fix(smolvla2): drop pixel_values from select_message generate path

SmolVLA's image preprocessor sizes frames to whatever the action
expert was trained on, but SmolVLM's standard vision tower expects
its own default tile grid (e.g. 384/14 → 27×27 patches). The
mismatch surfaces deep in the post-vision reshape as
``RuntimeError: shape '[2, 34, 34, 768]' is invalid for input of
size 1843200`` — the model has 1200 patches but expects 34×34=1156.

Drop ``pixel_values`` from ``vlm.generate(...)`` so SmolVLM runs as
a text-only LM at REPL time. The high-level branches (subtask /
plan / memory) are dominated by their text context anyway, so this
is acceptable for dry-run inference. VQA loses its image grounding
— that will be marked as expected for the dry-run path until a
follow-up either re-processes images through SmolVLM's own
``ImageProcessor`` to match its tile grid, or gives
``vlm_with_expert`` a real AR text decode mode that handles state
and image embeddings the way training does.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/lerobot/policies/smolvla2/modeling_smolvla2.py

@@ -324,23 +324,23 @@ class SmolVLA2Policy(SmolVLAPolicy):
         lang_tokens = batch[OBS_LANGUAGE_TOKENS]
         lang_masks = batch[OBS_LANGUAGE_ATTENTION_MASK]
 
-        # Collect any image features the runtime merged in. SmolVLM
-        # expects ``pixel_values`` shape ``[B, num_images, C, H, W]``;
-        # individual ``observation.images.*`` features are typically
-        # ``[B, C, H, W]`` after the preprocessor, so stack them on a
-        # new ``num_images`` axis.
-        image_tensors: list[Tensor] = []
-        for k, v in batch.items():
-            if (
-                isinstance(k, str)
-                and k.startswith("observation.images.")
-                and isinstance(v, Tensor)
-            ):
-                image_tensors.append(v if v.ndim == 4 else v.unsqueeze(0))
-        pixel_values = (
-            torch.stack(image_tensors, dim=1) if image_tensors else None
-        )
-
+        # NOTE: we deliberately do *not* forward ``pixel_values`` to
+        # ``vlm.generate``. The dataset's images go through SmolVLA's
+        # custom preprocessor (resize / normalise to whatever shape
+        # the action expert was trained on), but SmolVLM's standard
+        # vision tower expects images sized to its own default tile
+        # grid (e.g. 384/14 → 27×27 patches). The mismatch surfaces
+        # as ``RuntimeError: shape '[2, 34, 34, 768]' is invalid for
+        # input of size <other>`` deep in the post-vision reshape.
+        #
+        # For the dry-run REPL the high-level branches (subtask /
+        # plan / memory) are dominated by their text context anyway,
+        # so running text-only generation through SmolVLM is good
+        # enough. Restoring full vision conditioning at inference
+        # would mean either re-processing the images through the
+        # backbone's own ``ImageProcessor`` (and matching SmolVLA2
+        # training shape) or giving ``vlm_with_expert`` a real AR
+        # text decode mode — both are bigger follow-ups.
         gen_kwargs: dict[str, Any] = {
             "input_ids": lang_tokens,
             "attention_mask": lang_masks.long() if lang_masks.dtype == torch.bool else lang_masks,
@@ -353,8 +353,6 @@ class SmolVLA2Policy(SmolVLAPolicy):
             gen_kwargs["top_p"] = top_p
         if eos_token_id is not None:
             gen_kwargs["eos_token_id"] = eos_token_id
-        if pixel_values is not None:
-            gen_kwargs["pixel_values"] = pixel_values
 
         gen_ids = vlm.generate(**gen_kwargs)
         # ``vlm.generate`` returns the prompt + new tokens; slice off