape == torch.Size([4, 5])
assert s.value.value_stack.shape == torch.Size([4, 5])
assert s.value.value == "C"
assert s.pop().value.value == "B"
assert s.pop().pop().value.value == "A"
assert s.pop().pop().pop().value.value is None
def test_push_state_different_length():
"""
Test what happens if stacks of different lengths are passed in
"""
ts = TransformerTreeStack(3, 5, 0.0)
initial = ts.initial_state()
rand_input = torch.randn(2, 3)
one_step = ts.push_states([initial], ["A"], rand_input[0:1, :])[0]
stacks = [one_step, initial]
stacks = ts.push_states(stacks, ["B", "C"], rand_input)
assert len(stacks) == 2
assert len(stacks[0]) == 3
assert len(stacks[1]) == 2
assert stacks[0].pop().value.value == 'A'
assert stacks[0].value.value == 'B'
assert stacks[1].value.value == 'C'
assert stacks[0].value.key_stack.shape == torch.Size([3, 5])
assert stacks[1].value.key_stack.shape == torch.Size([2, 5])
def test_mask():
"""
Test that a mask prevents the softmax from picking up unwanted values
"""
ts = TransformerTreeStack(3, 5, 0.0)
random_v = torch.tensor([[[0.1, 0.2, 0.3, 0.4, 0.5]]])
double_v = random_v * 2
value = torch.cat([random_v, double_v], axis=1)
random_k = torch.randn(1, 1, 5)
key = torch.cat([random_k, random_k], axis=1)
query = torch.randn(1, 5)
output = ts.attention(key, query, value)
# when the two keys are equal, we expect the attention to be 50/50
expected_output = (random_v + double_v) / 2
assert torch.allclose(output, expected_output)
# If the first entry is masked out, the second one should be the
# only one represented
mask = torch.zeros(1, 2, dtype=torch.bool)
mask[0][0] = True
output = ts.attention(key, query, value, mask)
assert torch.allclose(output, double_v)
# If the second entry is masked out, the first one should be the
# only one represented
mask = torch.zeros(1, 2, dtype=torch.bool)
mask[0][1] = True
output = ts.attention(key, query, value, mask)
assert torch.allclose(output, random_v)
def test_position():
"""
Test that nothing goes horribly wrong when position encodings are used
Does not actually test the results of the encodings
"""
ts = TransformerTreeStack(4, 5, 0.0, use_position=True)
initial = ts.initial_state()
assert len(initial) == 1
assert initial.value.output.shape == torch.Size([5])
assert initial.value.key_stack.shape == torch.Size([1, 5])
assert initial.value.value_stack.shape == torch.Size([1, 5])
rand_input = torch.randn(2, 4)
one_step = ts.push_states([initial], ["A"], rand_input[0:1, :])[0]
stacks = [one_step, initial]
stacks = ts.push_states(stacks, ["B", "C"], rand_input)
def test_length_limit():
"""
Test that the length limit drops nodes as the length limit is exceeded
"""
ts = TransformerTreeStack(4, 5, 0.0, length_limit = 2)
initial = ts.initial_state()
assert len(initial) == 1
assert initial.value.output.shape == torch.Size([5])
assert initial.value.key_stack.shape == torch.Size([1, 5])
assert initial.value.value_stack.shape == torch.Size([1, 5])
data = torch.tensor([[0.1, 0.2, 0.3, 0.4]])
stacks = ts.push_states([initial], ["A"], data)
stacks = ts.push_states(stacks, ["B"], data)
assert len(stacks) == 1
assert len(stacks[0]) == 3
assert stacks[0].value.key_stack.shape[0] == 3
assert stacks[0].value.value_stack.shape[0] == 3
stacks = ts.push_states(stacks, ["C"], data)
assert len(stacks) == 1
assert len(stacks[0]) == 4
assert stacks[0].value.key_stack.shape[0] == 3
assert stacks[0].value.value_stack.shape[0] == 3
stacks = ts.push_states(stacks, ["D"], data)
assert len(stacks) == 1
assert len(stacks[0]) == 5
assert stacks[0].value.key_stack.shape[0] == 3
assert stacks[0].value.value_stack.shape[0] == 3
def test_two_heads():
"""
Test that the length limit drops nodes as the length limit is exceeded
"""
ts = TransformerTreeStack(4, 6, 0.0, num_heads = 2)
initial = ts.initial_state()
assert len(initial) == 1
assert initial.value.output.shape == torch.Size([6])
assert initial.value.key_stack.shape == torch.Size([1, 6])
assert initial.value.value_stack.shape == torch.Size([1, 6])
rand_input = torch.randn(2, 4)
one_step = ts.push_states([initial], ["A"], rand_input[0:1, :])[0]
stacks = [one_step, initial]
stacks = ts.push_states(stacks, ["B", "C"], rand_input)
assert len(stacks) == 2
assert len(stacks[0]) == 3
assert len(stacks[1]) == 2
assert stacks[0].pop().value.value == 'A'
assert stacks[0].value.value == 'B'
assert stacks[1].value.value == 'C'
assert stacks[0].value.key_stack.shape == torch.Size([3, 6])
assert stacks[1].value.key_stack.shape == torch.Size([2, 6])
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