linting, moved to community folder

environments/community/openvla_robotics/action_tokenizer.py

@@ -0,0 +1,91 @@
+"""
+action_tokenizer.py
+
+Extension class; wraps base LLM/VLM tokenizer with logic to discretize and tokenize continuous robot actions.
+"""
+
+from typing import List, Union
+
+import numpy as np
+from transformers import PreTrainedTokenizerBase
+
+
+class ActionTokenizer:
+    def __init__(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        bins: int = 256,
+        min_action: int = -1,
+        max_action: int = 1,
+    ) -> None:
+        """
+        Discretizes continuous robot actions into N bins per dimension and maps to the least used tokens.
+
+        NOTE =>> by default, assumes a BPE-style tokenizer akin to the LlamaTokenizer, where *the least used tokens*
+                 appear at the end of the vocabulary!
+
+        :param tokenizer: Base LLM/VLM tokenizer to extend.
+        :param bins: Number of bins for each continuous value; we'll adopt a uniform binning strategy.
+        :param min_action: Minimum action value (for clipping, setting lower bound on bin interval).
+        :param max_action: Maximum action value (for clipping, setting upper bound on bin interval).
+        """
+        self.tokenizer, self.n_bins, self.min_action, self.max_action = (
+            tokenizer,
+            bins,
+            min_action,
+            max_action,
+        )
+
+        # Create Uniform Bins + Compute Bin Centers
+        self.bins = np.linspace(min_action, max_action, self.n_bins)
+        self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
+
+        # [Contract] Set "action_token_begin_idx" based on `self.tokenizer.vocab_size - (self.n_bins + 1)`
+        #   =>> Assumes we're always overwriting the final `n_bins` tokens of the vocabulary!
+        self.action_token_begin_idx: int = int(
+            self.tokenizer.vocab_size - (self.n_bins + 1)
+        )
+
+    def __call__(self, action: np.ndarray) -> Union[str, List[str]]:
+        """Clip & bin actions to *the last `n_bins` tokens* of the vocabulary (e.g., tokenizer.vocab[-256:])."""
+        action = np.clip(
+            action, a_min=float(self.min_action), a_max=float(self.max_action)
+        )
+        discretized_action = np.digitize(action, self.bins)
+
+        # Handle single element vs. batch
+        if len(discretized_action.shape) == 1:
+            return self.tokenizer.decode(
+                list(self.tokenizer.vocab_size - discretized_action)
+            )
+        else:
+            return self.tokenizer.batch_decode(
+                (self.tokenizer.vocab_size - discretized_action).tolist()
+            )
+
+    def decode_token_ids_to_actions(self, action_token_ids: np.ndarray) -> np.ndarray:
+        """
+        Returns continuous actions for discrete action token IDs.
+
+        NOTE =>> Because of the way the actions are discretized w.r.t. the bins (and not the bin centers), the
+                 digitization returns bin indices between [1, # bins], inclusive, when there are actually only
+                 (# bins - 1) bin intervals.
+
+                 Therefore, if the digitization returns the last possible index, we map this to the last bin interval.
+
+        EXAMPLE =>> Let's say self._bins has 256 values. Then self._bin_centers has 255 values. Digitization returns
+                    indices between [1, 256]. We subtract 1 from all indices so that they are between [0, 255]. There
+                    is still one index (i==255) that would cause an out-of-bounds error if used to index into
+                    self._bin_centers. Therefore, if i==255, we subtract 1 from it so that it just becomes the index of
+                    the last bin center. We implement this simply via clipping between [0, 255 - 1].
+        """
+        discretized_actions = self.tokenizer.vocab_size - action_token_ids
+        discretized_actions = np.clip(
+            discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1
+        )
+
+        return self.bin_centers[discretized_actions]
+
+    @property
+    def vocab_size(self) -> int:
+        return self.n_bins