def test_derivatives():
left_interp_indices = Variable(torch.LongTensor([[2, 3], [3, 4], [4, 5]])).repeat(5, 3, 1)
left_interp_values = Variable(torch.Tensor([[1, 2], [0.5, 1], [1, 3]])).repeat(5, 3, 1)
right_interp_indices = Variable(torch.LongTensor([[2, 3], [3, 4], [4, 5]])).repeat(5, 3, 1)
right_interp_values = Variable(torch.Tensor([[1, 2], [0.5, 1], [1, 3]])).repeat(5, 3, 1)
base_lazy_variable_mat = torch.randn(5, 6, 6)
base_lazy_variable_mat = base_lazy_variable_mat.transpose(1, 2).matmul(base_lazy_variable_mat)
test_matrix = Variable(torch.randn(1, 9, 4))
base_lazy_variable = NonLazyVariable(Variable(base_lazy_variable_mat, requires_grad=True))
interp_lazy_var = InterpolatedLazyVariable(base_lazy_variable, left_interp_indices, left_interp_values,
right_interp_indices, right_interp_values)
res = interp_lazy_var.matmul(test_matrix)
res.sum().backward()
base_lazy_variable2 = Variable(base_lazy_variable_mat, requires_grad=True)
left_matrix = torch.Tensor([
[0, 0, 1, 2, 0, 0],
[0, 0, 0, 0.5, 1, 0],
[0, 0, 0, 0, 1, 3],
[0, 0, 1, 2, 0, 0],
[0, 0, 0, 0.5, 1, 0],
[0, 0, 0, 0, 1, 3],
[0, 0, 1, 2, 0, 0],
[0, 0, 0, 0.5, 1, 0],
[0, 0, 0, 0, 1, 3],
]).repeat(5, 1, 1)
actual = Variable(left_matrix).matmul(base_lazy_variable2).matmul(Variable(left_matrix).transpose(-1, -2))
actual = actual.matmul(test_matrix)
actual.sum().backward()
assert approx_equal(base_lazy_variable.var.grad.data, base_lazy_variable2.grad.data)
def test_batch_matmul():
left_interp_indices = Variable(
torch.LongTensor([[2, 3], [3, 4], [4, 5]]).repeat(5, 3, 1))
left_interp_values = Variable(torch.Tensor([[1, 2], [0.5, 1],
[1, 3]]).repeat(5, 3, 1),
requires_grad=True)
left_interp_values_copy = Variable(left_interp_values.data,
requires_grad=True)
right_interp_indices = Variable(
torch.LongTensor([[0, 1], [1, 2], [2, 3]]).repeat(5, 3, 1))
right_interp_values = Variable(torch.Tensor([[1, 2], [2, 0.5],
[1, 3]]).repeat(5, 3, 1),
requires_grad=True)
right_interp_values_copy = Variable(right_interp_values.data,
requires_grad=True)
base_lazy_variable_mat = torch.randn(5, 6, 6)
base_lazy_variable_mat = base_lazy_variable_mat.transpose(
-1, -2).matmul(base_lazy_variable_mat)
base_variable = Variable(base_lazy_variable_mat, requires_grad=True)
base_variable_copy = Variable(base_lazy_variable_mat, requires_grad=True)
base_lazy_variable = NonLazyVariable(base_variable)
test_matrix = torch.randn(5, 9, 4)
interp_lazy_var = InterpolatedLazyVariable(base_lazy_variable,
left_interp_indices,
left_interp_values,
right_interp_indices,
right_interp_values)
res = interp_lazy_var.matmul(Variable(test_matrix))
left_matrix_comps = []
right_matrix_comps = []
for i in range(5):
left_matrix_comp = Variable(torch.zeros(9, 6))
right_matrix_comp = Variable(torch.zeros(9, 6))
left_matrix_comp.scatter_(1, left_interp_indices[i],
left_interp_values_copy[i])
right_matrix_comp.scatter_(1, right_interp_indices[i],
right_interp_values_copy[i])
left_matrix_comps.append(left_matrix_comp.unsqueeze(0))
right_matrix_comps.append(right_matrix_comp.unsqueeze(0))
left_matrix = torch.cat(left_matrix_comps)
right_matrix = torch.cat(right_matrix_comps)
actual = left_matrix.matmul(base_variable_copy).matmul(
right_matrix.transpose(-1, -2))
actual = actual.matmul(Variable(test_matrix))
assert approx_equal(res.data, actual.data)
res.sum().backward()
actual.sum().backward()
assert approx_equal(base_variable.grad.data, base_variable_copy.grad.data)
assert approx_equal(left_interp_values.grad.data,
left_interp_values_copy.grad.data)
def test_matmul(self):
left_interp_indices = Variable(
torch.LongTensor([[2, 3], [3, 4], [4, 5]]).repeat(3, 1))
left_interp_values = Variable(torch.Tensor([[1, 2], [0.5, 1],
[1, 3]]).repeat(3, 1),
requires_grad=True)
left_interp_values_copy = Variable(left_interp_values.data,
requires_grad=True)
right_interp_indices = Variable(
torch.LongTensor([[0, 1], [1, 2], [2, 3]]).repeat(3, 1))
right_interp_values = Variable(torch.Tensor([[1, 2], [2, 0.5],
[1, 3]]).repeat(3, 1),
requires_grad=True)
right_interp_values_copy = Variable(right_interp_values.data,
requires_grad=True)
base_lazy_variable_mat = torch.randn(6, 6)
base_lazy_variable_mat = (
base_lazy_variable_mat.t().matmul(base_lazy_variable_mat))
base_variable = Variable(base_lazy_variable_mat, requires_grad=True)
base_variable_copy = Variable(base_lazy_variable_mat,
requires_grad=True)
base_lazy_variable = NonLazyVariable(base_variable)
test_matrix = torch.randn(9, 4)
interp_lazy_var = InterpolatedLazyVariable(
base_lazy_variable,
left_interp_indices,
left_interp_values,
right_interp_indices,
right_interp_values,
)
res = interp_lazy_var.matmul(Variable(test_matrix))
left_matrix = Variable(torch.zeros(9, 6))
right_matrix = Variable(torch.zeros(9, 6))
left_matrix.scatter_(1, left_interp_indices, left_interp_values_copy)
right_matrix.scatter_(1, right_interp_indices,
right_interp_values_copy)
actual = (left_matrix.matmul(base_variable_copy).matmul(
right_matrix.t()).matmul(Variable(test_matrix)))
self.assertTrue(approx_equal(res.data, actual.data))
res.sum().backward()
actual.sum().backward()
self.assertTrue(
approx_equal(base_variable.grad.data,
base_variable_copy.grad.data))
self.assertTrue(
approx_equal(left_interp_values.grad.data,
left_interp_values_copy.grad.data))
def test_matmul_batch(self):
left_interp_indices = Variable(
torch.LongTensor([[2, 3], [3, 4], [4, 5]])).repeat(5, 3, 1)
left_interp_values = Variable(torch.Tensor([[1, 2], [0.5, 1],
[1, 3]])).repeat(5, 3, 1)
right_interp_indices = Variable(
torch.LongTensor([[0, 1], [1, 2], [2, 3]])).repeat(5, 3, 1)
right_interp_values = Variable(torch.Tensor([[1, 2], [2, 0.5],
[1, 3]])).repeat(5, 3, 1)
base_lazy_variable_mat = torch.randn(5, 6, 6)
base_lazy_variable_mat = (base_lazy_variable_mat.transpose(
1, 2).matmul(base_lazy_variable_mat))
test_matrix = Variable(torch.randn(1, 9, 4))
base_lazy_variable = NonLazyVariable(
Variable(base_lazy_variable_mat, requires_grad=True))
interp_lazy_var = InterpolatedLazyVariable(
base_lazy_variable,
left_interp_indices,
left_interp_values,
right_interp_indices,
right_interp_values,
)
res = interp_lazy_var.matmul(test_matrix)
left_matrix = torch.Tensor([
[0, 0, 1, 2, 0, 0],
[0, 0, 0, 0.5, 1, 0],
[0, 0, 0, 0, 1, 3],
[0, 0, 1, 2, 0, 0],
[0, 0, 0, 0.5, 1, 0],
[0, 0, 0, 0, 1, 3],
[0, 0, 1, 2, 0, 0],
[0, 0, 0, 0.5, 1, 0],
[0, 0, 0, 0, 1, 3],
]).repeat(5, 1, 1)
right_matrix = torch.Tensor([
[1, 2, 0, 0, 0, 0],
[0, 2, 0.5, 0, 0, 0],
[0, 0, 1, 3, 0, 0],
[1, 2, 0, 0, 0, 0],
[0, 2, 0.5, 0, 0, 0],
[0, 0, 1, 3, 0, 0],
[1, 2, 0, 0, 0, 0],
[0, 2, 0.5, 0, 0, 0],
[0, 0, 1, 3, 0, 0],
]).repeat(5, 1, 1)
actual = (left_matrix.matmul(base_lazy_variable_mat).matmul(
right_matrix.transpose(-1, -2)).matmul(test_matrix.data))
self.assertTrue(approx_equal(res.data, actual))
def test_matmul():
left_interp_indices = Variable(torch.LongTensor([[2, 3], [3, 4], [4, 5]])).repeat(3, 1)
left_interp_values = Variable(torch.Tensor([[1, 2], [0.5, 1], [1, 3]])).repeat(3, 1)
right_interp_indices = Variable(torch.LongTensor([[0, 1], [1, 2], [2, 3]])).repeat(3, 1)
right_interp_values = Variable(torch.Tensor([[1, 2], [2, 0.5], [1, 3]])).repeat(3, 1)
base_lazy_variable_mat = torch.randn(6, 6)
base_lazy_variable_mat = base_lazy_variable_mat.t().matmul(base_lazy_variable_mat)
base_lazy_variable = NonLazyVariable(Variable(base_lazy_variable_mat))
test_matrix = torch.randn(9, 4)
test_matrix = torch.ones(9)
interp_lazy_var = InterpolatedLazyVariable(base_lazy_variable, left_interp_indices, left_interp_values,
right_interp_indices, right_interp_values)
res = interp_lazy_var.matmul(Variable(test_matrix)).data
left_matrix = torch.Tensor([
[0, 0, 1, 2, 0, 0],
[0, 0, 0, 0.5, 1, 0],
[0, 0, 0, 0, 1, 3],
[0, 0, 1, 2, 0, 0],
[0, 0, 0, 0.5, 1, 0],
[0, 0, 0, 0, 1, 3],
[0, 0, 1, 2, 0, 0],
[0, 0, 0, 0.5, 1, 0],
[0, 0, 0, 0, 1, 3],
])
right_matrix = torch.Tensor([
[1, 2, 0, 0, 0, 0],
[0, 2, 0.5, 0, 0, 0],
[0, 0, 1, 3, 0, 0],
[1, 2, 0, 0, 0, 0],
[0, 2, 0.5, 0, 0, 0],
[0, 0, 1, 3, 0, 0],
[1, 2, 0, 0, 0, 0],
[0, 2, 0.5, 0, 0, 0],
[0, 0, 1, 3, 0, 0],
])
actual = left_matrix.matmul(base_lazy_variable_mat).matmul(right_matrix.t()).matmul(test_matrix)
assert approx_equal(res, actual)