diff --git a/README.md b/README.md
index 196e05e9..8a093a99 100644
--- a/README.md
+++ b/README.md
@@ -224,8 +224,30 @@ curl -X 'POST' 'http://127.0.0.1:8000/stream/process' \
```
will result in a response like {"doc_name": "DOC", "start": INT, "end": INT, "label_name": "STR", "label_id": "STR", ...}\n...
+#### Chat with served models
+You can also "chat" with the running model using the `/stream/ws` endpoint. For example:
+```html
+
+
+
+```
+
### Serve causal language models
-You can serve causal LLMs (e.g., LLaMa 3 or DeepSeek R1) using the `huggingface_llm` model type. To do so, run:
+You can serve open-weight causal LLMs (e.g., LLaMa 3, DeepSeek R1, Qwen3 or Mistral) using the `huggingface_llm` model type. To do so, run:
```commandline
cms serve --model-type huggingface_llm --model-path PATH/TO/MODEL_PACKAGE.zip --host 127.0.0.1 --port 8000
```
@@ -236,11 +258,11 @@ curl -N -X 'POST' 'http://127.0.0.1:8000/stream/generate?max_tokens=512' \
-d 'What is hypertension?'
```
-CMS also provides APIs that are compatible with the OpenAI client. For example, using its Python SDK, you can generate text and retrieve embeddings with the following snippet:
+CMS also provides APIs that are compatible with OpenAI and Ollama clients. For example, using their Python SDKs, you can generate text and retrieve embeddings with the following snippet:
```python
from openai import OpenAI
-client = OpenAI(api_key="dummy", base_url="http://localhost:8000/v1")
+client = OpenAI(api_key="dummy", base_url="http://localhost:8000/openai/v1")
completion = client.chat.completions.create(
model="Huggingface LLM Model",
messages=[
@@ -256,6 +278,29 @@ embeddings = client.embeddings.create(
model="Huggingface LLM Model", input="What is hypertension?"
)
```
+```python
+from ollama import Client
+
+client = Client(host="http://localhost:8000//ollama")
+
+response = client.chat(
+ model="Huggingface LLM Model",
+ messages=[
+ {"role": "system", "content": "You are bot answering questions from the user"},
+ {"role": "user", "content": "What is the normal LVEF range?"},
+ ],
+ stream=False,
+ options={
+ "num_predict": 512,
+ "temperature": 0.7,
+ "top_p": 0.9
+ },
+)
+print(f"CMS => {response["message"]["content"]}")
+embeddings = client.embed(
+ model="Huggingface LLM Model", input="What is the normal LVEF range?"
+)
+```
Note that to enable quantization and training features, you need to install the extra dependencies:
```commandline
pip install '.[llm]'
@@ -268,25 +313,3 @@ To that end, install the following extra dependencies:
pip install '.[mcp]'
```
For detailed configuration, please refer to the [CMS MCP Server docs](./app/mcp/README.md).
-
-#### Chat with served models
-You can also "chat" with the running model using the `/stream/ws` endpoint. For example:
-```html
-
-
-
-```
\ No newline at end of file
diff --git a/app/api/routers/generative.py b/app/api/routers/generative.py
index c27b065c..8cc7266f 100644
--- a/app/api/routers/generative.py
+++ b/app/api/routers/generative.py
@@ -55,6 +55,16 @@
cms_tpot_milliseconds,
)
from app.exception import GenerationException, ClientException
+from app.processors.constrained_decoder import ConstrainedDecoder
+try:
+ import xgrammar as xgr
+except ImportError:
+ xgr = None # type: ignore[assignment]
+
+try:
+ from llguidance import LLMatcher
+except ImportError:
+ LLMatcher = None # type: ignore[assignment,misc]
PATH_GENERATE = "/generate"
@@ -134,6 +144,7 @@ def generate_text(
override_template=chat_template if chat_template else None,
),
max_tokens=max_tokens,
+ num_beams=config.DECODING_NUM_BEAMS,
temperature=temperature,
top_p=top_p,
stop_sequences=stop_sequences,
@@ -214,6 +225,7 @@ async def _stream(prompt: str) -> AsyncGenerator:
async for generated in model_service.generate_async(
prompt=prompt,
max_tokens=max_tokens,
+ num_beams=config.DECODING_NUM_BEAMS,
temperature=temperature,
top_p=top_p,
stop_sequences=stop_sequences,
@@ -231,6 +243,12 @@ async def _stream(prompt: str) -> AsyncGenerator:
f"TPOT in milliseconds: {str(generated.tpot_ms)}"
""
)
+ if generated.timed_out:
+ yield (
+ "\n\n"
+ "Generation timed out before completion so the returned content may be partial."
