Adding PrefixConstrainedLogitsProcessor

src/transformers/generation_logits_process.py

@@ -13,8 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import math
 from abc import ABC
-from typing import Iterable, List
+from typing import Callable, Iterable, List
 
 import numpy as np
 import torch
@@ -372,3 +373,30 @@ def _set_scores_to_inf_for_banned_tokens(self, scores: torch.Tensor, banned_toke
         )
         scores = scores.masked_fill(banned_mask, -float("inf"))
         return scores
+
+
+class PrefixConstrainedLogitsProcessor(LogitsProcessor):
+    r"""
+    :class:`transformers.LogitsProcessor` that enforces contrained generation and is useful for prefix-conditioned
+    constrained generation. See `Autoregressive Entity Retrieval <https://arxiv.org/abs/2010.00904>`__ for more
+    information.
+
+    Args:
+        prefix_allowed_tokens_fn: (:obj:`Callable[[int, torch.Tensor], List[int]]`):
+            This function constraints the beam search to allowed tokens only at each step. This function takes 2
+            arguments :obj:`inputs_ids` and the batch ID :obj:`batch_id`. It has to return a list with the allowed
+            tokens for the next generation step conditioned on the previously generated tokens :obj:`inputs_ids` and
+            the batch ID :obj:`batch_id`.
+    """
+
+    def __init__(self, prefix_allowed_tokens_fn: Callable[[int, torch.Tensor], List[int]], num_beams: int):
+        self._prefix_allowed_tokens_fn = prefix_allowed_tokens_fn
+        self._num_beams = num_beams
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        mask = torch.full_like(scores, -math.inf)
+        for batch_id, beam_sent in enumerate(input_ids.view(-1, self._num_beams, input_ids.shape[-1])):
+            for beam_id, sent in enumerate(beam_sent):
+                mask[batch_id * self._num_beams + beam_id, self._prefix_allowed_tokens_fn(batch_id, sent)] = 0
+
+        return scores + mask

src/transformers/generation_utils.py

@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict, Iterable, List, Optional, Tuple
+from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
 
 import torch
 from torch.nn import functional as F
@@ -26,6 +26,7 @@
     MinLengthLogitsProcessor,
     NoBadWordsLogitsProcessor,
     NoRepeatNGramLogitsProcessor,
+    PrefixConstrainedLogitsProcessor,
     RepetitionPenaltyLogitsProcessor,
     TemperatureLogitsWarper,
     TopKLogitsWarper,
@@ -258,6 +259,8 @@ def _get_logits_processor(
         bad_words_ids: List[List[int]],
         min_length: int,
         eos_token_id: int,
+        prefix_allowed_tokens_fn: Callable[[int, torch.Tensor], List[int]],
+        num_beams: int,
     ) -> LogitsProcessorList:
         """
         This class returns a :obj:`~transformers.LogitsProcessorList` list object that contains all relevant
@@ -285,6 +288,8 @@ def _get_logits_processor(
             processors.append(NoBadWordsLogitsProcessor(bad_words_ids, eos_token_id))
         if min_length is not None and eos_token_id is not None and min_length > -1:
             processors.append(MinLengthLogitsProcessor(min_length, eos_token_id))
+        if prefix_allowed_tokens_fn is not None:
+            processors.append(PrefixConstrainedLogitsProcessor(prefix_allowed_tokens_fn, num_beams))
         return processors
 
     @torch.no_grad()
@@ -309,6 +314,7 @@ def generate(
         num_return_sequences: Optional[int] = None,
         decoder_start_token_id: Optional[int] = None,
         use_cache: Optional[bool] = None,
+        prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
         **model_kwargs
     ) -> torch.LongTensor:
         r"""
@@ -375,6 +381,13 @@ def generate(
             use_cache: (:obj:`bool`, `optional`, defaults to :obj:`True`):
                 Whether or not the model should use the past last key/values attentions (if applicable to the model) to
                 speed up decoding.
+            prefix_allowed_tokens_fn: (:obj:`Callable[[int, torch.Tensor], List[int]]`, `optional`):
+                If provided, this function constraints the beam search to allowed tokens only at each step. If not
+                provided no constraint is applied. This function takes 2 arguments :obj:`inputs_ids` and the batch ID
+                :obj:`batch_id`. It has to return a list with the allowed tokens for the next generation step
+                conditioned on the previously generated tokens :obj:`inputs_ids` and the batch ID :obj:`batch_id`. This
+                argument is useful for constrained generation conditioned on the prefix, as described in
+                `Autoregressive Entity Retrieval <https://arxiv.org/abs/2010.00904>`__.
             model_kwargs:
                 Additional model specific kwargs will be forwarded to the :obj:`forward` function of the model. If the
                 model is an Encoder-Decoder model, encoder specific kwargs should not be prefixed and decoder specific
@@ -494,6 +507,8 @@ def generate(
             bad_words_ids=bad_words_ids,
             min_length=min_length,
             eos_token_id=eos_token_id,
+            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+            num_beams=num_beams,
         )
 
