Make the nll op closer to the pytorch version + add a test. (#286)

1 files changed, 22 insertions, 2 deletions

diff --git a/candle-nn/src/loss.rs b/candle-nn/src/loss.rs
index d388af6c..b380c426 100644
--- a/candle-nn/src/loss.rs
+++ b/candle-nn/src/loss.rs
@@ -1,8 +1,28 @@
 use candle::{Result, Tensor};
 
+/// The negative loss likelihodd loss.
+///
+/// Arguments
+///
+/// * [inp]: The input tensor of dimensions `N, C` where `N` is the batch size and `C` the number
+///          of categories. This is expected to contain log probabilities.
+/// * [target]: The ground truth labels as a tensor of u32 of dimension `N`.
+///
+/// The resulting tensor is a scalar containing the average value over the batch.
 pub fn nll(inp: &Tensor, target: &Tensor) -> Result<Tensor> {
-    let b_sz = target.dim(0)?;
-    inp.gather(target, 1)?
+    let b_sz = match target.dims() {
+        &[b_sz] => b_sz,
+        dims => candle::bail!("the target tensor should have a single dimension ({dims:?})"),
+    };
+    match inp.dims() {
+        &[inp_b_sz, _] => {
+            if inp_b_sz != b_sz {
+                candle::bail!("batch size mismatch between inp ({inp_b_sz}) and target ({b_sz})")
+            }
+        }
+        dims => candle::bail!("the target tensor should have two dimensions ({dims:?})"),
+    }
+    inp.gather(&target.unsqueeze(1)?, 1)?
         .sum_all()?
         .affine(-1f64 / b_sz as f64, 0.)
 }