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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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18
candle-core/tests/grad_tests.rs
View File

@ -1,3 +1,4 @@
#![allow(clippy::approx_constant)]
use anyhow::{Context, Result};
use candle_core::{test_device, test_utils, Device, Shape, Tensor, Var};
@ -96,24 +97,24 @@ fn unary_grad(device: &Device) -> Result<()> {
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(
y.to_vec1::<f32>()?,
[20.085537, 2.7182817, 54.59815, 1.1618342]
test_utils::to_vec1_round(&y, 4)?,
[20.0855, 2.7183, 54.5982, 1.1618]
);
assert_eq!(
grad_x.to_vec1::<f32>()?,
[20.085537, 2.7182817, 54.59815, 1.1618342]
test_utils::to_vec1_round(grad_x, 4)?,
[20.0855, 2.7183, 54.5982, 1.1618]
);
let y = x.exp()?.sqr()?;
let grads = y.backward()?;
let grad_x = grads.get(x).context("no grad for x")?;
assert_eq!(
y.to_vec1::<f32>()?,
[403.4288, 7.3890557, 2980.9578, 1.3498588]
test_utils::to_vec1_round(&y, 3)?,
[403.429, 7.389, 2980.958, 1.35]
);
// exp(x)^2 = exp(2*x)
assert_eq!(
grad_x.to_vec1::<f32>()?,
[806.8576, 14.778111, 5961.9155, 2.6997175]
test_utils::to_vec1_round(grad_x, 2)?,
[806.86, 14.78, 5961.92, 2.7]
);
let y = x.sin()?;
let grads = y.backward()?;
@ -261,6 +262,7 @@ fn unary_grad(device: &Device) -> Result<()> {
let y = elu_x.elu(2.)?;
let grads = y.backward()?;
let grad_x = grads.get(&elu_x).context("no grad for x")?;
assert_eq!(
test_utils::to_vec1_round(&y, 4)?,
[-1.2642, 0.0000, -1.7293, 3.0000]