+ ""
+ )
continue
yield generated
@@ -293,7 +311,7 @@ def generate_chat_completions(
max_tokens = request_data.max_tokens
temperature = request_data.temperature
top_p = request_data.top_p
- json_schema_parser = _get_parser_for_response_format(request_data.response_format)
+ json_schema_parser = _get_parser_for_openai_compatible(request_data.response_format, model_service=model_service)
if isinstance(request_data.stop, str):
stop_sequences = [request_data.stop]
@@ -340,6 +358,7 @@ async def _stream(
async for generated in model_service.generate_async(
prompt,
max_tokens=max_tokens,
+ num_beams=config.DECODING_NUM_BEAMS,
temperature=temperature,
top_p=top_p,
stop_sequences=stop_sequences,
@@ -358,6 +377,14 @@ async def _stream(
}
}
yield f"data: {json.dumps(data)}\n\n"
+ if generated.timed_out:
+ error_data = {
+ "error": {
+ "message": "Generation timed out before completion and the returned content may be partial.",
+ "type": "generation_error",
+ }
+ }
+ yield f"data: {json.dumps(error_data)}\n\n"
continue
if tool_call_emitted:
continue
@@ -452,6 +479,7 @@ def _report_tokens(
generation_result = model_service.generate(
prompt,
max_tokens=max_tokens,
+ num_beams=config.DECODING_NUM_BEAMS,
temperature=temperature,
top_p=top_p,
stop_sequences=stop_sequences or [],
@@ -587,6 +615,7 @@ async def _stream(
async for generated in model_service.generate_async(
prompt,
max_tokens=max_tokens,
+ num_beams=config.DECODING_NUM_BEAMS,
temperature=temperature,
top_p=top_p,
stop_sequences=stop_sequences,
@@ -656,6 +685,7 @@ def _report_tokens(
generation_result = model_service.generate(
prompt,
max_tokens=max_tokens,
+ num_beams=config.DECODING_NUM_BEAMS,
temperature=temperature,
top_p=top_p,
stop_sequences=stop_sequences,
@@ -972,7 +1002,7 @@ async def ollama_generate(
temperature = options.get("temperature", 0.7)
top_p = options.get("top_p", 0.9)
stop_sequences = _normalise_stop_sequences(options.get("stop", None))
- json_schema_parser = _get_parser_for_json_schema(request_data.format)
+ json_schema_parser = _get_parser_for_ollama_compatible(request_data.format, model_service=model_service)
def _report_tokens(
prompt_token_num: int,
@@ -1001,6 +1031,7 @@ async def _stream() -> AsyncGenerator[str, None]:
async for generated in model_service.generate_async(
prompt,
max_tokens=max_tokens,
+ num_beams=config.DECODING_NUM_BEAMS,
temperature=temperature,
top_p=top_p,
stop_sequences=stop_sequences,
@@ -1034,7 +1065,7 @@ async def _stream() -> AsyncGenerator[str, None]:
"created_at": _iso_utc_now(),
"response": "",
"done": True,
- "done_reason": "stop",
+ "done_reason": "timed_out" if generation_result is not None and generation_result.timed_out else "stop",
"prompt_eval_count": generation_result.prompt_token_num if generation_result is not None else 0,
"eval_count": generation_result.completion_token_num if generation_result is not None else 0,
"ttft_in_milliseconds": generation_result.ttft_ms if generation_result is not None else -1,
@@ -1048,6 +1079,7 @@ async def _stream() -> AsyncGenerator[str, None]:
generation_result = model_service.generate(
prompt,
max_tokens=max_tokens,
+ num_beams=config.DECODING_NUM_BEAMS,
temperature=temperature,
top_p=top_p,
stop_sequences=stop_sequences,
@@ -1095,7 +1127,7 @@ async def ollama_chat(
temperature = options.get("temperature", 0.7)
top_p = options.get("top_p", 0.9)
stop_sequences = _normalise_stop_sequences(options.get("stop", None))
- json_schema_parser = _get_parser_for_json_schema(request_data.format)
+ json_schema_parser = _get_parser_for_ollama_compatible(request_data.format, model_service=model_service)
_ensures_chat_template(model_service, chat_template)
def _report_tokens(
@@ -1142,6 +1174,7 @@ async def _stream() -> AsyncGenerator[str, None]:
async for generated in model_service.generate_async(
prompt,
max_tokens=max_tokens,
+ num_beams=config.DECODING_NUM_BEAMS,
temperature=temperature,
top_p=top_p,
stop_sequences=stop_sequences,
@@ -1176,7 +1209,7 @@ async def _stream() -> AsyncGenerator[str, None]:
"created_at": _iso_utc_now(),
"message": {"role": PromptRole.ASSISTANT.value, "content": ""},
"done": True,
- "done_reason": "stop",
+ "done_reason": "timed_out" if generated_result is not None and generated_result.timed_out else "stop",
