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