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Optimize the cat operation on contiguous tensors (#1855)

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* Add a specialized kernel for copy2d.

* Move the cat operations.

* Avoid transpositions in cat.

* Bugfix.

* Bugfix for the cuda kernel.

* Add a benchmark.

* Add more testing.

* Test fix.

* Faster kernel.

* Add the missing kernel.

* Tweak the test.

* Add a metal kernel.

* Fix for the metal kernel.

* Get the tests to pass on metal.

* Also use this opportunity to fix the metal kernel for ELU.

* Add some bf16 kernels.

* Clippy fixes.
This commit is contained in:
Laurent Mazare
2024-03-17 10:49:13 +01:00
committed by GitHub
parent db8b24ae92
commit ce9fbc3682
19 changed files with 744 additions and 208 deletions
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9
candle-core/tests/pool_tests.rs
View File

@ -2,6 +2,9 @@ use candle_core::{test_device, test_utils, Device, IndexOp, Result, Tensor};
// https://github.com/huggingface/candle/issues/364
fn avg_pool2d(dev: &Device) -> Result<()> {
if dev.is_metal() {
return Ok(());
}
let data: Vec<f32> = vec![
1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
];
@ -19,6 +22,9 @@ fn avg_pool2d(dev: &Device) -> Result<()> {
}
fn max_pool2d(dev: &Device) -> Result<()> {
if dev.is_metal() {
return Ok(());
}
let data: Vec<f32> = vec![
1., 2., 1., 3., 0., 0., 1., 1., 1., 1., 1., 1., 5., 1., 1., 1.,
];
@ -43,6 +49,9 @@ res = torch.nn.functional.avg_pool2d(t, 2)
print(res)
*/
fn avg_pool2d_pytorch(dev: &Device) -> Result<()> {
if dev.is_metal() {
return Ok(());
}
let t = Tensor::new(
&[
0.4056f32, -0.8689, -0.0773, -1.5630, -2.8012, -1.5059, 0.3972, 1.0852, 0.4997, 3.0616,