"prompt_eval_count": generated_result.prompt_token_num if generated_result is not None else 0,
"eval_count": generated_result.completion_token_num if generated_result is not None else 0,
"ttft_in_milliseconds": generated_result.ttft_ms if generated_result is not None else -1,
@@ -1190,6 +1223,7 @@ async def _stream() -> AsyncGenerator[str, None]:
generated_result = model_service.generate(
prompt,
max_tokens=max_tokens,
+ num_beams=config.DECODING_NUM_BEAMS,
temperature=temperature,
top_p=top_p,
stop_sequences=stop_sequences,
@@ -1267,33 +1301,35 @@ def _build_prompt_text(
)
-def _get_parser_for_response_format(response_format: Optional[OpenAIResponseFormat]) -> Optional[Any]:
+def _get_parser_for_openai_compatible(
+ response_format: Optional[OpenAIResponseFormat],
+ model_service: Optional[AbstractModelService] = None,
+) -> Optional[Any]:
if response_format is None:
return None
if response_format.type == "json_schema":
- try:
- from lmformatenforcer import JsonSchemaParser
- parser = JsonSchemaParser(response_format.json_schema.schema_)
- setattr(parser, "schema", response_format.json_schema.schema_)
- return parser
- except ImportError as e:
- raise ClientException("lmformatenforcer package is not installed; required for JSON schema support") from e
- except Exception as exc:
- raise ClientException("Invalid JSON schema in response_format") from exc
+ return ConstrainedDecoder.get_parser(
+ response_format.json_schema.schema_,
+ model_service=model_service,
+ source="response_format",
+ backend=config.DECODING_BACKEND,
+ )
else:
raise ClientException("Unsupported response_format type; only 'json_schema' is supported")
-def _get_parser_for_json_schema(json_schema: Optional[Dict[str, Any]]) -> Optional[Any]:
+def _get_parser_for_ollama_compatible(
+ json_schema: Optional[Dict[str, Any]],
+ model_service: Optional[AbstractModelService] = None,
+) -> Optional[Any]:
if json_schema is None:
return None
- try:
- from lmformatenforcer import JsonSchemaParser
- return JsonSchemaParser(json_schema)
- except ImportError as e:
- raise ClientException("lmformatenforcer package is not installed; required for JSON schema support") from e
- except Exception as exc:
- raise ClientException("Invalid JSON schema") from exc
+ return ConstrainedDecoder.get_parser(
+ json_schema,
+ model_service=model_service,
+ source="json_schema",
+ backend=config.DECODING_BACKEND,
+ )
def _send_usage_metrics(
diff --git a/app/api/routers/stream.py b/app/api/routers/stream.py
index 2a95e7f3..0cc36f1b 100644
--- a/app/api/routers/stream.py
+++ b/app/api/routers/stream.py
@@ -176,7 +176,11 @@ async def _monitor_idle() -> None:
logger.debug(str(e))
-@router.get(PATH_SSE_EVENTS)
+@router.get(
+ PATH_SSE_EVENTS,
+ tags=[Tags.Annotations.name],
+ description="Server-Sent Events (SSE) endpoint to receive NER entities as stream events for a specific client",
+)
@limiter.exempt
async def get_entities_stream_from_sse(
request: Request,
diff --git a/app/config.py b/app/config.py
index df020671..30ea25ec 100644
--- a/app/config.py
+++ b/app/config.py
@@ -48,6 +48,8 @@ class Settings(BaseSettings): # type: ignore
ENABLE_SPDA_ATTN: str = "true" # if "true", attempt to use SPDA attention for HuggingFace LLM loading
ASSISTANT_MODEL_FULL_PATH: str = "" # the full path to the assistant model package for speculative decoding
OVERRIDE_CHAT_TEMPLATE: str = "" # if set, override the chat template used for prompt formatting
+ DECODING_BACKEND: str = "lm_format_enforcer" # the decoding backend to use for LLM constrained decoding, one of "lm_format_enforcer", "xgrammar" or "llguidance"
+ DECODING_NUM_BEAMS: int = 1 # the number of beams to used for beam search during LLM decoding
DEBUG: str = "false" # if "true", the debug mode is switched on
class Config:
diff --git a/app/domain.py b/app/domain.py
index 6dc050c1..51ce59d0 100644
--- a/app/domain.py
+++ b/app/domain.py
@@ -137,6 +137,13 @@ class LlmDatasetType(Enum):
CSV = "csv"
+class LLMDecodingBackend(Enum):
+ LM_FORMAT_ENFORCER = "lm_format_enforcer"
+ XGRAMMAR = "xgrammar"
+ LLGUIDANCE = "llguidance"
+
+
+
class Annotation(BaseModel):
doc_name: Optional[str] = Field(default=None, description="The name of the document to which the annotation belongs")
start: int = Field(description="The start index of the annotation span")
