Add the candle-datasets crate (#322)

* Move the vision datasets to a separate crate.

* Move the batcher bits.

* Update the readme.

* Move the tiny-stories bits.

---------

Co-authored-by: Jane Doe <jane.doe@example.org>

candle-datasets/src/batcher.rs

@@ -0,0 +1,171 @@
+use candle::{Result, Tensor};
+
+pub struct Batcher<I> {
+    inner: I,
+    batch_size: usize,
+    return_last_incomplete_batch: bool,
+}
+
+impl<I> Batcher<I> {
+    fn new(inner: I) -> Self {
+        Self {
+            inner,
+            batch_size: 16,
+            return_last_incomplete_batch: false,
+        }
+    }
+
+    pub fn batch_size(mut self, batch_size: usize) -> Self {
+        self.batch_size = batch_size;
+        self
+    }
+
+    pub fn return_last_incomplete_batch(mut self, r: bool) -> Self {
+        self.return_last_incomplete_batch = r;
+        self
+    }
+}
+
+pub struct Iter1<I: Iterator<Item = Tensor>> {
+    inner: I,
+}
+
+pub struct Iter2<I: Iterator<Item = (Tensor, Tensor)>> {
+    inner: I,
+}
+
+impl<I: Iterator<Item = Tensor>> Batcher<Iter1<I>> {
+    pub fn new1(inner: I) -> Self {
+        Self::new(Iter1 { inner })
+    }
+}
+
+impl<I: Iterator<Item = (Tensor, Tensor)>> Batcher<Iter2<I>> {
+    pub fn new2(inner: I) -> Self {
+        Self::new(Iter2 { inner })
+    }
+}
+
+pub struct IterResult1<I: Iterator<Item = Result<Tensor>>> {
+    inner: I,
+}
+
+pub struct IterResult2<I: Iterator<Item = Result<(Tensor, Tensor)>>> {
+    inner: I,
+}
+
+impl<I: Iterator<Item = Result<Tensor>>> Batcher<IterResult1<I>> {
+    pub fn new_r1(inner: I) -> Self {
+        Self::new(IterResult1 { inner })
+    }
+}
+
+impl<I: Iterator<Item = Result<(Tensor, Tensor)>>> Batcher<IterResult2<I>> {
+    pub fn new_r2(inner: I) -> Self {
+        Self::new(IterResult2 { inner })
+    }
+}
+
+impl<I: Iterator<Item = Tensor>> Iterator for Batcher<Iter1<I>> {
+    type Item = Result<Tensor>;
+
+    fn next(&mut self) -> Option<Self::Item> {
+        let mut items = Vec::with_capacity(self.batch_size);
+        for _i in 0..self.batch_size {
+            // We have two levels of inner here so that we can have two implementations of the
+            // Iterator trait that are different for Iter1 and Iter2. If rust gets better
+            // specialization at some point we can get rid of this.
+            match self.inner.inner.next() {
+                Some(item) => items.push(item),
+                None => {
+                    if self.return_last_incomplete_batch {
+                        break;
+                    }
+                    return None;
+                }
+            }
+        }
+        Some(Tensor::stack(&items, 0))
+    }
+}
+
+impl<I: Iterator<Item = (Tensor, Tensor)>> Iterator for Batcher<Iter2<I>> {
+    type Item = Result<(Tensor, Tensor)>;
+
+    fn next(&mut self) -> Option<Self::Item> {
+        let mut xs = Vec::with_capacity(self.batch_size);
+        let mut ys = Vec::with_capacity(self.batch_size);
+        for _i in 0..self.batch_size {
+            match self.inner.inner.next() {
+                Some((x, y)) => {
+                    xs.push(x);
+                    ys.push(y)
+                }
+                None => {
+                    if self.return_last_incomplete_batch {
+                        break;
+                    }
+                    return None;
+                }
+            }
+        }
+        let xs = Tensor::stack(&xs, 0);
+        let ys = Tensor::stack(&ys, 0);
+        Some(xs.and_then(|xs| ys.map(|ys| (xs, ys))))
+    }
+}
+
+impl<I: Iterator<Item = Result<Tensor>>> Iterator for Batcher<IterResult1<I>> {
+    type Item = Result<Tensor>;
+
+    fn next(&mut self) -> Option<Self::Item> {
+        let mut items = Vec::with_capacity(self.batch_size);
+        for _i in 0..self.batch_size {
+            // We have two levels of inner here so that we can have two implementations of the
+            // Iterator trait that are different for Iter1 and Iter2. If rust gets better
+            // specialization at some point we can get rid of this.
+            match self.inner.inner.next() {
+                Some(item) => items.push(item),
+                None => {
+                    if self.return_last_incomplete_batch {
+                        break;
+                    }
+                    return None;
+                }
+            }
+        }
+        let items = items.into_iter().collect::<Result<Vec<Tensor>>>();
+        Some(items.and_then(|items| Tensor::stack(&items, 0)))
+    }
+}
+
+impl<I: Iterator<Item = Result<(Tensor, Tensor)>>> Iterator for Batcher<IterResult2<I>> {
+    type Item = Result<(Tensor, Tensor)>;
+
+    fn next(&mut self) -> Option<Self::Item> {
+        let mut xs = Vec::with_capacity(self.batch_size);
+        let mut ys = Vec::with_capacity(self.batch_size);
+        let mut errs = vec![];
+        for _i in 0..self.batch_size {
+            match self.inner.inner.next() {
+                Some(Ok((x, y))) => {
+                    xs.push(x);
+                    ys.push(y)
+                }
+                Some(Err(err)) => errs.push(err),
+                None => {
+                    if self.return_last_incomplete_batch {
+                        break;
+                    }
+                    return None;
+                }
+            }
+        }
+        if !errs.is_empty() {
+            return Some(Err(errs.swap_remove(0)));
+        }
+        let xs = Tensor::stack(&xs, 0);
+        let ys = Tensor::stack(&ys, 0);
+        Some(xs.and_then(|xs| ys.map(|ys| (xs, ys))))
+    }
+}