Fix NLL double-shift bug and head weight init

- NLL loss was shifting labels twice (olmo_labels already shifted,
  then code did logits[:,:-1] vs labels[:,1:]). Fixed in 9 locations:
  trainer, pipeline, olmo_graph, sanity_check, eval.
- Head U/V weights init with std=0.01 (was Kaiming ~5.7 std) so
  UV^T≈0 at init, ensuring Z≈logit_bias=15 and A≈0.953.
- Updated SVD rank test to subtract logit_bias before checking.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

1 files changed, 9 insertions, 9 deletions

diff --git a/src/training/trainer.py b/src/training/trainer.py
index de0eb96..7ebd21e 100644
--- a/src/training/trainer.py
+++ b/src/training/trainer.py
@@ -299,10 +299,10 @@ class Trainer:
                 A = self.predictor(raw_texts, tau=tau, mode="train")
                 logits = self.olmo_wrapper(olmo_ids, A)
 
-                # NLL loss
+                # NLL loss (olmo_labels already shifted, no additional shift needed)
                 nll = F.cross_entropy(
-                    logits[:, :-1].contiguous().view(-1, self.olmo.config.vocab_size),
-                    olmo_labels[:, 1:].contiguous().view(-1),
+                    logits.contiguous().view(-1, self.olmo.config.vocab_size),
+                    olmo_labels.contiguous().view(-1),
                 )
 
                 # Sparsity loss
@@ -417,8 +417,8 @@ class Trainer:
             A_soft = self.predictor(raw_texts, tau=tau, mode="eval_soft")
             logits_soft = self.olmo_wrapper(olmo_ids, A_soft)
             nll_soft = F.cross_entropy(
-                logits_soft[:, :-1].contiguous().view(-1, vocab_size),
-                olmo_labels[:, 1:].contiguous().view(-1),
+                logits_soft.contiguous().view(-1, vocab_size),
+                olmo_labels.contiguous().view(-1),
             )
             nll_soft_total += nll_soft.item()
 
@@ -426,8 +426,8 @@ class Trainer:
             A_hard = self.predictor(raw_texts, tau=tau, mode="eval_hard")
             logits_hard = self.olmo_wrapper(olmo_ids, A_hard)
             nll_hard = F.cross_entropy(
-                logits_hard[:, :-1].contiguous().view(-1, vocab_size),
-                olmo_labels[:, 1:].contiguous().view(-1),
+                logits_hard.contiguous().view(-1, vocab_size),
+                olmo_labels.contiguous().view(-1),
             )
             nll_hard_total += nll_hard.item()
 
@@ -435,8 +435,8 @@ class Trainer:
             A_ones = create_all_ones_A(olmo_ids.shape[0]).to(self.device)
             logits_base = self.olmo_wrapper(olmo_ids, A_ones)
             nll_base = F.cross_entropy(
-                logits_base[:, :-1].contiguous().view(-1, vocab_size),
-                olmo_labels[:, 1:].contiguous().view(-1),
+                logits_base.contiguous().view(-1, vocab_size),
+                olmo_labels.contiguous().view(-1),
             )
             nll_baseline_total += nll_base.item()