[Bugfix] Fix guided decoding with tokenizer mode mistral (#11046)

.buildkite/test-pipeline.yaml

@@ -224,8 +224,12 @@ steps:
   mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/model_executor/layers
+  - vllm/model_executor/guided_decoding
   - tests/test_logits_processor
-  command: pytest -v -s test_logits_processor.py
+  - tests/model_executor/test_guided_processors
+  commands: 
+    - pytest -v -s test_logits_processor.py
+    - pytest -v -s model_executor/test_guided_processors.py
 
 - label: Speculative decoding tests # 30min
   source_file_dependencies:

requirements-common.txt

@@ -14,12 +14,13 @@ aiohttp
 openai >= 1.45.0 # Ensure modern openai package (ensure types module present and max_completion_tokens field support)
 uvicorn[standard]
 pydantic >= 2.9  # Required for fastapi >= 0.113.0
-pillow  # Required for image processing
 prometheus_client >= 0.18.0
+pillow  # Required for image processing
 prometheus-fastapi-instrumentator >= 7.0.0
 tiktoken >= 0.6.0  # Required for DBRX tokenizer
 lm-format-enforcer >= 0.10.9, < 0.11
 outlines == 0.1.11
+lark == 1.2.2 
 xgrammar >= 0.1.6; platform_machine == "x86_64"
 typing_extensions >= 4.10
 filelock >= 3.16.1 # need to contain https://github.com/tox-dev/filelock/pull/317

tests/model_executor/test_guided_processors.py

@@ -1,13 +1,19 @@
+import pickle
+
 import pytest
 import torch
 from transformers import AutoTokenizer
 
+from vllm.config import ModelConfig
 from vllm.model_executor.guided_decoding import (
-    get_guided_decoding_logits_processor)
+    get_guided_decoding_logits_processor,
+    get_local_guided_decoding_logits_processor)
 from vllm.model_executor.guided_decoding.outlines_logits_processors import (
     JSONLogitsProcessor, RegexLogitsProcessor)
 from vllm.sampling_params import GuidedDecodingParams
 
+MODEL_NAME = 'HuggingFaceH4/zephyr-7b-beta'
+
 
 def test_guided_logits_processors(sample_regex, sample_json_schema):
     """Basic unit test for RegexLogitsProcessor and JSONLogitsProcessor."""
@@ -38,14 +44,29 @@ def test_guided_logits_processors(sample_regex, sample_json_schema):
 @pytest.mark.asyncio
 @pytest.mark.parametrize("backend",
                          ["outlines", "lm-format-enforcer", "xgrammar"])
-async def test_guided_logits_processor_black_box(backend: str, sample_regex,
+@pytest.mark.parametrize("is_local", [True, False])
+async def test_guided_logits_processor_black_box(backend: str, is_local: bool,
+                                                 sample_regex,
                                                  sample_json_schema):
-    tokenizer = AutoTokenizer.from_pretrained('HuggingFaceH4/zephyr-7b-beta')
+
+    config = ModelConfig(
+        MODEL_NAME,
+        task="generate",
+        tokenizer=MODEL_NAME,
+        tokenizer_mode="auto",
+        trust_remote_code=False,
+        seed=0,
+        dtype="bfloat16",
+    )
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
     token_ids = tokenizer.encode(
         f"Give an example IPv4 address with this regex: {sample_regex}")
     regex_request = GuidedDecodingParams(regex=sample_regex, backend=backend)
-    regex_lp = await get_guided_decoding_logits_processor(
-        regex_request, tokenizer)
+
+    regex_lp = get_local_guided_decoding_logits_processor(
+            regex_request, tokenizer, config) if is_local else \
+            await get_guided_decoding_logits_processor(
+                    regex_request, tokenizer, config)
     assert regex_lp is not None
     tensor = torch.rand(32000)
     original_tensor = torch.clone(tensor)
@@ -59,7 +80,7 @@ async def test_guided_logits_processor_black_box(backend: str, sample_regex,
     json_request = GuidedDecodingParams(json=sample_json_schema,
                                         backend=backend)
     json_lp = await get_guided_decoding_logits_processor(
-        json_request, tokenizer)
+        json_request, tokenizer, config)
     assert json_lp is not None
     tensor = torch.rand(32000)
     original_tensor = torch.clone(tensor)
@@ -84,3 +105,24 @@ def test_multiple_guided_options_not_allowed(sample_json_schema, sample_regex):
     with pytest.raises(ValueError,
                        match="You can only use one kind of guided"):
         GuidedDecodingParams(json=sample_json_schema, grammar="test grammar")
+
+
+def test_pickle_xgrammar_tokenizer_data():
+
+    # TODO: move to another test file for xgrammar
+    try:
+        import xgrammar as xgr
+    except ImportError:
+        pytest.skip("Could not import xgrammar to run test")
+
+    from vllm.model_executor.guided_decoding.xgrammar_decoding import (
+        TokenizerData)
+    tokenizer_data = TokenizerData(vocab_type=xgr.VocabType.RAW)
+    pickled = pickle.dumps(tokenizer_data)
+
+    assert pickled is not None
+
+    depickled: TokenizerData = pickle.loads(pickled)
+
+    assert depickled is not None
+    assert depickled.vocab_type == xgr.VocabType.RAW