         if is_greedy_gen_mode:

src/transformers/models/rag/modeling_rag.py

@@ -15,7 +15,7 @@
 """RAG model implementation."""
 
 from dataclasses import dataclass
-from typing import List, Optional, Tuple
+from typing import Callable, List, Optional, Tuple
 
 import torch
 
@@ -1229,6 +1229,7 @@ def generate(
         num_return_sequences=None,
         decoder_start_token_id=None,
         n_docs=None,
+        prefix_allowed_tokens_fn: Callable[[int, torch.Tensor], List[int]] = None,
         **model_kwargs
     ):
         """
@@ -1302,6 +1303,13 @@ def generate(
                 If an encoder-decoder model starts decoding with a different token than `bos`, the id of that token.
             n_docs (:obj:`int`, `optional`, defaults to :obj:`config.n_docs`)
                 Number of documents to retrieve and/or number of documents for which to generate an answer.
+            prefix_allowed_tokens_fn: (:obj:`Callable[[int, torch.Tensor], List[int]]`, `optional`):
+                If provided, this function constraints the beam search to allowed tokens only at each step. If not
+                provided no constraint is applied. This function takes 2 arguments :obj:`inputs_ids` and the batch ID
+                :obj:`batch_id`. It has to return a list with the allowed tokens for the next generation step
+                conditioned on the previously generated tokens :obj:`inputs_ids` and the batch ID :obj:`batch_id`. This
+                argument is useful for constrained generation conditioned on the prefix, as described in
+                `Autoregressive Entity Retrieval <https://arxiv.org/abs/2010.00904>`__.
 
         Return:
             :obj:`torch.LongTensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`: The generated
@@ -1395,6 +1403,8 @@ def extend_enc_output(tensor, num_beams=None):
             bad_words_ids=bad_words_ids,
             min_length=min_length,
             eos_token_id=eos_token_id,
+            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+            num_beams=num_beams,
         )
 
         if num_beams == 1:

tests/test_generation_logits_process.py

@@ -31,6 +31,7 @@
         MinLengthLogitsProcessor,
         NoBadWordsLogitsProcessor,
         NoRepeatNGramLogitsProcessor,
+        PrefixConstrainedLogitsProcessor,
         RepetitionPenaltyLogitsProcessor,
         TemperatureLogitsWarper,
         TopKLogitsWarper,
@@ -281,3 +282,23 @@ def test_processor_list(self):
 
         # input_ids should never be changed
         self.assertListEqual(input_ids.tolist(), input_ids_comp.tolist())
+
+    def test_prefix_constrained_logits_processor(self):
+        vocab_size = 5
+        batch_size = 2
+
+        input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long)
+        scores = self._get_uniform_logits(batch_size, vocab_size)
+
+        def prefix_allowed_tokens_fn(batch_id, inputs_ids):
+            return [[0, 1], [2, 3]][batch_id]
+
+        prefix_constrained_logits_proc = PrefixConstrainedLogitsProcessor(prefix_allowed_tokens_fn, 1)
+
+        filtered_scores = prefix_constrained_logits_proc(input_ids, scores.clone())
+
+        # batch 1: 1st, 2nd (0, 1) token are allowed
+        # batch 2: 3rd, 4th (2, 3) token are allowed
+        self.assertListEqual(
+            torch.isinf(filtered_scores).tolist(), [[False, False, True, True, True], [True, True, False, False, True]]
+        )