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Add cpu support for min and max. (#202)

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* Add cpu support for min and max.

* Add min/max all.
This commit is contained in:
Laurent Mazare
2023-07-19 18:11:44 +02:00
committed by GitHub
parent e6584476c4
commit ad12e20f6b
3 changed files with 62 additions and 28 deletions
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31
candle-core/src/tensor.rs
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@ -1,6 +1,7 @@
use crate::backend::{BackendDevice, BackendStorage};
use crate::op::{Op, ReduceOp};
use crate::shape::{Dim, Dims};
use crate::{op::Op, storage::Storage, DType, Device, Error, Layout, Result, Shape};
use crate::{storage::Storage, DType, Device, Error, Layout, Result, Shape};
use std::sync::{Arc, RwLock};
/// Unique identifier for tensors.
@ -629,9 +630,9 @@ impl Tensor {
fn max_impl<D: Dims>(&self, max_dims: D, keepdim: bool) -> Result<Self> {
let max_dims = max_dims.to_indexes(self.shape(), "max")?;
let storage =
self.storage()
.reduce_op(crate::op::ReduceOp::Max, self.layout(), &max_dims)?;
let storage = self
.storage()
.reduce_op(ReduceOp::Max, self.layout(), &max_dims)?;
let op = if self.track_op() {
Some(Op::Max(self.clone(), max_dims.to_vec()))
} else {
@ -651,9 +652,9 @@ impl Tensor {
fn min_impl<D: Dims>(&self, min_dims: D, keepdim: bool) -> Result<Self> {
let min_dims = min_dims.to_indexes(self.shape(), "min")?;
let storage =
self.storage()
.reduce_op(crate::op::ReduceOp::Min, self.layout(), &min_dims)?;
let storage = self
.storage()
.reduce_op(ReduceOp::Min, self.layout(), &min_dims)?;
let op = if self.track_op() {
Some(Op::Min(self.clone(), min_dims.to_vec()))
} else {
@ -673,9 +674,9 @@ impl Tensor {
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()
.reduce_op(crate::op::ReduceOp::Sum, self.layout(), &sum_dims)?;
let storage = self
.storage()
.reduce_op(ReduceOp::Sum, self.layout(), &sum_dims)?;
let op = if self.track_op() {
Some(Op::Sum(self.clone(), sum_dims.to_vec()))
} else {
@ -729,6 +730,11 @@ impl Tensor {
self.max_impl(max_dims, false)
}
pub fn max_all(&self) -> Result<Tensor> {
let dims: Vec<_> = (0..self.rank()).collect();
self.max(dims)
}
pub fn min_keepdim<D: Dims>(&self, min_dims: D) -> Result<Self> {
self.min_impl(min_dims, true)
}
@ -737,6 +743,11 @@ impl Tensor {
self.min_impl(min_dims, false)
}
pub fn min_all(&self) -> Result<Tensor> {
let dims: Vec<_> = (0..self.rank()).collect();
self.min(dims)
}
/// Applies a 1D convolution over the input tensor.
pub fn conv1d(&self, kernel: &Self, padding: usize, stride: usize) -> Result<Self> {
let (c_out, c_in_k, k_size) = kernel.shape().r3()?;