tests/models/decoder_only/language/test_mistral.py

@@ -3,17 +3,20 @@
 Run `pytest tests/models/test_mistral.py`.
 """
 import copy
+import json
 
+import jsonschema
+import jsonschema.exceptions
 import pytest
 
-from vllm import SamplingParams
 from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import (  # noqa
     MistralToolParser)
+from vllm.sampling_params import GuidedDecodingParams, SamplingParams
 
 from ...utils import check_logprobs_close
 
 MODELS = [
-    "mistralai/Mistral-7B-Instruct-v0.1",
+    "mistralai/Mistral-7B-Instruct-v0.3",
 ]
 
 MISTRAL_FORMAT_MODELS = [
@@ -126,6 +129,45 @@ MSGS = [
     }
 ]
 
+SAMPLE_JSON_SCHEMA = {
+    "type": "object",
+    "properties": {
+        "name": {
+            "type": "string"
+        },
+        "age": {
+            "type": "integer"
+        },
+        "skills": {
+            "type": "array",
+            "items": {
+                "type": "string",
+                "maxLength": 10
+            },
+            "minItems": 3
+        },
+        "work_history": {
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "company": {
+                        "type": "string"
+                    },
+                    "duration": {
+                        "type": "number"
+                    },
+                    "position": {
+                        "type": "string"
+                    }
+                },
+                "required": ["company", "position"]
+            }
+        }
+    },
+    "required": ["name", "age", "skills", "work_history"]
+}
+
 
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["bfloat16"])
@@ -251,3 +293,43 @@ def test_mistral_function_calling(
         assert parsed_message.tool_calls[
             0].function.arguments == '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}'  # noqa
         assert parsed_message.content is None
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("guided_backend",
+                         ["outlines", "lm-format-enforcer", "xgrammar"])
+def test_mistral_guided_decoding(
+    vllm_runner,
+    model: str,
+    guided_backend: str,
+) -> None:
+    with vllm_runner(model, dtype='bfloat16',
+                     tokenizer_mode="mistral") as vllm_model:
+
+        guided_decoding = GuidedDecodingParams(json=SAMPLE_JSON_SCHEMA,
+                                               backend=guided_backend)
+        params = SamplingParams(max_tokens=512,
+                                temperature=0.7,
+                                guided_decoding=guided_decoding)
+
+        messages = [{
+            "role": "system",
+            "content": "you are a helpful assistant"
+        }, {
+            "role":
+            "user",
+            "content":
+            f"Give an example JSON for an employee profile that "
+            f"fits this schema: {SAMPLE_JSON_SCHEMA}"
+        }]
+        outputs = vllm_model.model.chat(messages, sampling_params=params)
+
+        generated_text = outputs[0].outputs[0].text
+        json_response = json.loads(generated_text)
+        assert outputs is not None
+
+        try:
+            jsonschema.validate(instance=json_response,
+                                schema=SAMPLE_JSON_SCHEMA)
+        except jsonschema.exceptions.ValidationError:
+            pytest.fail("Generated response is not valid with JSON schema")

vllm/model_executor/guided_decoding/xgrammar_decoding.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 import json
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any, NamedTuple
+from typing import TYPE_CHECKING, Any
 
 import torch
 from transformers import PreTrainedTokenizerFast
@@ -16,6 +16,7 @@ except ImportError:
 
 from vllm.model_executor.guided_decoding.xgrammar_utils import (
     convert_lark_to_gbnf, grammar_is_likely_lark)
+from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
 
 if TYPE_CHECKING:
     from transformers import PreTrainedTokenizer
@@ -37,11 +38,21 @@ def get_local_xgrammar_guided_decoding_logits_processor(
     return XGrammarLogitsProcessor(config)
 
