fix issues: checkpoints keys mismatch and 'task' tokenisation in smolvla (#1256)

Co-authored-by: danaaubakirova <d.aubakirova@alumni.edu.kz>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Co-authored-by: Simon Alibert <simon.alibert@huggingface.co>

lerobot/common/policies/smolvla/modeling_smolvla.py

@@ -53,8 +53,11 @@ policy = SmolVLAPolicy.from_pretrained("lerobot/smolvla_base")
 """
 
 import math
+import os
+import re
 from collections import deque
 
+import safetensors
 import torch
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
@@ -73,6 +76,98 @@ from lerobot.common.policies.utils import (
 )
 from lerobot.common.utils.utils import get_safe_dtype
 
+# Matches ".soNNN", optionally followed by "-something", up to the "_buffer_" marker
+_VARIANT_RE = re.compile(r"\.so\d+(?:-[\w]+)?_buffer_")
+
+
+def canonicalise(k: str) -> str:
+    """
+    Remove dataset-variant markers like '.so100-blue_' or '.so100_' from a
+    normalisation-buffer key.
+    """
+    return _VARIANT_RE.sub(".buffer_", k)
+
+
+def standardise_state_dict(
+    checkpoint: dict[str, torch.Tensor], ref_keys: set[str], *, verbose: bool = True
+) -> tuple[dict[str, torch.Tensor], list[str]]:
+    """
+    • Re-keys `checkpoint ` so that every entry matches the *reference* key set.
+    • If several variant keys collapse to the same canonical name we keep the
+      first one and log the collision.
+    • Returns the new dict + a list of entries that could not be matched.
+    """
+    out, collisions, unmatched = {}, {}, []
+
+    for k, v in checkpoint.items():
+        canon = canonicalise(k)
+        if canon in ref_keys:
+            if canon in out:  # duplicate after collapsing
+                collisions.setdefault(canon, []).append(k)
+            else:
+                out[canon] = v
+        else:
+            unmatched.append(k)
+
+    if verbose:
+        for canon, variants in collisions.items():
+            print(f"[standardise_state_dict] '{canon}'  ←  {variants}")
+        if unmatched:
+            print(f"[standardise_state_dict] kept {len(unmatched)} unmatched keys")
+
+    out.update({k: checkpoint[k] for k in unmatched})
+    return out, unmatched
+
+
+def rename_checkpoint_keys(checkpoint: dict, rename_str: str):
+    """
+    Renames keys in a checkpoint dictionary based on the given rename string.
+
+    Args:
+        checkpoint (dict): The checkpoint dictionary.
+        rename_str (str): A string specifying key mappings in the format "old1//new1,old2//new2".
+
+    Returns:
+        dict: The modified checkpoint with renamed keys.
+    """
+
+    rename_dict = dict(pair.split("//") for pair in rename_str.split(","))
+
+    new_checkpoint = {}
+    for k, v in checkpoint.items():
+        for old_key, new_key in rename_dict.items():
+            if old_key in k:
+                k = k.replace(old_key, new_key)
+        new_checkpoint[k] = v
+    return new_checkpoint
+
+
+def load_smolvla(
+    model: torch.nn.Module,
+    filename: str | os.PathLike,
+    *,
+    device: str = "cpu",
+    checkpoint_keys_mapping: str = "",
+) -> torch.nn.Module:
+    state_dict = safetensors.torch.load_file(filename, device=device)
+
+    # Optional user-supplied renames (e.g. "model._orig_mod.//model.")
+    if checkpoint_keys_mapping and "//" in checkpoint_keys_mapping:
+        state_dict = rename_checkpoint_keys(state_dict, checkpoint_keys_mapping)
+
+    state_dict, _ = standardise_state_dict(state_dict, set(model.state_dict().keys()))
+
+    missing, unexpected = model.load_state_dict(state_dict)
+
+    if missing or unexpected:
+        raise RuntimeError(
+            "SmolVLA %d missing / %d unexpected keys",
+            len(missing),
+            len(unexpected),
+        )
+
+    return model
+
 
 def create_sinusoidal_pos_embedding(
     time: torch.tensor, dimension: int, min_period: float, max_period: float, device="cpu"
@@ -264,6 +359,23 @@ class SmolVLAPolicy(PreTrainedPolicy):
             ACTION: deque(maxlen=self.config.n_action_steps),
         }
 
+    # HACK(aliberts, danaaubakirova): we overwrite this classmethod here to fix smolVLA-specific issues
+    @classmethod
+    def _load_as_safetensor(
+        cls,
+        model: "SmolVLAPolicy",
+        model_file: str,
+        map_location: str,
+        strict: bool,
+    ):
+        safetensors.torch.load_model(model, model_file, strict=strict, device=map_location)
+        return load_smolvla(
+            model,
+            model_file,
+            device=map_location,
+            checkpoint_keys_mapping="model._orig_mod.//model.",
+        )
+
     def get_optim_params(self) -> dict:
         return self.parameters()
 
@@ -387,10 +499,14 @@ class SmolVLAPolicy(PreTrainedPolicy):
         """Tokenize the text input"""
         device = batch[OBS_STATE].device
         tasks = batch["task"]
+        if isinstance(tasks, str):
+            tasks = [tasks]
+
         if len(tasks) == 1:
             tasks = [tasks[0] for _ in range(batch[OBS_STATE].shape[0])]
 
         tasks = [task if task.endswith("\n") else f"{task}\n" for task in tasks]
+
         tokenized_prompt = self.language_tokenizer.__call__(
             tasks,
             padding=self.config.pad_language_to,

pyproject.toml

@@ -90,7 +90,7 @@ intelrealsense = [
     "pyrealsense2-macosx>=2.54 ; sys_platform == 'darwin'",
 ]
 pi0 = ["transformers>=4.48.0"]
-smolvla = ["transformers>=4.50.3", "num2words>=0.5.14", "accelerate>=1.7.0"]
+smolvla = ["transformers>=4.50.3", "num2words>=0.5.14", "accelerate>=1.7.0", "safetensors>=0.4.3"]
 pusht = ["gym-pusht>=0.1.5 ; python_version < '4.0'"]
 stretch = [
     "hello-robot-stretch-body>=0.7.27 ; python_version < '4.0' and sys_platform == 'linux'",