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Simplify the parameters used by sum and sum_keepdim. (#165)

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This commit is contained in:
Laurent Mazare
2023-07-14 08:22:08 +01:00
committed by GitHub
parent 2bfa791336
commit a2f72edc0d
13 changed files with 179 additions and 98 deletions
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100
candle-core/src/tensor.rs
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@ -1,5 +1,5 @@
use crate::backend::{BackendDevice, BackendStorage};
use crate::shape::Dim;
use crate::shape::{Dim, Dims};
use crate::{op::Op, storage::Storage, DType, Device, Error, Layout, Result, Shape};
use std::sync::{Arc, RwLock};
@ -572,7 +572,7 @@ impl Tensor {
// We do not have a cuda kernel for divide_by_sum_over_dim so split
// the operation.
let exp = self.exp()?;
let sum_exp = exp.sum_keepdim(&[dim])?;
let sum_exp = exp.sum_keepdim(dim)?;
exp.broadcast_div(&sum_exp)
} else {
let shape = self.shape();
@ -588,28 +588,9 @@ impl Tensor {
}
}
/// Returns the sum of all elements in the input tensor. The sum is performed over all the
/// input dimensions.
///
/// The resulting tensor has a shape that is similar to the shape of the input tensor, except
/// that the number of elements for each dimension index in `sum_dims` is 1.
///
/// ```rust
/// use candle::{Tensor, Device};
/// let a = Tensor::new(&[[0f32, 1.], [2., 3.]], &Device::Cpu)?;
/// let s = a.sum_keepdim(&[0])?;
/// assert_eq!(s.to_vec2::<f32>()?, &[[2., 4.]]);
/// let s = a.sum_keepdim(&[1])?;
/// assert_eq!(s.to_vec2::<f32>()?, &[[1.], [5.]]);
/// let s = a.sum_keepdim(&[0, 1])?;
/// assert_eq!(s.to_vec2::<f32>()?, &[[6.]]);
/// # Ok::<(), candle::Error>(())
/// ```
pub fn sum_keepdim(&self, sum_dims: &[usize]) -> Result<Self> {
for &dim in sum_dims {
self.check_dim(dim, "sum")?;
}
let storage = self.storage().sum(self.layout(), sum_dims)?;
pub fn sum_impl<D: Dims>(&self, sum_dims: D, keepdim: bool) -> Result<Self> {
let sum_dims = sum_dims.to_indexes(self.shape(), "sum")?;
let storage = self.storage().sum(self.layout(), &sum_dims)?;
let op = if self.track_op() {
Some(Op::Sum(self.clone(), sum_dims.to_vec()))
} else {
@ -619,33 +600,58 @@ impl Tensor {
for &sum_dim in sum_dims.iter() {
dims[sum_dim] = 1
}
Ok(from_storage(storage, dims, op, false))
let sum = from_storage(storage, dims, op, false);
if keepdim {
Ok(sum)
} else {
match sum_dims.as_slice() {
[] => Ok(sum),
[i] => sum.squeeze(*i),
sum_dims => {
let dims = sum
.dims()
.iter()
.enumerate()
.filter_map(|(dim_idx, &v)| {
if sum_dims.contains(&dim_idx) {
None
} else {
Some(v)
}
})
.collect::<Vec<_>>();
sum.reshape(dims)
}
}
}
}
/// Returns the sum of all elements in the input tensor. The sum is performed over all the
/// input dimensions.
///
/// The resulting tensor has a shape that is similar to the shape of the input tensor, except
/// that the number of elements for each dimension index in `sum_dims` is 1.
///
/// ```rust
/// use candle::{Tensor, Device};
/// let a = Tensor::new(&[[0f32, 1.], [2., 3.]], &Device::Cpu)?;
/// let s = a.sum_keepdim(0)?;
/// assert_eq!(s.to_vec2::<f32>()?, &[[2., 4.]]);
/// let s = a.sum_keepdim(1)?;
/// assert_eq!(s.to_vec2::<f32>()?, &[[1.], [5.]]);
/// let s = a.sum_keepdim((0, 1))?;
/// assert_eq!(s.to_vec2::<f32>()?, &[[6.]]);
/// # Ok::<(), candle::Error>(())
/// ```
pub fn sum_keepdim<D: Dims>(&self, sum_dims: D) -> Result<Self> {
self.sum_impl(sum_dims, true)
}
/// Returns the sum of all elements in the input tensor. The sum is performed over all the
/// input dimensions and compared to `sum_keepdim` these dimensions are squeezed rather than
/// kept.
pub fn sum(&self, sum_dims: &[usize]) -> Result<Self> {
let sum = self.sum_keepdim(sum_dims)?;
match sum_dims {
[] => Ok(sum),
[i] => sum.squeeze(*i),
sum_dims => {
let dims = sum
.dims()
.iter()
.enumerate()
.filter_map(|(dim_idx, &v)| {
if sum_dims.contains(&dim_idx) {
None
} else {
Some(v)
}
})
.collect::<Vec<_>>();
sum.reshape(dims)
}
}
pub fn sum<D: Dims>(&self, sum_dims: D) -> Result<Self> {
self.sum_impl(sum_dims, false)
}
/// Applies a 1D convolution over the input tensor.
@ -962,7 +968,7 @@ impl Tensor {
/// ```
pub fn sum_all(&self) -> Result<Tensor> {
let dims: Vec<_> = (0..self.rank()).collect();
self.sum_keepdim(&dims)?.reshape(())
self.sum(dims)
}
fn flatten_<D1: Dim, D2: Dim>(