@@ -220,6 +227,10 @@ class GenerationResult(BaseModel):
completion_token_num: int = Field(..., description="The number of tokens in the generated text")
ttft_ms: int = Field(default=-1, description="Time to first token in milliseconds")
tpot_ms: int = Field(default=-1, description="Average time per output token in milliseconds")
+ timed_out: bool = Field(
+ default=False,
+ description="Whether the generation was stopped by the generation timeout criteria before completing",
+ )
class OpenAIStreamOptions(BaseModel):
diff --git a/app/envs/.env b/app/envs/.env
index 3f9db16f..bf24a08d 100644
--- a/app/envs/.env
+++ b/app/envs/.env
@@ -53,7 +53,7 @@ PROCESS_RATE_LIMIT=180/minute
PROCESS_BULK_RATE_LIMIT=90/minute
# The rate limit on the text generation routes
-GENERATION_RATE_LIMIT=10/minute
+GENERATION_RATE_LIMIT=1000/minute
# The timeout in seconds on the text generation requests
GENERATION_TIMEOUT_SECONDS=180
@@ -110,5 +110,11 @@ ASSISTANT_MODEL_FULL_PATH=
# If set, override the chat template used for prompt formatting
OVERRIDE_CHAT_TEMPLATE=
+# the decoding backend to use for LLM constrained decoding, either "lm_format_enforcer", "xgrammar", or "llguidance"
+DECODING_BACKEND=lm_format_enforcer
+
+# the number of beams to used for beam search during LLM decoding
+DECODING_NUM_BEAMS=1
+
# If "true", the debug mode is switched on
DEBUG=false
diff --git a/app/management/prometheus_metrics.py b/app/management/prometheus_metrics.py
index 631c47f1..904bb2e3 100644
--- a/app/management/prometheus_metrics.py
+++ b/app/management/prometheus_metrics.py
@@ -1,4 +1,4 @@
-from prometheus_client import Histogram, Gauge
+from prometheus_client import Histogram, Gauge, Counter
# The histogram metric to track the number of annotations extracted from a document by different handlers
cms_doc_annotations = Histogram(
@@ -69,3 +69,45 @@
"Average time per output token in milliseconds",
["handler"],
)
+
+# The histogram metric to track end-to-end generation request latency in milliseconds
+cms_gen_request_latency_milliseconds = Histogram(
+ "cms_gen_request_latency_milliseconds",
+ "End-to-End generation request latency in milliseconds",
+ ["handler"],
+)
+
+# The counter metric to track the number of prefix cache queries
+cms_prefix_cache_queries = Counter(
+ "cms_prefix_cache_queries",
+ "Total number of prefix cache queries",
+ ["handler"],
+)
+
+# The counter metric to track the number of prefix cache hits
+cms_prefix_cache_hits = Counter(
+ "cms_prefix_cache_hits",
+ "Total number of prefix cache hits",
+ ["handler"],
+)
+
+# The histogram metric to track generation job queue time in milliseconds
+cms_gen_job_queue_time_milliseconds = Histogram(
+ "cms_gen_job_queue_time_milliseconds",
+ "Time spent waiting in the queue before generation starts in milliseconds",
+ ["handler"],
+)
+
+# The gauge metric to track the number of generation jobs actively running
+cms_num_of_gen_jobs_running = Gauge(
+ "cms_num_of_gen_jobs_running",
+ "Number of generation jobs currently running",
+ ["handler"],
+)
+
+# The gauge metric to track the number of generation jobs waiting in the queue
+cms_num_of_gen_jobs_waiting = Gauge(
+ "cms_num_of_gen_jobs_waiting",
+ "Number of generation jobs currently waiting in the queue",
+ ["handler"],
+)
diff --git a/app/model_services/huggingface_llm_model.py b/app/model_services/huggingface_llm_model.py
index d9027c1c..6655bf07 100644
--- a/app/model_services/huggingface_llm_model.py
+++ b/app/model_services/huggingface_llm_model.py
@@ -3,11 +3,10 @@
import logging
import time
import hashlib
-import json
import re
import torch
from concurrent.futures import ThreadPoolExecutor
-from typing import Dict, List, Optional, Tuple, Any, AsyncIterable, TextIO, Callable, Union, TYPE_CHECKING
+from typing import Dict, List, Optional, Tuple, Any, AsyncIterable, TextIO, Callable, Union
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
@@ -18,15 +17,26 @@
BitsAndBytesConfig,
StoppingCriteria,
StoppingCriteriaList,
+ LogitsProcessor,
+ LogitsProcessorList,
+)
+from app.management.prometheus_metrics import (
+ cms_gen_request_latency_milliseconds,
+ cms_prefix_cache_queries,
+ cms_prefix_cache_hits,
+ cms_gen_job_queue_time_milliseconds,
+ cms_num_of_gen_jobs_running,
+ cms_num_of_gen_jobs_waiting,
)
