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Fix torch.diag backward with non-square matrix #4538

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Merged
soumith merged 2 commits into from
Feb 5, 2018
Merged

Fix torch.diag backward with non-square matrix #4538

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a810a93
Fix torch.diag backward with non-square matrix
zou3519 Jan 8, 2018
35f90b2
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zou3519 Jan 9, 2018
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6 changes: 6 additions & 0 deletions test/test_autograd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2393,6 +2393,12 @@ class dont_convert(tuple):
('dist', (S, S, S), ((), 4), 'scalar_4_broadcast_rhs'),
('dist', (), ((S, S, S), 4), 'scalar_4_broadcast_lhs'),
('diag', (M, M), NO_ARGS, '2d'),
('diag', (3, 5), NO_ARGS, '2d_wide'),
('diag', (3, 5), (2,), '2d_wide_pos'),
('diag', (3, 5), (-2,), '2d_wide_neg'),
('diag', (5, 3), NO_ARGS, '2d_tall'),
('diag', (5, 3), (2,), '2d_tall_pos'),
('diag', (5, 3), (-2,), '2d_tall_neg'),
('diag', (M,), NO_ARGS, '1d'),
('diag', (M, M), (1,), '2d_1'),
('diag', (M, M), (2,), '2d_2'),
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2 changes: 1 addition & 1 deletion tools/autograd/derivatives.yaml
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Expand Up @@ -192,7 +192,7 @@
self: _det_with_svd_backward(grads, self, result0, result1, result2, result3)

- name: diag(Tensor self, int64_t diagonal)
self: grad.diag(diagonal)
self: diag_backward(grad, self.sizes(), diagonal)

- name: dist(Tensor self, Tensor other, Scalar p)
self: norm_backward(grad, self - other, p, result)
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32 changes: 32 additions & 0 deletions tools/autograd/templates/Functions.cpp
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Expand Up @@ -711,6 +711,38 @@ Tensor smooth_l1_loss_double_backward_grad_output(const Tensor & grad, const Ten
return (r * grad).sum().view({1});
}

static inline int64_t diag_size(int64_t height, int64_t width, int64_t diagonal) {
if (width > height) {
return diag_size(width, height, -diagonal);
}
// Assumes height >= width
auto longest_diag = width;
if (diagonal >= 0) {
return longest_diag - diagonal;
}
if (longest_diag < height + diagonal) {
return longest_diag;
}
return height + diagonal;
}

Tensor diag_backward(const Tensor & grad, IntList input_sizes, int64_t diagonal) {
auto ndimension = input_sizes.size();
TORCH_ASSERT(ndimension == 1 || ndimension == 2);

if (ndimension == 1 || input_sizes[0] == input_sizes[1]) {
return grad.diag(diagonal);
}

// Input was a matrix but was not square

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auto grad_input = grad.type().zeros(input_sizes);

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auto diagonal_size = diag_size(input_sizes[0], input_sizes[1], diagonal);
auto storage_offset = diagonal >= 0 ? diagonal : -diagonal * input_sizes[1];
auto diag = grad_input.as_strided({diagonal_size}, {input_sizes[1] + 1}, storage_offset);
diag.copy_(grad);
return grad_input;
}

Tensor max_pool2d_double_backward(const Tensor & grad, const Tensor & indices) {
// fold the first two dims together and the last two together
auto fold = [](const Tensor & t) -> Tensor {
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