1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
|
//! Batch Normalization.
//!
//! This layer applies Batch Normalization over a mini-batch of inputs as described in [`Batch
//! Normalization`]. The input is expected to have at least three dimensions.
//!
//! Note that this implementation is for inference only, there is no possibility to track the
//! running stats.
//!
//! [`Batch Normalization`]: https://arxiv.org/abs/1502.03167
use candle::{DType, Result, Tensor};
#[derive(Debug, Clone, Copy, PartialEq)]
pub struct BatchNormConfig {
pub eps: f64,
pub remove_mean: bool,
/// The meaning of affine here is different from LayerNorm: when false there is no learnable
/// parameter at all, 1 used for gamma and 0 for beta.
pub affine: bool,
}
impl Default for BatchNormConfig {
fn default() -> Self {
Self {
eps: 1e-5,
remove_mean: true,
affine: true,
}
}
}
impl From<f64> for BatchNormConfig {
fn from(eps: f64) -> Self {
Self {
eps,
remove_mean: true,
affine: true,
}
}
}
#[derive(Clone, Debug)]
pub struct BatchNorm {
running_mean: Tensor,
running_var: Tensor,
weight_and_bias: Option<(Tensor, Tensor)>,
remove_mean: bool,
eps: f64,
num_features: usize,
}
impl BatchNorm {
pub fn new(
num_features: usize,
running_mean: Tensor,
running_var: Tensor,
weight: Tensor,
bias: Tensor,
eps: f64,
) -> Result<Self> {
if eps < 0. {
candle::bail!("batch-norm eps cannot be negative {eps}")
}
if weight.dims() != [num_features] {
candle::bail!(
"batch-norm unexpected weight shape {:?} {num_features}",
weight.shape()
)
}
if bias.dims() != [num_features] {
candle::bail!(
"batch-norm unexpected bias shape {:?} {num_features}",
bias.shape()
)
}
Ok(Self {
running_mean,
running_var,
weight_and_bias: Some((weight, bias)),
remove_mean: true,
eps,
num_features,
})
}
pub fn new_no_bias(
num_features: usize,
running_mean: Tensor,
running_var: Tensor,
eps: f64,
) -> Result<Self> {
if eps < 0. {
candle::bail!("batch-norm eps cannot be negative {eps}")
}
Ok(Self {
running_mean,
running_var,
weight_and_bias: None,
remove_mean: true,
eps,
num_features,
})
}
pub fn running_mean(&self) -> &Tensor {
&self.running_mean
}
pub fn running_var(&self) -> &Tensor {
&self.running_var
}
pub fn eps(&self) -> f64 {
self.eps
}
pub fn weight_and_bias(&self) -> Option<(&Tensor, &Tensor)> {
self.weight_and_bias.as_ref().map(|v| (&v.0, &v.1))
}
pub fn forward_learning(&self, x: &Tensor) -> Result<Tensor> {
let x_dtype = x.dtype();
let internal_dtype = match x_dtype {
DType::F16 | DType::BF16 => DType::F32,
d => d,
};
if x.rank() < 2 {
candle::bail!(
"batch-norm input tensor must have at least two dimensions ({:?})",
x.shape()
)
}
if x.dim(1)? != self.num_features {
candle::bail!(
"batch-norm input doesn't have the expected number of features ({:?} <> {})",
x.shape(),
self.num_features
)
}
let x = x.to_dtype(internal_dtype)?;
let x = x.transpose(0, 1)?;
let x_dims_post_transpose = x.dims();
let x = x.flatten_from(1)?.contiguous()?;
let x = if self.remove_mean {
let mean_x = x.mean_keepdim(1)?;
x.broadcast_sub(&mean_x)?
} else {
x
};
let norm_x = x.sqr()?.mean_keepdim(1)?;
let x_normed = x.broadcast_div(&(norm_x + self.eps)?.sqrt()?)?;
let x = x_normed.to_dtype(x_dtype)?;
let x = match &self.weight_and_bias {
None => x,
Some((weight, bias)) => {
let weight = weight.reshape((self.num_features, 1))?;
let bias = bias.reshape((self.num_features, 1))?;
x.broadcast_mul(&weight)?.broadcast_add(&bias)?
}
};
x.reshape(x_dims_post_transpose)?.transpose(0, 1)
}
}
impl crate::Module for BatchNorm {
fn forward(&self, x: &Tensor) -> Result<Tensor> {
let target_shape: Vec<usize> = x
.dims()
.iter()
.enumerate()
.map(|(idx, v)| if idx == 1 { *v } else { 1 })
.collect();
let target_shape = target_shape.as_slice();
let x = x
.broadcast_sub(&self.running_mean.reshape(target_shape)?)?
.broadcast_div(&(self.running_var.reshape(target_shape)? + self.eps)?.sqrt()?)?;
match &self.weight_and_bias {
None => Ok(x),
Some((weight, bias)) => {
let weight = weight.reshape(target_shape)?;
let bias = bias.reshape(target_shape)?;
x.broadcast_mul(&weight)?.broadcast_add(&bias)
}
}
}
}
pub fn batch_norm<C: Into<BatchNormConfig>>(
num_features: usize,
config: C,
vb: crate::VarBuilder,
) -> Result<BatchNorm> {
let config = config.into();
if config.eps < 0. {
candle::bail!("batch-norm eps cannot be negative {}", config.eps)
}
let running_mean = vb.get_with_hints(num_features, "running_mean", crate::Init::Const(0.))?;
let running_var = vb.get_with_hints(num_features, "running_var", crate::Init::Const(1.))?;
let weight_and_bias = if config.affine {
let weight = vb.get_with_hints(num_features, "weight", crate::Init::Const(1.))?;
let bias = vb.get_with_hints(num_features, "bias", crate::Init::Const(0.))?;
Some((weight, bias))
} else {
None
};
Ok(BatchNorm {
running_mean,
running_var,
weight_and_bias,
remove_mean: config.remove_mean,
eps: config.eps,
num_features,
})
}
|