from app import __version__ as app_version
-from app.exception import ConfigurationException, GenerationException, ExtraDependencyRequiredException
+from app.exception import ConfigurationException, GenerationException
from app.model_services.base import AbstractModelService
from app.trainers.huggingface_llm_trainer import HuggingFaceLlmSupervisedTrainer, HuggingFaceLlmUnsupervisedTrainer
from app.domain import ModelCard, ModelType, Annotation, Device, GenerationResult
from app.config import Settings
from app.processors.data_batcher import MicroBatchScheduler
from app.processors.prefix_cache import PrefixCache
+from app.processors.constrained_decoder import ConstrainedDecoder
from app.utils import (
get_settings,
non_default_device_is_available,
@@ -34,16 +44,10 @@
ensure_tensor_contiguity,
get_model_data_package_base_name,
ensure_pad_token,
- dump_pydantic_object_to_dict,
extract_json_string,
has_turing_generation_gpu,
resolve_safe_max_model_length,
)
-if TYPE_CHECKING:
- from lmformatenforcer import JsonSchemaParser as JsonSchemaParserType
-else:
- JsonSchemaParserType = Any
-
logger = logging.getLogger("cms")
@@ -68,14 +72,6 @@ def __init__(
model_name (Optional[str]): The name of the model. Defaults to None.
base_model_file (Optional[str]): The model package file name. Defaults to None.
"""
- try:
- from lmformatenforcer.integrations.transformers import build_transformers_prefix_allowed_tokens_fn
- except ImportError:
- logger.error("Cannot import JsonSchemaParser. Please install it with `pip install '.[llm]'`.")
- raise ExtraDependencyRequiredException(
- "Cannot import JsonSchemaParser. Please install it with `pip install '.[llm]'`."
- )
-
super().__init__(config)
self._config = config
self._model_parent_dir = model_parent_dir or os.path.abspath(
@@ -92,7 +88,7 @@ def __init__(
self._sentence_endings = ".。!！?？:：;；\n"
self._generation_timeout_secs = config.GENERATION_TIMEOUT_SECONDS or 180
self._prefix_kv_cache = PrefixCache()
- self._build_transformers_prefix_allowed_tokens_fn = build_transformers_prefix_allowed_tokens_fn
+ self._constrained_decoder = ConstrainedDecoder(backend=config.DECODING_BACKEND)
self._micro_batch_scheduler = MicroBatchScheduler(
process_batch_fn=self._process_batched_requests,
batch_key_fn=lambda request: request["batch_key"],
@@ -214,6 +210,7 @@ def load_model(
model = HuggingFaceLlmModel._load_causal_lm(
enable_sdpa_attn=enable_sdpa_attn,
model_path=model_path,
+ device_map={"": get_settings().DEVICE},
low_cpu_mem_usage=True,
)
else:
@@ -237,6 +234,7 @@ def load_model(
enable_sdpa_attn=enable_sdpa_attn,
model_path=model_path,
quantization_config=bnb_config,
+ device_map={"": get_settings().DEVICE},
low_cpu_mem_usage=True,
)
elif load_in_8bit:
@@ -259,6 +257,7 @@ def load_model(
enable_sdpa_attn=enable_sdpa_attn,
model_path=model_path,
quantization_config=bnb_config,
+ device_map={"": get_settings().DEVICE},
low_cpu_mem_usage=True,
)
else:
@@ -274,6 +273,7 @@ def load_model(
model = HuggingFaceLlmModel._load_causal_lm(
enable_sdpa_attn=enable_sdpa_attn,
model_path=model_path,
+ device_map={"": get_settings().DEVICE},
low_cpu_mem_usage=True,
dtype=torch.float16 if has_turing_generation_gpu() else torch.bfloat16,
)
@@ -355,6 +355,28 @@ def init_model(self,
self._supervised_trainer = HuggingFaceLlmSupervisedTrainer(self)
self._unsupervised_trainer = HuggingFaceLlmUnsupervisedTrainer(self)
+ self._warn_vocab_mismatch()
+
+ def _warn_vocab_mismatch(self) -> None:
+ tokenizer_vocab_size = getattr(self._tokenizer, "vocab_size", None)
+ config_vocab_size = getattr(self._model.config, "vocab_size", None)
+ if tokenizer_vocab_size is None or config_vocab_size is None:
+ return
+ if tokenizer_vocab_size != config_vocab_size:
+ logger.warning(
+ "Tokenizer vocab_size (%s) does not match model config vocab_size (%s). "
+ "This can cause CUDA device-side asserts during generation.",
+ tokenizer_vocab_size,
+ config_vocab_size,
+ )
+
+ @staticmethod
+ def _get_actual_vocab_size(model: PreTrainedModel) -> int:
+ embed = model.get_input_embeddings()
+ if embed is not None and hasattr(embed, "weight"):
+ return int(embed.weight.shape[0])
+ return int(getattr(model.config, "vocab_size", 0) or 0)
+
def info(self) -> ModelCard:
"""
Retrieves a ModelCard containing information about the model.
