[Frontend] Bad words sampling parameter

tests/samplers/test_no_bad_words.py

@@ -0,0 +1,185 @@
+"""Make sure bad_words works.
+
+Run `pytest tests/samplers/test_no_bad_words.py`.
+
+"""
+from typing import List, Optional
+
+from transformers import AutoTokenizer
+
+from vllm import LLM, SamplingParams
+
+
+def _generate(
+    model: LLM,
+    prompt: str,
+    num_prompt_tokens: int,
+    temperature: float = 0,
+    bad_words: Optional[List[str]] = None,
+) -> List[int]:
+    sampling_params = SamplingParams(
+        temperature=temperature,
+        bad_words=bad_words,
+    )
+
+    # [([output_token_ids, ], [output_text, ]), ]
+    output = model.generate([prompt], sampling_params=sampling_params)
+
+    output_token_ids = output[0][0][0][num_prompt_tokens:]
+    # [0] first (and only) request output
+    # [0] token_ids (not text)
+    # [0] first (and only) output completion
+
+    return output_token_ids
+
+
+class TestOneTokenBadWord:
+    MODEL = "TheBloke/Llama-2-7B-fp16"
+
+    PROMPT = "Hi! How are"
+    TARGET_TOKEN = "you"
+
+    def setup_method(self, method):
+        self.tokenizer = AutoTokenizer.from_pretrained(self.MODEL,
+                                                       add_prefix_space=True)
+
+        self.num_prompt_tokens = len(self._encode(self.PROMPT))
+        self.target_token_id = self._encode(self.TARGET_TOKEN,
+                                            add_special_tokens=False)[0]
+
+    def test_one_token_bad_word(self, vllm_runner):
+        with vllm_runner(self.MODEL) as llm:
+            output_token_ids = self._generate(llm)
+            assert output_token_ids[0] == self.target_token_id
+
+            output_token_ids = self._generate(llm,
+                                              bad_words=[self.TARGET_TOKEN])
+            assert self.target_token_id not in output_token_ids
+
+    def _generate(self,
+                  model: LLM,
+                  bad_words: Optional[List[str]] = None) -> List[int]:
+        return _generate(
+            model=model,
+            prompt=self.PROMPT,
+            num_prompt_tokens=self.num_prompt_tokens,
+            bad_words=bad_words,
+        )
+
+    def _encode(self,
+                prompt: str,
+                add_special_tokens: bool = True) -> List[int]:
+        return self.tokenizer(prompt,
+                              add_special_tokens=add_special_tokens).input_ids
+
+
+class TestTwoTokenBadWord:
+    # Another model (with a different tokenizer behaviour)
+    MODEL = "openai-community/gpt2"
+
+    PROMPT = "How old are you? I am 10"
+    TARGET_TOKEN1 = "years"
+    TARGET_TOKEN2 = "old"
+    NEIGHBOUR_TOKEN2 = "older"
+
+    def setup_method(self, method):
+        self.tokenizer = AutoTokenizer.from_pretrained(self.MODEL,
+                                                       add_prefix_space=True)
+
+        self.num_prompt_tokens = len(self._encode(self.PROMPT))
+        self.target_token_id1 = self._encode(self.TARGET_TOKEN1,
+                                             add_special_tokens=False)[0]
+        self.target_token_id2 = self._encode(self.TARGET_TOKEN2,
+                                             add_special_tokens=False)[0]
+        self.neighbour_token_id2 = self._encode(self.NEIGHBOUR_TOKEN2,
+                                                add_special_tokens=False)[0]
+
+    def test_two_token_bad_word(self, vllm_runner):
+        with vllm_runner(self.MODEL) as llm:
+            output_token_ids = self._generate(llm)
+            assert output_token_ids[:2] == [
+                self.target_token_id1, self.target_token_id2
+            ]
+
+            output_token_ids = self._generate(llm,
+                                              bad_words=[self.TARGET_TOKEN1])
+            assert self.target_token_id1 not in output_token_ids
+
+            output_token_ids = self._generate(llm,
+                                              bad_words=[self.TARGET_TOKEN2])
+            assert output_token_ids[0] == self.target_token_id1
+            assert self.target_token_id2 not in output_token_ids
+
+            output_token_ids = self._generate(
+                llm, bad_words=[f'{self.TARGET_TOKEN1} {self.TARGET_TOKEN2}'])
+            assert output_token_ids[0] == self.target_token_id1
+            assert output_token_ids[:2] != [
+                self.target_token_id1, self.target_token_id2
+            ]
+            assert not self._contains(
+                output_token_ids,
+                [self.target_token_id1, self.target_token_id2])
+            # Model dependent behaviour
+            assert output_token_ids[:2] == [
+                self.target_token_id1, self.neighbour_token_id2
+            ]
+
+            output_token_ids = self._generate(
+                llm,
+                bad_words=[
+                    f'{self.TARGET_TOKEN1} {self.TARGET_TOKEN2}',
+                    f'{self.TARGET_TOKEN1} {self.NEIGHBOUR_TOKEN2}'
+                ])
+            assert output_token_ids[0] == self.target_token_id1
+            assert output_token_ids[:2] != [
+                self.target_token_id1, self.target_token_id2
+            ]
+            assert not self._contains(
+                output_token_ids,
+                [self.target_token_id1, self.target_token_id2])
+            assert output_token_ids[:2] != [
+                self.target_token_id1, self.neighbour_token_id2
+            ]
+            assert not self._contains(
+                output_token_ids,
+                [self.target_token_id1, self.neighbour_token_id2])
+            assert ((self.target_token_id2 in output_token_ids)
+                    or (self.neighbour_token_id2 in output_token_ids))
+
+    def _generate(self,
+                  model: LLM,
+                  bad_words: Optional[List[str]] = None) -> List[int]:
+        return _generate(
+            model=model,
+            prompt=self.PROMPT,
+            num_prompt_tokens=self.num_prompt_tokens,
+            bad_words=bad_words,
+        )
+
+    @staticmethod
+    def _contains(sequence: List[int], subsequence: List[int]) -> bool:
+        searched = False
+
+        for start in range(len(sequence)):
+            end = start + len(subsequence)
+            current_subsequence = sequence[start:end]
+
+            if len(current_subsequence) < len(subsequence):
+                continue
+
+            searched = True
+
+            assert len(current_subsequence) == len(subsequence)
+
+            if current_subsequence == subsequence:
+                return True
+
+        assert searched, "All subsequences did not match in length..."
+
+        return False
+
+    def _encode(self,
+                prompt: str,
+                add_special_tokens: bool = True) -> List[int]:
+        return self.tokenizer(prompt,
+                              add_special_tokens=add_special_tokens).input_ids

vllm/engine/llm_engine.py

@@ -26,14 +26,16 @@
     SequenceGroupOutputProcessor)
 from vllm.engine.output_processor.stop_checker import StopChecker
 from vllm.engine.output_processor.util import create_output_by_sequence_group
-from vllm.entrypoints.openai.logits_processors import get_logits_processors
+from vllm.entrypoints.openai.logits_processors import (
+    get_logits_processors as get_openai_logits_processors)
 from vllm.executor.executor_base import ExecutorBase
 from vllm.executor.gpu_executor import GPUExecutor
 from vllm.executor.ray_utils import initialize_ray_cluster
 from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs,
                          EncoderDecoderInputs, InputRegistry, PromptType)
 from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
+from vllm.logits_process import get_bad_words_logits_processors
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.guided_decoding import (
     get_local_guided_decoding_logits_processor)
@@ -1963,6 +1965,7 @@ def _build_logits_processors(
         logits_processors field. Returns the modified sampling params."""
 
         logits_processors = []
+
         if (guided_decoding := sampling_params.guided_decoding) is not None:
 
             logger.debug(
@@ -1984,7 +1987,7 @@ def _build_logits_processors(
         if (sampling_params.logit_bias or sampling_params.allowed_token_ids):
             tokenizer = self.get_tokenizer(lora_request=lora_request)
 
-            processors = get_logits_processors(
+            processors = get_openai_logits_processors(
                 logit_bias=sampling_params.logit_bias,
                 allowed_token_ids=sampling_params.allowed_token_ids,
                 tokenizer=tokenizer)
@@ -1994,6 +1997,12 @@ def _build_logits_processors(
             sampling_params.logit_bias = None
             sampling_params.allowed_token_ids = None
 
+        if len(sampling_params.bad_words) > 0:
+            tokenizer = self.get_tokenizer(lora_request)
+            processors = get_bad_words_logits_processors(
+                bad_words=sampling_params.bad_words, tokenizer=tokenizer)
+            logits_processors.extend(processors)
+
         if logits_processors:
             if sampling_params.logits_processors is None:
                 sampling_params.logits_processors = logits_processors

