dnrops
/
candle
mirror of https://github.com/huggingface/candle.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Implement group-norm. (#334) 

* Implement group-norm.

* Add some testing for group-norm.
This commit is contained in:
Laurent Mazare 2023-08-07 07:53:05 +02:00 committed by GitHub
parent 2c9f605976
commit 5bb2fce998
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 150 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
candle-examples/examples/stable-diffusion/clip.rs
View File

@ -6,6 +6,7 @@
//!
//! https://github.com/openai/CLIP
use candle::{Device, Result, Tensor, D};
use candle_nn as nn;
#[derive(Debug, Clone, Copy)]
pub enum Activation {
@ -16,7 +17,7 @@ pub enum Activation {
impl Activation {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
match self {
Activation::QuickGelu => xs * crate::utils::sigmoid(&(xs * 1.702f64)?)?,
Activation::QuickGelu => xs * nn::ops::sigmoid(&(xs * 1.702f64)?)?,
Activation::Gelu => xs.gelu(),
}
}

5
candle-examples/examples/stable-diffusion/utils.rs
View File

@ -1,10 +1,5 @@
use candle::{Device, Result, Tensor};
pub fn sigmoid(xs: &Tensor) -> Result<Tensor> {
// TODO: Add sigmoid as binary ops.
(xs.neg()?.exp()? - 1.0)?.recip()
}
pub fn avg_pool2d(_: &Tensor) -> Result<Tensor> {
todo!()
}

47
candle-nn/src/group_norm.rs
View File

@ -1,10 +1,9 @@
//! Group Normalization.
//!
//! This layer applies Group Normalization over a mini-batch of inputs.
use candle::{Result, Tensor};
use candle::{DType, Result, Tensor};
// This group norm version handles both weight and bias so removes the mean.
#[allow(dead_code)]
#[derive(Debug)]
pub struct GroupNorm {
weight: Tensor,
@ -21,18 +20,50 @@ impl GroupNorm {
num_channels: usize,
num_groups: usize,
eps: f64,
) -> Self {
Self {
) -> Result<Self> {
if num_channels % num_groups != 0 {
candle::bail!(
"GroupNorm: num_groups ({num_groups}) must divide num_channels ({num_channels})"
)
}
Ok(Self {
weight,
bias,
eps,
num_channels,
num_groups,
}
})
}
pub fn forward(&self, _: &Tensor) -> Result<Tensor> {
todo!()
pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
let x_shape = x.dims();
if x_shape.len() <= 2 {
candle::bail!("input rank for GroupNorm should be at least 3");
}
let (b_sz, n_channels) = (x_shape[0], x_shape[1]);
let hidden_size = x_shape[2..].iter().product::<usize>() * n_channels / self.num_groups;
if n_channels != self.num_channels {
candle::bail!(
"unexpected num-channels in GroupNorm ({n_channels} <> {}",
self.num_channels
)
}
let x_dtype = x.dtype();
let internal_dtype = match x_dtype {
DType::F16 | DType::BF16 => DType::F32,
d => d,
};
let x = x.reshape((b_sz, self.num_groups, hidden_size))?;
let x = x.to_dtype(internal_dtype)?;
let mean_x = (x.sum_keepdim(2)? / hidden_size as f64)?;
let x = x.broadcast_sub(&mean_x)?;
let norm_x = (x.sqr()?.sum_keepdim(2)? / hidden_size as f64)?;
let x_normed = x.broadcast_div(&(norm_x + self.eps)?.sqrt()?)?;
x_normed
.to_dtype(x_dtype)?
.broadcast_mul(&self.weight)?
.broadcast_add(&self.bias)?
.reshape(x_shape)
}
}
@ -44,5 +75,5 @@ pub fn group_norm(
) -> Result<GroupNorm> {
let weight = vb.get_or_init(num_channels, "weight", crate::Init::Const(1.))?;
let bias = vb.get_or_init(num_channels, "bias", crate::Init::Const(0.))?;
Ok(GroupNorm::new(weight, bias, num_channels, num_groups, eps))
GroupNorm::new(weight, bias, num_channels, num_groups, eps)
}

