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Allow non-square lu (#1889)
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@ -59,10 +59,14 @@ void lu_factor_impl(
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}
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// Subtract 1 to get 0-based index
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for (int j = 0; j < pivots.shape(-1); ++j) {
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int j = 0;
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for (; j < pivots.shape(-1); ++j) {
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pivots_ptr[j]--;
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row_indices_ptr[j] = j;
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}
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for (; j < row_indices.shape(-1); ++j) {
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row_indices_ptr[j] = j;
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}
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for (int j = pivots.shape(-1) - 1; j >= 0; --j) {
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auto piv = pivots_ptr[j];
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auto t1 = row_indices_ptr[piv];
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@ -539,10 +539,6 @@ void validate_lu(
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<< a.ndim() << " dimensions.";
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throw std::invalid_argument(msg.str());
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}
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if (a.shape(-1) != a.shape(-2)) {
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throw std::invalid_argument(fname + " Only defined for square matrices.");
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}
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}
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std::vector<array> lu_helper(const array& a, StreamOrDevice s /* = {} */) {
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@ -552,8 +548,10 @@ std::vector<array> lu_helper(const array& a, StreamOrDevice s /* = {} */) {
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Shape pivots_shape(a.shape().begin(), a.shape().end() - 2);
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pivots_shape.push_back(std::min(m, n));
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Shape row_idx_shape(a.shape().begin(), a.shape().end() - 1);
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return array::make_arrays(
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{a.shape(), pivots_shape, pivots_shape},
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{a.shape(), pivots_shape, row_idx_shape},
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{a.dtype(), uint32, uint32},
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std::make_shared<LUF>(to_stream(s)),
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{astype(a, a.dtype(), s)});
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@ -565,10 +563,24 @@ std::vector<array> lu(const array& a, StreamOrDevice s /* = {} */) {
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auto out = lu_helper(a, s);
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auto& LU = out[0];
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auto& row_pivots = out[2];
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int N = a.shape(-1);
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auto L = add(tril(LU, /* k = */ -1, s), eye(N, s), s);
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auto L = tril(LU, /* k = */ -1, s);
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auto U = triu(LU, /* k = */ 0, s);
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int M = a.shape(-2);
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int N = a.shape(-1);
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int K = std::min(M, N);
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if (N != K) {
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auto start = Shape(L.ndim(), 0);
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auto stop = L.shape();
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stop.back() = K;
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L = slice(L, std::move(start), std::move(stop), s);
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} else if (M != K) {
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auto start = Shape(U.ndim(), 0);
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auto stop = U.shape();
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stop[U.ndim() - 2] = K;
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U = slice(U, std::move(start), std::move(stop), s);
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}
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L = add(L, eye(M, K, s), s);
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return {row_pivots, L, U};
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}
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@ -358,6 +358,15 @@ class TestLinalg(mlx_tests.MLXTestCase):
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L = mx.take_along_axis(L, P[..., None], axis=-2)
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self.assertTrue(mx.allclose(L @ U, a))
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# Test non-square matrix
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a = mx.array([[3.0, 1.0, 2.0], [1.0, 8.0, 6.0]])
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P, L, U = mx.linalg.lu(a, stream=mx.cpu)
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self.assertTrue(mx.allclose(L[P, :] @ U, a))
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a = mx.array([[3.0, 1.0], [1.0, 8.0], [9.0, 2.0]])
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P, L, U = mx.linalg.lu(a, stream=mx.cpu)
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self.assertTrue(mx.allclose(L[P, :] @ U, a))
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def test_lu_factor(self):
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mx.random.seed(7)
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