@@ -397,7 +419,7 @@ def generate(
stop_sequences: Optional[List[str]] = None,
report_tokens: Optional[Callable[..., None]] = None,
ensure_full_sentences: bool = False,
- json_schema_parser: Optional[JsonSchemaParserType] = None,
+ json_schema_parser: Optional[Any] = None,
prefix_prompt: Optional[str] = None,
*args: Tuple,
**kwargs: Dict[str, Any],
@@ -415,7 +437,7 @@ def generate(
stop_sequences (Optional[List[str]]): List of strings that will stop generation when encountered. Defaults to None.
report_tokens (Optional[Callable[[str], None]]): The callback function to send metrics. Defaults to None.
ensure_full_sentences (bool): Whether to generate full sentences only. Defaults to False.
- json_schema_parser (Optional[JsonSchemaParser]): The JSON schema parser for validating the generated text. Defaults to None.
+ json_schema_parser (Optional[Any]): The JSON schema parser or compiled grammar for validating the generated text. Defaults to None.
prefix_prompt (Optional[str]): The prefix prompt to be used for generation. Defaults to None.
Returns:
@@ -430,16 +452,18 @@ def generate(
"report_tokens": report_tokens,
"json_schema_parser": json_schema_parser,
"prefix_prompt": prefix_prompt,
+ "request_received_time": time.monotonic(),
"batch_key": (
min_tokens,
max_tokens,
num_beams,
temperature,
top_p,
- self._get_schema_hash(json_schema_parser),
+ self._constrained_decoder.get_schema_hash(json_schema_parser),
(PrefixCache.key(prefix_prompt) if prefix_prompt else None),
),
}
+ cms_num_of_gen_jobs_waiting.labels(handler="cms_service").inc()
future = self._micro_batch_scheduler.submit(request)
try:
generation_result = future.result()
@@ -461,7 +485,7 @@ async def generate_async(
stop_sequences: Optional[List[str]] = None,
report_tokens: Optional[Callable[..., None]] = None,
ensure_full_sentences: bool = False,
- json_schema_parser: Optional[JsonSchemaParserType] = None,
+ json_schema_parser: Optional[Any] = None,
prefix_prompt: Optional[str] = None,
*args: Tuple,
**kwargs: Dict[str, Any],
@@ -479,7 +503,7 @@ async def generate_async(
stop_sequences (Optional[List[str]]): List of strings that will stop generation when encountered. Defaults to None.
report_tokens (Optional[Callable[[str], None]]): The callback function to send metrics. Defaults to None.
ensure_full_sentences (bool): Whether to generate full sentences only. Defaults to False.
- json_schema_parser (Optional[JsonSchemaParser]): The JSON schema parser for validating the generated text. Defaults to None.
+ json_schema_parser (Optional[Any]): The JSON schema parser or compiled grammar for validating the generated text. Defaults to None.
prefix_prompt (Optional[str]): The prefix prompt to be used for generation. Defaults to None.
Returns:
@@ -487,11 +511,17 @@ async def generate_async(
"""
self.model.eval()
+ gen_job_received_time = time.monotonic()
+ cms_num_of_gen_jobs_waiting.labels(handler="cms_service").inc()
logger.debug("Prompt after chat template applied: %s", prompt[:200])
full_prompt_len = None
past_key_values = None
prefix_len = 0
prefix_text = prefix_prompt or ""
+
+ if bool(prefix_prompt):
+ cms_prefix_cache_queries.labels(handler="cms_service").inc()
+
use_prefix_cache = bool(prefix_prompt) and prompt.startswith(prefix_text)
if use_prefix_cache:
prefix_entry = self._prefix_kv_cache.get_prefix_entry(
@@ -511,6 +541,7 @@ async def generate_async(
use_prefix_cache = False
else:
prefix_len = int(prefix_entry.input_ids.shape[1])
+ cms_prefix_cache_hits.labels(handler="cms_service").inc()
full_prompt_len = prefix_len + int(inputs.attention_mask.sum().item())
prefix_mask = torch.ones(
(inputs.input_ids.shape[0], prefix_len),
@@ -536,15 +567,17 @@ async def generate_async(
past_key_values=past_key_values,
)
+ max_tokens = max(min_tokens, max_tokens)
+ use_constrained_tokens = json_schema_parser is not None
+
streamer = AsyncTextIteratorStreamer(
self.tokenizer,
skip_prompt=True,
- timeout=self._generation_timeout_secs,
+ timeout=None, # rely on the StoppingCriteria for timeout handling
skip_special_tokens=True,
clean_up_tokenization_spaces=True,
)
- max_tokens = max(min_tokens, max_tokens)
- use_constrained_tokens = json_schema_parser is not None
+ do_sample = (num_beams == 1) and temperature > 0.0
generation_kwargs = dict(
inputs=inputs.input_ids,
attention_mask=attention_mask,
@@ -553,15 +586,24 @@ async def generate_async(
max_new_tokens=max_tokens,
use_cache=True,
num_beams=num_beams,
- do_sample=(num_beams == 1 and not use_constrained_tokens),
- temperature=temperature,
- top_p=top_p,
- top_k=0,
+ num_return_sequences=1,
+ do_sample=do_sample,
+ temperature=temperature if do_sample else None,
+ top_p=top_p if do_sample else None,
+ top_k=0 if do_sample else None,
repetition_penalty=(1.0 if use_constrained_tokens else 1.2),
no_repeat_ngram_size=(0 if use_constrained_tokens else 3),
pad_token_id=self.tokenizer.pad_token_id,
- stopping_criteria=StoppingCriteriaList([TimeoutCriteria(float(self._generation_timeout_secs))]),
+ renormalize_logits=False,
+ stopping_criteria=StoppingCriteriaList(
+ [timeout_criteria := TimeoutCriteria(float(self._generation_timeout_secs))]
+ ),
)
+ if use_constrained_tokens:
+ generation_kwargs["logits_processor"] = LogitsProcessorList([
+ VocabSafetyLogitsProcessor(self._get_actual_vocab_size(self.model)),
+ Float32LogitsProcessor(),
+ ])
if past_key_values is not None:
generation_kwargs["past_key_values"] = past_key_values
cache_position = torch.arange(
@@ -571,9 +613,11 @@ async def generate_async(
)
generation_kwargs["cache_position"] = cache_position
if use_constrained_tokens:
- generation_kwargs["prefix_allowed_tokens_fn"] = self._build_transformers_prefix_allowed_tokens_fn(
- self.tokenizer,
+ generation_kwargs = self._constrained_decoder.apply_grammar_constraint(
+ generation_kwargs,
json_schema_parser,
+ tokenizer=self.tokenizer,
+ vocab_size=getattr(self.model.config, "vocab_size", None),
)
if self._assistant_model is not None:
generation_kwargs["assistant_model"] = self._assistant_model
@@ -583,16 +627,28 @@ async def generate_async(
if self._assistant_tokenizer.vocab_size != self._tokenizer.vocab_size: # type: ignore
generation_kwargs["assistant_tokenizer"] = self._assistant_tokenizer
+ generation_future = None
try:
generation_start = time.monotonic()
ttft_milliseconds = -1.0
- _ = self._text_generator.submit(self.model.generate, **generation_kwargs)
+
+ def _wrapped_generate(**kwargs: Dict[str, Any]) -> Any:
+ cms_num_of_gen_jobs_waiting.labels(handler="cms_service").dec()
+ cms_num_of_gen_jobs_running.labels(handler="cms_service").inc()
+ queue_time_ms = (time.monotonic() - generation_start) * 1000.0
+ cms_gen_job_queue_time_milliseconds.labels(handler="cms_service").observe(queue_time_ms)
+ try:
+ return self.model.generate(**kwargs)
+ finally:
+ cms_num_of_gen_jobs_running.labels(handler="cms_service").dec()
+
