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Avoid using batched-matmul in nn::Linear. (#2883)

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* Avoid using batched-matmul in nn::Linear.

* Also avoid batched matmul in conv1d.

* Also tweak the conv2d.

* Batched tests.

* Also cover conv2d.
This commit is contained in:
Laurent Mazare
2025-04-12 19:53:58 +02:00
committed by GitHub
parent d7b7ce16e4
commit 34505fdf3a
3 changed files with 73 additions and 24 deletions
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30
candle-core/tests/conv_tests.rs
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@ -53,6 +53,20 @@ fn conv1d(dev: &Device) -> Result<()> {
test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
[2.4509, 2.6357, -1.3336, 4.1393, 0.5657, 1.8091, -1.1784, 3.5675, 0.5069, 3.3352]
);
let res = {
let t = Tensor::cat(&[&t.zeros_like()?, &t, &t.zeros_like()?], 0)?;
t.conv1d(&w, /*padding*/ 1, 1, 1, 1)?
};
assert_eq!(res.dims(), [3, 2, 5]);
// Same as pytorch default padding: use zeros.
assert_eq!(
test_utils::to_vec1_round(&res.i(0)?.flatten_all()?, 4)?,
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]
);
assert_eq!(
test_utils::to_vec1_round(&res.i(1)?.flatten_all()?, 4)?,
[2.4509, 2.6357, -1.3336, 4.1393, 0.5657, 1.8091, -1.1784, 3.5675, 0.5069, 3.3352]
);
let w = w.transpose(0, 1)?;
// The CPU kernels applied in the contiguous and non contiguous cases are different.
@ -163,6 +177,22 @@ fn conv2d(dev: &Device) -> Result<()> {
10.389, 3.6023, -4.2808, 0.2672, 5.3646, -5.2023, -2.1955, -9.4075
]
);
let res = {
let t = Tensor::cat(&[&t.zeros_like()?, &t, &t.zeros_like()?], 0)?;
t.conv2d(&w, 0, 1, 1, 1)?
};
assert_eq!(res.dims(), [3, 2, 3, 3]);
assert_eq!(
test_utils::to_vec1_round(&res.i(0)?.flatten_all()?, 4)?,
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]
);
assert_eq!(
test_utils::to_vec1_round(&res.i(1)?.flatten_all()?, 4)?,
[
-4.2812, 2.0923, 5.2187, 7.5184, 0.752, -14.9426, 10.0087, 4.391, 0.2918, 1.6715,
10.389, 3.6023, -4.2808, 0.2672, 5.3646, -5.2023, -2.1955, -9.4075
]
);
let res = t.conv_transpose2d(&w.transpose(0, 1)?, 0, 0, 1, 1)?;