6
candle-nn/src/ops.rs
View File

@ -34,5 +34,11 @@ pub fn log_softmax<D: candle::shape::Dim>(xs: &Tensor, d: D) -> Result<Tensor> {
}
pub fn silu(xs: &Tensor) -> Result<Tensor> {
// TODO: Should we have a specialized op for this?
xs / (xs.neg()?.exp()? + 1.0)?
}
pub fn sigmoid(xs: &Tensor) -> Result<Tensor> {
// TODO: Should we have a specialized op for this?
(xs.neg()?.exp()? + 1.0)?.recip()
}

103
candle-nn/tests/group_norm.rs Normal file
View File

@ -0,0 +1,103 @@
/* Equivalent PyTorch code.
import torch
from torch.nn.functional import group_norm
t = torch.tensor(
[[[-0.3034, 0.2726, -0.9659],
[-1.1845, -1.3236, 0.0172],
[ 1.9507, 1.2554, -0.8625],
[ 1.0682, 0.3604, 0.3985],
[-0.4957, -0.4461, -0.9721],
[ 1.5157, -0.1546, -0.5596]],
[[-1.6698, -0.4040, -0.7927],
[ 0.3736, -0.0975, -0.1351],
[-0.9461, 0.5461, -0.6334],
[-1.0919, -0.1158, 0.1213],
[-0.9535, 0.1281, 0.4372],
[-0.2845, 0.3488, 0.5641]]])
print(group_norm(t, num_groups=2))
print(group_norm(t, num_groups=3))
*/
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::Result;
use candle::{Device, Tensor};
use candle_nn::GroupNorm;
mod test_utils;
use test_utils::to_vec3_round;
#[test]
fn group_norm() -> Result<()> {
let device = &Device::Cpu;
let w = Tensor::new(&[1f32], device)?;
let b = Tensor::new(&[0f32], device)?;
let gn2 = GroupNorm::new(w.clone(), b.clone(), 6, 2, 1e-5)?;
let gn3 = GroupNorm::new(w, b, 6, 3, 1e-5)?;
let input = Tensor::new(
&[
[
[-0.3034f32, 0.2726, -0.9659],
[-1.1845, -1.3236, 0.0172],
[1.9507, 1.2554, -0.8625],
[1.0682, 0.3604, 0.3985],
[-0.4957, -0.4461, -0.9721],
[1.5157, -0.1546, -0.5596],
],
[
[-1.6698, -0.4040, -0.7927],
[0.3736, -0.0975, -0.1351],
[-0.9461, 0.5461, -0.6334],
[-1.0919, -0.1158, 0.1213],
[-0.9535, 0.1281, 0.4372],
[-0.2845, 0.3488, 0.5641],
],
],
device,
)?;
assert_eq!(
to_vec3_round(gn2.forward(&input)?, 4)?,
&[
[
[-0.1653, 0.3748, -0.7866],
[-0.9916, -1.1220, 0.1353],
[1.9485, 1.2965, -0.6896],
[1.2769, 0.3628, 0.4120],
[-0.7427, -0.6786, -1.3578],
[1.8547, -0.3022, -0.8252]
],
[
[-1.9342, 0.0211, -0.5793],
[1.2223, 0.4945, 0.4365],
[-0.8163, 1.4887, -0.3333],
[-1.7960, -0.0392, 0.3875],
[-1.5469, 0.3998, 0.9561],
[-0.3428, 0.7970, 1.1845]
]
]
);
assert_eq!(
to_vec3_round(gn3.forward(&input)?, 4)?,
&[
[
[0.4560, 1.4014, -0.6313],
[-0.9901, -1.2184, 0.9822],
[1.4254, 0.6360, -1.7682],
[0.4235, -0.3800, -0.3367],
[-0.3890, -0.3268, -0.9862],
[2.1325, 0.0386, -0.4691]
],
[
[-1.8797, 0.0777, -0.5234],
[1.2802, 0.5517, 0.4935],
[-1.0102, 1.5327, -0.4773],
[-1.2587, 0.4047, 0.8088],
[-1.9074, 0.1691, 0.7625],
[-0.6230, 0.5928, 1.0061]
]
]
);
Ok(())
}