def forward(self, # pylint: disable=arguments-differ
matrix_1: torch.Tensor,
matrix_2: torch.Tensor) -> torch.Tensor:
combined_tensors = util.combine_tensors_and_multiply(self._combination,
[matrix_1.unsqueeze(2), matrix_2.unsqueeze(1)],
self._weight_vector)
return self._activation(combined_tensors + self._bias)
def test_combine_tensors_and_multiply_with_same_batch_size_and_embedding_dim(self):
# This test just makes sure we handle some potential edge cases where the lengths of all
# dimensions are the same, making sure that the multiplication with the weight vector
# happens along the right dimension (it should be the last one).
tensors = [torch.Tensor([[[5, 5], [4, 4]], [[2, 3], [1, 1]]])] # (2, 2, 2)
weight = torch.Tensor([4, 5]) # (2,)
combination = "x"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[20 + 25, 16 + 20], [8 + 15, 4 + 5]])
tensors = [torch.Tensor([[[5, 5], [2, 2]], [[4, 4], [3, 3]]]),
torch.Tensor([[[2, 3]], [[1, 1]]])]
weight = torch.Tensor([4, 5])
combination = "x*y"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[5 * 2 * 4 + 5 * 3 * 5, 2 * 2 * 4 + 2 * 3 * 5],
[4 * 1 * 4 + 4 * 1 * 5, 3 * 1 * 4 + 3 * 1 * 5]])
def test_combine_tensors_and_multiply_with_same_batch_size_and_embedding_dim(self):
# This test just makes sure we handle some potential edge cases where the lengths of all
# dimensions are the same, making sure that the multiplication with the weight vector
# happens along the right dimension (it should be the last one).
tensors = [torch.Tensor([[[5, 5], [4, 4]], [[2, 3], [1, 1]]])] # (2, 2, 2)
weight = torch.Tensor([4, 5]) # (2,)
combination = "x"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[20 + 25, 16 + 20], [8 + 15, 4 + 5]])
tensors = [torch.Tensor([[[5, 5], [2, 2]], [[4, 4], [3, 3]]]),
torch.Tensor([[[2, 3]], [[1, 1]]])]
weight = torch.Tensor([4, 5])
combination = "x*y"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[5 * 2 * 4 + 5 * 3 * 5, 2 * 2 * 4 + 2 * 3 * 5],
[4 * 1 * 4 + 4 * 1 * 5, 3 * 1 * 4 + 3 * 1 * 5]])
def test_combine_tensors_and_multiply_with_batch_size_one(self):
seq_len_1 = 10
seq_len_2 = 5
embedding_dim = 8
combination = "x,y,x*y"
t1 = torch.randn(1, seq_len_1, embedding_dim)
t2 = torch.randn(1, seq_len_2, embedding_dim)
combined_dim = util.get_combined_dim(combination,
[embedding_dim, embedding_dim])
weight = torch.Tensor(combined_dim)
result = util.combine_tensors_and_multiply(
combination, [t1.unsqueeze(2), t2.unsqueeze(1)], weight)
assert_almost_equal(result.size(), [1, seq_len_1, seq_len_2])
def test_combine_tensors_and_multiply_with_batch_size_one_and_seq_len_one(self):
seq_len_1 = 10
seq_len_2 = 1
embedding_dim = 8
combination = "x,y,x*y"
t1 = torch.randn(1, seq_len_1, embedding_dim)
t2 = torch.randn(1, seq_len_2, embedding_dim)
combined_dim = util.get_combined_dim(combination, [embedding_dim, embedding_dim])
weight = torch.Tensor(combined_dim)
result = util.combine_tensors_and_multiply(combination, [t1.unsqueeze(2), t2.unsqueeze(1)], weight)
assert_almost_equal(
result.size(),
[1, seq_len_1, seq_len_2]
)
def test_combine_tensors_and_multiply(self):
tensors = [torch.Tensor([[[2, 3]]]), torch.Tensor([[[5, 5]]])]
weight = torch.Tensor([4, 5])
combination = "x"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[8 + 15]])
combination = "y"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[20 + 25]])
combination = "x,y"
weight2 = torch.Tensor([4, 5, 4, 5])
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight2),
[[8 + 20 + 15 + 25]])
combination = "x-y"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[-3 * 4 + -2 * 5]])
combination = "y-x"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[3 * 4 + 2 * 5]])
combination = "y+x"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[7 * 4 + 8 * 5]])
combination = "y*x"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[10 * 4 + 15 * 5]])
combination = "y/x"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[(5 / 2) * 4 + (5 / 3) * 5]], decimal=4)
combination = "x/y"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[(2 / 5) * 4 + (3 / 5) * 5]], decimal=4)
with pytest.raises(ConfigurationError):
util.combine_tensors_and_multiply("x+y+y", tensors, weight)
with pytest.raises(ConfigurationError):
util.combine_tensors_and_multiply("x%y", tensors, weight)
def forward(self, matrix_1: torch.Tensor,
matrix_2: torch.Tensor) -> torch.Tensor:
combined_tensors = util.combine_tensors_and_multiply(
self._combination, [matrix_1.unsqueeze(2),
matrix_2.unsqueeze(1)], self._weight_vector)
return self._activation(combined_tensors + self._bias)
def _forward_internal(self, vector: torch.Tensor,
matrix: torch.Tensor) -> torch.Tensor:
combined_tensors = util.combine_tensors_and_multiply(
self._combination, [vector.unsqueeze(1), matrix],
self._weight_vector)
return self._activation(combined_tensors.squeeze(1) + self._bias)
def _forward_internal(self, vector: torch.Tensor, matrix: torch.Tensor) -> torch.Tensor:
combined_tensors = util.combine_tensors_and_multiply(self._combination,
[vector.unsqueeze(1), matrix],
self._weight_vector)
return self._activation(combined_tensors.squeeze(1) + self._bias)
def test_combine_tensors_and_multiply(self):
tensors = [torch.Tensor([[[2, 3]]]), torch.Tensor([[[5, 5]]])]
weight = torch.Tensor([4, 5])
combination = "x"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[8 + 15]])
combination = "y"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[20 + 25]])
combination = "x,y"
weight2 = torch.Tensor([4, 5, 4, 5])
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight2),
[[8 + 20 + 15 + 25]])
combination = "x-y"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[-3 * 4 + -2 * 5]])
combination = "y-x"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[3 * 4 + 2 * 5]])
combination = "y+x"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[7 * 4 + 8 * 5]])
combination = "y*x"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[10 * 4 + 15 * 5]])
combination = "y/x"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[(5 / 2) * 4 + (5 / 3) * 5]], decimal=4)
combination = "x/y"
assert_almost_equal(util.combine_tensors_and_multiply(combination, tensors, weight),
[[(2 / 5) * 4 + (3 / 5) * 5]], decimal=4)
with pytest.raises(ConfigurationError):
util.combine_tensors_and_multiply("x+y+y", tensors, weight)
with pytest.raises(ConfigurationError):
util.combine_tensors_and_multiply("x%y", tensors, weight)
def forward(self, matrix_1: torch.Tensor, matrix_2: torch.Tensor) -> torch.Tensor:
combined_tensors = util.combine_tensors_and_multiply()
reveal_type(matrix_1)
