# Copyright 2022 DeepMind Technologies Limited. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Basic encoder for inputs with a fixed vocabulary.""" import abc from typing import Any, List, Optional, Sequence from tracr.craft import bases class Encoder(abc.ABC): """Encodes a list of tokens into a list of inputs for a transformer model. The abstract class does not make assumptions on the input and output types, and we have different encoders for different input types. """ @abc.abstractmethod def encode(self, inputs: List[Any]) -> List[Any]: return list() @abc.abstractmethod def decode(self, encodings: List[Any]) -> List[Any]: return list() @property def pad_token(self) -> Optional[str]: return None @property def bos_token(self) -> Optional[str]: return None @property def pad_encoding(self) -> Optional[int]: return None @property def bos_encoding(self) -> Optional[int]: return None class NumericalEncoder(Encoder): """Encodes numerical variables (simply using the identity mapping).""" def encode(self, inputs: List[float]) -> List[float]: return inputs def decode(self, encodings: List[float]) -> List[float]: return encodings class CategoricalEncoder(Encoder): """Encodes categorical variables with a fixed vocabulary.""" def __init__( self, basis: Sequence[bases.BasisDirection], enforce_bos: bool = False, bos_token: Optional[str] = None, pad_token: Optional[str] = None, max_seq_len: Optional[int] = None, ): """Initialises. If enforce_bos is set, ensures inputs start with it.""" if enforce_bos and not bos_token: raise ValueError("BOS token must be specified if enforcing BOS.") self.encoding_map = {} for i, direction in enumerate(basis): val = direction.value self.encoding_map[val] = i if bos_token and bos_token not in self.encoding_map: raise ValueError("BOS token missing in encoding.") if pad_token and pad_token not in self.encoding_map: raise ValueError("PAD token missing in encoding.") self.enforce_bos = enforce_bos self._bos_token = bos_token self._pad_token = pad_token self._max_seq_len = max_seq_len def encode(self, inputs: List[bases.Value]) -> List[int]: if self.enforce_bos and inputs[0] != self.bos_token: raise ValueError("First input token must be BOS token. " f"Should be '{self.bos_token}', but was '{inputs[0]}'.") if missing := set(inputs) - set(self.encoding_map.keys()): raise ValueError(f"Inputs {missing} not found in encoding ", self.encoding_map.keys()) if self._max_seq_len is not None and len(inputs) > self._max_seq_len: raise ValueError(f"inputs={inputs} are longer than the maximum " f"sequence length {self._max_seq_len}") return [self.encoding_map[x] for x in inputs] def decode(self, encodings: List[int]) -> List[bases.Value]: """Recover the tokens that corresponds to `ids`. Inverse of __call__.""" decoding_map = {val: key for key, val in self.encoding_map.items()} if missing := set(encodings) - set(decoding_map.keys()): raise ValueError(f"Inputs {missing} not found in decoding map ", decoding_map.keys()) return [decoding_map[x] for x in encodings] @property def vocab_size(self) -> int: return len(self.encoding_map) @property def bos_token(self) -> Optional[str]: return self._bos_token @property def pad_token(self) -> Optional[str]: return self._pad_token @property def bos_encoding(self) -> Optional[int]: return None if self.bos_token is None else self.encoding_map[self.bos_token] @property def pad_encoding(self) -> Optional[int]: return None if self.pad_token is None else self.encoding_map[self.pad_token]