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from sre_parse import Tokenizer
from typing import ClassVar
import numpy as np
from scipy.fft import dct, idct
from tokenizers import Tokenizer, decoders, pre_tokenizers, processors
from tokenizers.models import BPE
from tokenizers.trainers import BpeTrainer
from transformers import PreTrainedTokenizerFast
from transformers.processing_utils import ProcessorMixin
class UniversalActionProcessor(ProcessorMixin):
attributes: ClassVar[list[str]] = ["bpe_tokenizer"]
bpe_tokenizer_class: str = "AutoTokenizer"
def __init__(
self,
bpe_tokenizer: PreTrainedTokenizerFast,
scale: float = 10,
vocab_size: int = 1024,
min_token: int = 0,
*,
action_dim: int | None = None,
time_horizon: int | None = None,
):
self.scale = scale
self.vocab_size = vocab_size
self.min_token = min_token
# Action horizon and dimension needed during decoding. These can be specified
# in three ways (in order of priority):
# 1. passed in as kwargs to decode()
# 2. in the constructor
# 3. cached from the last time decode() was called
self.time_horizon = time_horizon
self.action_dim = action_dim
self.called_time_horizon = time_horizon
self.called_action_dim = action_dim
super().__init__(bpe_tokenizer)
def __call__(self, action_chunk: np.array) -> np.array:
assert action_chunk.ndim <= 3, (
"Only 3 dimensions supported: [batch, timesteps, action_dim]"
)
if action_chunk.ndim == 2:
action_chunk = action_chunk[None, ...]
# Cache the time horizon and action dimension for decoding
self.called_time_horizon = action_chunk.shape[-2]
self.called_action_dim = action_chunk.shape[-1]
dct_coeff = dct(action_chunk, axis=1, norm="ortho")
dct_coeff = np.around(dct_coeff * self.scale)
tokens = []
for elem in dct_coeff:
token_str = "".join(
map(chr, np.maximum(elem.flatten() - self.min_token, 0).astype(int))
)
tokens.append(self.bpe_tokenizer(token_str)["input_ids"])
return tokens
def decode(
self,
tokens: list[list[int]],
*,
time_horizon: int | None = None,
action_dim: int | None = None,
) -> np.array:
self.time_horizon = (
time_horizon or self.time_horizon or self.called_time_horizon
)
self.action_dim = action_dim or self.action_dim or self.called_action_dim
# Cache the time horizon and action dimension for the next call
self.called_time_horizon = self.time_horizon
self.called_action_dim = self.action_dim
assert self.time_horizon is not None and self.action_dim is not None, (
"Tokenizer not initialized, call encode() once or pass in time_horizon and action_dim."
)
decoded_actions = []
for token in tokens:
try:
decoded_tokens = self.bpe_tokenizer.decode(token)
decoded_dct_coeff = (
np.array(list(map(ord, decoded_tokens))) + self.min_token
)
decoded_dct_coeff = decoded_dct_coeff.reshape(-1, self.action_dim)
assert decoded_dct_coeff.shape == (
self.time_horizon,
self.action_dim,
), (
f"Decoded DCT coefficients have shape {decoded_dct_coeff.shape}, expected ({self.time_horizon}, {self.action_dim})"
)
except Exception as e:
print(f"Error decoding tokens: {e}")
print(f"Tokens: {token}")
decoded_dct_coeff = np.zeros((self.time_horizon, self.action_dim))
decoded_actions.append(
idct(decoded_dct_coeff / self.scale, axis=0, norm="ortho")
)
return np.stack(decoded_actions)
@classmethod
def fit(
cls,
action_data: list[np.array],
scale: float = 10,
vocab_size: int = 1024,
*,
time_horizon: int | None = None,
action_dim: int | None = None,
) -> "UniversalActionProcessor":
# Run DCT over all inputs
dct_tokens = [dct(a, axis=0, norm="ortho").flatten() for a in action_data]
# Quantize and find min token
max_token = int(np.around(np.concatenate(dct_tokens) * scale).max())
min_token = int(np.around(np.concatenate(dct_tokens) * scale).min())
min_vocab_size = max_token - min_token
print(
f"Min token: {min_token}, Max token: {max_token}, Min vocab size: {min_vocab_size}"
)
assert min_vocab_size <= vocab_size, (
f"Vocab size {vocab_size} is too small for the range of tokens {min_vocab_size}"
)
if min_vocab_size + 100 > vocab_size:
logging.warning(
f"Initial alphabet size {min_vocab_size} is almost as large as the vocab"
f"size {vocab_size}, consider increasing vocab size"
)
# Make token iterator for BPE training
def _token_iter():
for tokens in dct_tokens:
rounded_tokens = np.around(tokens * scale) - min_token
rounded_tokens = rounded_tokens.astype(int)
string = "".join(map(chr, rounded_tokens))
yield string
# Train BPE tokenizer
tokenizer = Tokenizer(BPE())
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False, use_regex=False)
tokenizer.decoder = decoders.ByteLevel()
tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)
# Set up the entire range of possible tokens as the initial alphabet
alphabet = [chr(i) for i in range(max_token - min_token + 1)]
trainer = BpeTrainer(
vocab_size=vocab_size,
min_frequency=2,
show_progress=True,
special_tokens=[],
initial_alphabet=pre_tokenizers.ByteLevel.alphabet(),
# initial_alphabet=alphabet,
max_token_length=10000,
)
tokenizer.train_from_iterator(
_token_iter(), trainer=trainer, length=len(dct_tokens)
)
return cls(
PreTrainedTokenizerFast(
tokenizer_object=tokenizer, clean_up_tokenization_spaces=False
),
scale=scale,
vocab_size=vocab_size,
min_token=min_token,
time_horizon=time_horizon,
action_dim=action_dim,
)
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