huggingface/candle
1
0
Fork 0
mirror of https://github.com/huggingface/candle.git synced 2025-06-17 19:18:50 +00:00
Code Issues Packages Projects Releases Wiki Activity

Implement the module trait directly for QMatMul. (#1372)

Browse Source
This commit is contained in:
Laurent Mazare
2023-11-25 10:09:45 +00:00
committed by GitHub
parent 762e996ce6
commit bfa7c8fc01
7 changed files with 11 additions and 18 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
candle-core/examples/basics.rs
View File

@ -8,11 +8,10 @@ use anyhow::Result;
use candle_core::{Device, Tensor};
fn main() -> Result<()> {
let inp = Tensor::randn(0f32, 1., (2, 320, 96, 96), &Device::Cpu)?;
let w = Tensor::randn(0f32, 1., (320, 320, 3, 3), &Device::Cpu)?;
let start = std::time::Instant::now();
let res = inp.conv2d(&w, 0, 1, 1, 1)?;
println!("{:?}", start.elapsed());
println!("{res:?}");
let a = Tensor::new(&[[0.0f32, 1.0, 2.0], [3.0, 4.0, 5.0]], &Device::Cpu)?;
let b = Tensor::new(&[[88.0f32, 99.0]], &Device::Cpu)?;
let new_a = a.slice_scatter(&b, 1, 2)?;
assert_eq!(a.to_vec2::<f32>()?, [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]);
assert_eq!(new_a.to_vec2::<f32>()?, [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]);
Ok(())
}