 
-class TokenizerData(NamedTuple):
+@dataclass(frozen=True)
+class TokenizerData:
     """Immutable container for cached tokenizer data."""
-    encoded_vocab: list[str]
-    stop_token_ids: list[int] | None
-    backend_str: str
+    encoded_vocab: list[str] = field(default_factory=list)
+    stop_token_ids: list[int] | None = None
+    # These fields are mutually exclusive: `backend_str` is used to create a
+    # TokenizeInfo with `TokenizerInfo.from_huggingface` while `vocab_type` is
+    # used within the constructor of TokenizeInfo
+    backend_str: str | None = None
+    vocab_type: xgr.VocabType | None = None
+
+    def __post_init__(self):
+        # Check for mutual exclusive
+        assert not (self.backend_str and self.vocab_type), \
+            "backend_str and vocab_type are mutual exclusive"
 
 
 class TokenizerDataCache:
@@ -68,18 +79,27 @@ class TokenizerDataCache:
                     "get_vocab method.") from e
 
             stop_token_ids = None
-            backend_str = xgr.VocabType.RAW
+            backend_str = ""
+            vocab_type = xgr.VocabType.RAW
+
+            if stop_token_ids is None and hasattr(
+                    tokenizer,
+                    "eos_token_id") and tokenizer.eos_token_id is not None:
+                stop_token_ids = [tokenizer.eos_token_id]
+
             if isinstance(tokenizer, PreTrainedTokenizerFast):
                 backend_str = tokenizer.backend_tokenizer.to_str()
-                if stop_token_ids is None and hasattr(
-                        tokenizer,
-                        "eos_token_id") and tokenizer.eos_token_id is not None:
-                    stop_token_ids = [tokenizer.eos_token_id]
+                vocab_type = None
+
+            elif isinstance(tokenizer, MistralTokenizer):
+                # REF: https://github.com/mlc-ai/xgrammar/blob/5e141f6ff1ca02bc31f9e512e68b61f2a8ae88e5/tests/python/test_tokenizer_info.py#L43 # noqa: E501
+                vocab_type = xgr.VocabType.BYTE_FALLBACK
 
             cls._cache[tokenizer_hash] = TokenizerData(
                 encoded_vocab=encoded_vocab,
                 stop_token_ids=stop_token_ids,
-                backend_str=backend_str)
+                backend_str=backend_str,
+                vocab_type=vocab_type)
 
         return cls._cache[tokenizer_hash]
 
@@ -98,11 +118,30 @@ class GrammarCompilerCache:
         cache_key = str(config.tokenizer_hash)
 
         if cache_key not in cls._cache:
-            assert config.encoded_vocab is not None
-            tokenizer_info = xgr.TokenizerInfo._create_from_handle(
-                xgr_core.TokenizerInfo.from_huggingface(
-                    config.encoded_vocab, config.backend_str,
-                    config.vocab_size, config.stop_token_ids))
+            assert config.tokenizer_data is not None
+            assert config.tokenizer_data.encoded_vocab is not None
+
+            config_data = config.tokenizer_data
+
+            # In TokenizerDataCache.get_tokenizer_data, a serializable
+            # tokenizer_data is created and cached. This data is used to build
+            # a tokenizer_info and create an xgrammar compiler.
+            # - If tokenizer_data has backend_str set, use
+            # xgr_core.TokenizerInfo.from_huggingface (a C++ bind).
+            # - Otherwise, use the default constructor with vocab_type.
+            # - xgr_core.TokenizerInfo.from_huggingface !=
+            #   xgr.TokenizerInfo.from_huggingface.
+            if config_data.backend_str:
+                tokenizer_info = xgr.TokenizerInfo._create_from_handle(
+                    xgr_core.TokenizerInfo.from_huggingface(
+                        config_data.encoded_vocab, config_data.backend_str,
+                        config.vocab_size, config_data.stop_token_ids))
+            else:
+                tokenizer_info = xgr.TokenizerInfo(
+                    config_data.encoded_vocab,
+                    config_data.vocab_type,
+                    vocab_size=config.vocab_size,
+                    stop_token_ids=config_data.stop_token_ids)
             cls._cache[cache_key] = xgr.GrammarCompiler(
                 tokenizer_info, max_threads=config.max_threads)
 