vllm/logits_process.py

@@ -0,0 +1,119 @@
+from typing import Callable, List, Tuple, Union
+
+import torch
+
+from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+
+LogitsProcessor = Union[Callable[[List[int], torch.Tensor], torch.Tensor],
+                        Callable[[List[int], List[int], torch.Tensor],
+                                 torch.Tensor]]
+"""LogitsProcessor is a function that takes a list
+of previously generated tokens, the logits tensor
+for the next token and, optionally, prompt tokens as a
+first argument, and returns a modified tensor of logits
+to sample from."""
+
+
+def get_bad_words_logits_processors(
+        bad_words: List[str],
+        tokenizer: AnyTokenizer) -> List[LogitsProcessor]:
+    bad_words_ids: List[List[int]] = list()
+
+    for bad_word in bad_words:
+        # To prohibit words both at the beginning
+        # and in the middle of text
+        # (related to add_prefix_space tokenizer parameter)
+        for add_prefix_space in [False, True]:
+            prefix = " " if add_prefix_space else ""
+            prompt = prefix + bad_word.lstrip()
+
+            if isinstance(tokenizer, MistralTokenizer):
+                # Mistral tokenizers should not add special tokens
+                prompt_token_ids = tokenizer.encode(prompt=prompt)
+            else:
+                prompt_token_ids = tokenizer.encode(text=prompt,
+                                                    add_special_tokens=False)
+
+            # If no space at the beginning
+            # or if prefix space produces a new word token
+            if (not add_prefix_space) or (
+                    add_prefix_space
+                    and prompt_token_ids[0] != bad_words_ids[-1][0]
+                    and len(prompt_token_ids) == len(bad_words_ids[-1])):
+                bad_words_ids.append(prompt_token_ids)
+
+    return [NoBadWordsLogitsProcessor(bad_words_ids=bad_words_ids)]
+
+
+class NoBadWordsLogitsProcessor:
+    _SMALLEST_LOGIT = float("-inf")
+    _NEUTRAL_LOGIT = 0.0
+
+    def __init__(self, bad_words_ids: List[List[int]]):
+        self.bad_words_ids = bad_words_ids
+        self.word_bias: torch.FloatTensor = None
+
+    def __call__(
+        self,
+        past_tokens_ids: Union[List[int], Tuple[int]],
+        logits: torch.FloatTensor,
+    ) -> torch.Tensor:
+        if self.word_bias is None:
+            self._init_word_bias(logits=logits)
+
+        last_token_bias = torch.zeros_like(logits)
+
+        for bad_word_ids in self.bad_words_ids:
+            if len(bad_word_ids) == 1:  # 1-token words already processed
+                continue
+
+            if len(bad_word_ids) > len(past_tokens_ids) + 1:
+                continue
+
+            prefix_length = len(bad_word_ids) - 1
+            last_token_id = bad_word_ids[-1]
+            actual_prefix = past_tokens_ids[-prefix_length:]
+            expected_prefix = bad_word_ids[:prefix_length]
+
+            assert len(actual_prefix) == len(expected_prefix)
+
+            is_match = tuple(actual_prefix) == tuple(expected_prefix)
+            last_token_bias[last_token_id] += (self._SMALLEST_LOGIT if is_match
+                                               else self._NEUTRAL_LOGIT)
+
+        logits = logits + self.word_bias + last_token_bias
+
+        return logits
+
+    def _init_word_bias(self, logits: torch.FloatTensor) -> None:
+        # Code based on NoBadWordsLogitsProcessor and SequenceBiasLogitsProcessor  # noqa: E501
+        # from https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py
+
+        vocab_size = logits.shape[-1]
+
+        self._check_token_ids_bounds(vocab_size=vocab_size)
+
+        self.word_bias = torch.zeros((vocab_size, ),
+                                     dtype=torch.float,
+                                     device=logits.device)
+
+        for bad_word_ids in self.bad_words_ids:
+            if len(bad_word_ids) == 1:
+                bad_word_id = bad_word_ids[-1]
+                self.word_bias[bad_word_id] = self._SMALLEST_LOGIT
+
+    def _check_token_ids_bounds(self, vocab_size: int) -> None:
+        invalid_token_ids = []
+
+        for bad_word_ids in self.bad_words_ids:
+            for token_id in bad_word_ids:
+                if token_id < 0 or token_id >= vocab_size:
+                    invalid_token_ids.append(token_id)
+
+        if len(invalid_token_ids) > 0:
+            raise ValueError(
+                f"The model vocabulary size is {vocab_size},"
+                f" but the following tokens"
+                f" were specified as bad: {invalid_token_ids}."
+                f" All token id values should be integers satisfying:"
+                f" 0 <= token_id < {vocab_size}.")

vllm/model_executor/guided_decoding/__init__.py

@@ -1,6 +1,7 @@
 from typing import Optional
 
-from vllm.sampling_params import GuidedDecodingParams, LogitsProcessor
+from vllm.logits_process import LogitsProcessor
+from vllm.sampling_params import GuidedDecodingParams
 
 
 async def get_guided_decoding_logits_processor(

vllm/model_executor/guided_decoding/lm_format_enforcer_decoding.py

@@ -9,7 +9,8 @@
     build_vllm_logits_processor, build_vllm_token_enforcer_tokenizer_data)
 from transformers import PreTrainedTokenizerBase
 
-from vllm.sampling_params import GuidedDecodingParams, LogitsProcessor
+from vllm.logits_process import LogitsProcessor
+from vllm.sampling_params import GuidedDecodingParams
 
 
 def get_local_lm_format_enforcer_guided_decoding_logits_processor(