| """Explicit action-token constraints for Hugging Face generate().""" |
|
|
| from __future__ import annotations |
|
|
| import torch |
| from transformers import LogitsProcessor |
|
|
| from ...backends.actioncodec2.cache import BoundedCache |
|
|
|
|
| class ActionCodec2LogitsProcessor(LogitsProcessor): |
| 'Constrain generated action IDs, then allow EOS only at complete coverage.' |
|
|
| def __init__( |
| self, codec, horizon, *, fps, prompt_length, eos_token_id, token_offset=0, pad_token_id=None |
| ): |
| for name, value in { |
| "prompt_length": prompt_length, |
| "eos_token_id": eos_token_id, |
| "token_offset": token_offset, |
| }.items(): |
| if type(value) is not int or value < 0: |
| raise ValueError(f"{name} must be a nonnegative integer") |
| if pad_token_id is not None and (type(pad_token_id) is not int or pad_token_id < 0): |
| raise ValueError("pad_token_id must be a nonnegative integer") |
| self.grammar = codec.grammar(horizon, fps=fps) |
| self.prompt_length = prompt_length |
| self.eos_token_id = eos_token_id |
| self.pad_token_id = pad_token_id |
| self.token_offset = token_offset |
| stop = token_offset + self.grammar.vocab_size |
| if token_offset <= eos_token_id < stop: |
| raise ValueError("eos_token_id must be outside the action token interval") |
| if pad_token_id is not None and token_offset <= pad_token_id < stop: |
| raise ValueError("pad_token_id must be outside the action token interval") |
| self._mask_cache = BoundedCache(64) |
|
|
| def _state(self, row): |
| if self.eos_token_id in row: |
| end = row.index(self.eos_token_id) |
| if any(t not in (self.eos_token_id, self.pad_token_id) for t in row[end:]): |
| raise ValueError("only EOS/padding may follow completed action generation") |
| tokens = [t - self.token_offset for t in row[:end]] |
| if not self.grammar.is_complete(tokens): |
| raise ValueError("EOS occurred before action coverage was complete") |
| else: |
| tokens = [t - self.token_offset for t in row] |
| state = self.grammar._consume(tokens) |
| if state is None: |
| raise ValueError( |
| "generated prefix violates the action grammar; check prompt_length and token_offset" |
| ) |
| return state |
|
|
| def _mask(self, state, scores): |
| key = (state, str(scores.device), scores.shape[-1]) |
| mask = self._mask_cache.get(key) |
| if mask is None: |
| mask = torch.zeros(scores.shape[-1], device=scores.device, dtype=torch.bool) |
| index, states = state |
| if index == len(self.grammar.segments): |
| mask[self.eos_token_id] = True |
| else: |
| segment = self.grammar.segments[index] |
| allowed = self.grammar._grammars[index]._next_token_mask_from_state(states[index]) |
| start = self.token_offset + segment.profile.spec.token_offset |
| mask[start : start + len(allowed)] = torch.as_tensor(allowed, device=scores.device) |
| self._mask_cache[key] = mask |
| return mask |
|
|
| def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: |
| if input_ids.ndim != 2 or scores.ndim != 2 or input_ids.shape[0] != scores.shape[0]: |
| raise ValueError("expected input_ids(B,L) and scores(B,V) with the same batch size") |
| if input_ids.shape[1] < self.prompt_length: |
| raise ValueError("prompt_length exceeds the supplied input length") |
| required = max( |
| self.token_offset + self.grammar.vocab_size, |
| self.eos_token_id + 1, |
| 0 if self.pad_token_id is None else self.pad_token_id + 1, |
| ) |
| if scores.shape[1] < required: |
| raise ValueError("model vocabulary does not cover configured action/EOS/padding IDs") |
| self.grammar.reserve_states(int(input_ids.shape[0])) |
| rows = input_ids[:, self.prompt_length :].detach().cpu().tolist() |
| output = scores.clone() |
| groups = {} |
| for index, row in enumerate(rows): |
| groups.setdefault(self._state(row), []).append(index) |
| for state, indices in groups.items(): |
| output[indices] = scores[indices].masked_fill(~self._mask(state, scores), -torch.inf) |
| if not torch.isfinite(output).any(dim=1).all(): |
| raise ValueError( |
| "all legal action logits were suppressed; check other generation processors" |
| ) |
| return output |
|
|