@@ -118,10 +157,7 @@ class GrammarConfig:
     grammar_str: str | None = None
     json_object: bool | None = None
     max_threads: int = 8
-    # Only populated if tokenizer_hash not in cache
-    encoded_vocab: list[str] | None = None
-    stop_token_ids: list[int] | None = None
-    backend_str: str | None = None
+    tokenizer_data: TokenizerData | None = None
 
     @classmethod
     def from_guided_params(cls,
@@ -132,9 +168,6 @@ class GrammarConfig:
 
         tokenizer_hash = hash(tokenizer)
         tokenizer_data = TokenizerDataCache.get_tokenizer_data(tokenizer)
-        encoded_vocab = tokenizer_data.encoded_vocab
-        stop_token_ids = tokenizer_data.stop_token_ids
-        backend_str = tokenizer_data.backend_str
 
         if guided_params.json:
             if not isinstance(guided_params.json, str):
@@ -152,11 +185,9 @@ class GrammarConfig:
 
             return cls(json_str=json_str,
                        vocab_size=model_config.hf_text_config.vocab_size,
-                       encoded_vocab=encoded_vocab,
-                       stop_token_ids=stop_token_ids,
-                       backend_str=backend_str,
                        tokenizer_hash=tokenizer_hash,
-                       max_threads=max_threads)
+                       max_threads=max_threads,
+                       tokenizer_data=tokenizer_data)
         elif guided_params.grammar:
             # XGrammar only supports GBNF grammars, so we must convert Lark
             if grammar_is_likely_lark(guided_params.grammar):
@@ -181,19 +212,17 @@ class GrammarConfig:
 
             return cls(grammar_str=grammar_str,
                        vocab_size=model_config.hf_text_config.vocab_size,
-                       encoded_vocab=encoded_vocab,
-                       stop_token_ids=stop_token_ids,
-                       backend_str=backend_str,
                        tokenizer_hash=tokenizer_hash,
-                       max_threads=max_threads)
+                       max_threads=max_threads,
+                       tokenizer_data=tokenizer_data)
         elif guided_params.json_object:
-            return cls(json_object=True,
-                       vocab_size=model_config.hf_text_config.vocab_size,
-                       encoded_vocab=encoded_vocab,
-                       stop_token_ids=stop_token_ids,
-                       backend_str=backend_str,
-                       tokenizer_hash=tokenizer_hash,
-                       max_threads=max_threads)
+            return cls(
+                json_object=True,
+                vocab_size=model_config.hf_text_config.vocab_size,
+                tokenizer_hash=tokenizer_hash,
+                max_threads=max_threads,
+                tokenizer_data=tokenizer_data,
+            )
         else:
             raise ValueError(
                 "Currently only support JSON and EBNF grammar mode for xgrammar"
@@ -269,10 +298,14 @@ class XGrammarLogitsProcessor:
         # fill_next_token_bitmask so we move it to the device of scores
         device_type = scores.device.type
         if device_type != "cuda":
-            scores = scores.to("cpu")
+            scores = scores.to("cpu").unsqueeze(0)
+
+        # Note: In this method, if the tensors have different dimensions
+        # on CPU device fails, but on GPU it runs without error. Hence the
+        # unsqueeze above for scores, to match the token bitmask shape
         xgr.apply_token_bitmask_inplace(scores,
                                         self.token_bitmask.to(scores.device))
         if device_type != "cuda":
-            scores = scores.to(device_type)
+            scores = scores.to(device_type).squeeze()
 
         return scores

vllm/transformers_utils/tokenizer.py

@@ -132,7 +132,7 @@ def get_tokenizer(
     if is_from_mistral_org and tokenizer_mode != "mistral":
         warnings.warn(
             'It is strongly recommended to run mistral models with '
-            '`--tokenizer_mode "mistral"` to ensure correct '
+            '`--tokenizer-mode "mistral"` to ensure correct '
             'encoding and decoding.',
             FutureWarning,
             stacklevel=2)

vllm/transformers_utils/tokenizers/mistral.py

@@ -314,12 +314,15 @@ class MistralTokenizer:
 
             if regular_tokens:
                 decoded_list.append(
-                    self.decode(regular_tokens))  # type: ignore
+                    self.tokenizer.decode(regular_tokens))  # type: ignore
 
             decoded = ''.join(decoded_list)
 
         return decoded
 
+    # WARN: Outlines logits processors can overwrite this method.
+    # See: guided_decoding/outlines_logits_processors.py::_adapt_tokenizer
+    # for more.
     def decode(self,
                ids: Union[List[int], int],
                skip_special_tokens: bool = True) -> str: