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Implements gradients calculation for trtrs #3972

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Merged
soumith merged 1 commit into from
Dec 2, 2017
Merged

Implements gradients calculation for trtrs #3972

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16 changes: 16 additions & 0 deletions test/test_autograd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1584,6 +1584,22 @@ def test_cat_negdim_2(self):
lambda a, b, c, dim: torch.cat((a, b, c), dim),
True, f_args_variable, f_args_tensor)

@skipIfNoLapack
def test_trtrs(self):
def _test_with_size(N, C):
A = Variable(torch.rand(N, N), requires_grad=True)
b = Variable(torch.rand(N, C), requires_grad=True)

for upper, transpose, unitriangular in product((True, False), repeat=3):
def func(A, b):
return torch.trtrs(b, A, upper, transpose, unitriangular)

gradcheck(func, [A, b])
gradgradcheck(func, [A, b])

_test_with_size(S, S + 1)
_test_with_size(S, S - 1)

def test_variable_traverse(self):
def get_out_and_unrefed_cycle():
inp = Variable(torch.randn(10), requires_grad=True)
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2 changes: 1 addition & 1 deletion tools/autograd/derivatives.yaml
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Expand Up @@ -588,7 +588,7 @@
self: grad.triu(diagonal)

- name: trtrs(Tensor self, Tensor A, bool upper, bool transpose, bool unitriangular)
self: not_implemented("trtrs")
self, A: trtrs_backward(grads[0], grads[1], self, A, res1, upper, transpose, unitriangular, grad_input_mask)

- name: trunc(Tensor self)
self: zeros_like(grad)
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32 changes: 32 additions & 0 deletions tools/autograd/templates/Functions.cpp
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Expand Up @@ -596,6 +596,38 @@ Tensor _det_with_svd_backward(const std::vector<torch::autograd::Variable> &grad
return svd_term + u.mm(sigma.pow(-1).mul_(det.mul(det_grad)).diag()).mm(v.transpose(0, 1));
}

// Reference:
// https://people.maths.ox.ac.uk/gilesm/files/NA-08-01.pdf
// Sec. 2.3.1 Matrix inverse product
std::tuple<Tensor, Tensor> trtrs_backward(
const Tensor & grad_x, const Tensor & grad_m,
const Tensor & b, const Tensor & a, const Tensor & x,
const bool upper, const bool transpose, const bool unitriangular,
std::array<bool, 2> output_mask) {
Tensor grad_b, grad_a;
if (grad_x.defined()) {
grad_b = std::get<0>(grad_x.trtrs(a, upper, !transpose, unitriangular));
if (output_mask[1]) {
grad_a = transpose ? -x.mm(grad_b.t()) : -grad_b.mm(x.t());
if (upper) {
grad_a = grad_a.triu((int) unitriangular);
} else {
grad_a = grad_a.tril(-((int) unitriangular));
}
}
}
if (!grad_a.defined()) {
grad_a = a.type().zeros({1}).expand_as(a);
}
if (!grad_b.defined()) {
grad_b = b.type().zeros({1}).expand_as(b);
}
if (output_mask[1] && grad_m.defined()) {
grad_a = grad_a.add(grad_m);
}
return std::tuple<Tensor, Tensor>{grad_b, grad_a};
}

}

${autograd_function_definitions}
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