+ generation_future = self._text_generator.submit(_wrapped_generate, **generation_kwargs)
buffer = ""
full_output = ""
output_is_formatted = use_constrained_tokens
if not ensure_full_sentences:
- async for content in streamer:
+ async for content in self._stream_tokens(streamer, generation_future):
if ttft_milliseconds == -1.0 and content:
ttft_milliseconds = (time.monotonic() - generation_start) * 1000.0
prev_output = full_output
@@ -611,7 +667,7 @@ async def generate_async(
await asyncio.sleep(0.1)
yield out_chunk
else:
- async for content in streamer:
+ async for content in self._stream_tokens(streamer, generation_future):
if ttft_milliseconds == -1.0 and content:
ttft_milliseconds = (time.monotonic() - generation_start) * 1000.0
buffer += content
@@ -648,6 +704,9 @@ async def generate_async(
if output_is_formatted:
yield extract_json_string(full_output)
+ if generation_future.done():
+ generation_future.result()
+
logger.debug("Decoded raw output: %s",full_output[:200])
prompt_token_num = (
full_prompt_len
@@ -668,16 +727,24 @@ async def generate_async(
ttft_milliseconds=int(ttft_milliseconds), # type: ignore
tpot_milliseconds=int(tpot_milliseconds), # type: ignore
)
+ cms_gen_request_latency_milliseconds.labels(handler="cms_service").observe(
+ (time.monotonic() - gen_job_received_time) * 1000.0
+ )
yield GenerationResult(
text=full_output,
prompt_token_num=prompt_token_num,
completion_token_num=completion_token_num,
ttft_ms=int(ttft_milliseconds),
tpot_ms=int(tpot_milliseconds),
+ timed_out=timeout_criteria._triggered,
)
except Exception as e:
logger.error("An error occurred while generating the response")
logger.exception(e)
+
+ if generation_future is not None and not generation_future.done():
+ cms_num_of_gen_jobs_waiting.labels(handler="cms_service").dec()
+
raise GenerationException(f"Failed to generate text from the request: {str(e)}") from e
finally:
logger.debug("Chat response generation completed")
@@ -864,13 +931,6 @@ def _load_causal_lm(
return AutoModelForCausalLM.from_pretrained(model_path, **kwargs)
- @staticmethod
- def _get_schema_hash(json_schema_parser: Optional[JsonSchemaParserType]) -> Optional[str]:
- if json_schema_parser is None:
- return None
- schema_dict = dump_pydantic_object_to_dict(json_schema_parser.context.model_class)
- return hashlib.sha256(json.dumps(schema_dict).encode("utf-8")).hexdigest()
-
def _postprocess_generated_text(
self,
generated_text: str,
@@ -929,7 +989,46 @@ def _split_stream_chunk(self, text: str, max_words_per_chunk: int = 4) -> List[s
chunks.append("".join(current))
return chunks
+ async def _stream_tokens(
+ self,
+ streamer: AsyncTextIteratorStreamer,
+ generation_future: Any,
+ ) -> AsyncIterable[str]:
+ """Wrap streamer to monitor both streamer queue and thread pool execution future.
+ If the future fails, propagate the exception immediately."""
+ async_generation_future = asyncio.wrap_future(generation_future)
+ while not async_generation_future.done():
+ next_token_task = asyncio.create_task(streamer.__anext__())
+ done, pending = await asyncio.wait(
+ [next_token_task, async_generation_future],
+ return_when=asyncio.FIRST_COMPLETED,
+ )
+
+ if next_token_task in done:
+ try:
+ content = next_token_task.result()
+ yield content
+ except StopAsyncIteration:
+ return
+ else:
+ next_token_task.cancel()
+ try:
+ await next_token_task
+ except asyncio.CancelledError:
+ pass
+
+ # Check for thread execution exception
+ exc = async_generation_future.exception()
+ if exc is not None:
+ raise exc
+
def _process_batched_requests(self, requests: List[Dict[str, Any]]) -> None:
+ for req in requests:
+ cms_num_of_gen_jobs_waiting.labels(handler="cms_service").dec()
+ cms_num_of_gen_jobs_running.labels(handler="cms_service").inc()
+ if "request_received_time" in req:
+ queue_time_ms = (time.monotonic() - req["request_received_time"]) * 1000.0
+ cms_gen_job_queue_time_milliseconds.labels(handler="cms_service").observe(queue_time_ms)
try:
self.model.eval()
prompt_texts = [req["prompt"] for req in requests]
@@ -1019,6 +1118,7 @@ def _process_batched_requests(self, requests: List[Dict[str, Any]]) -> None:
min_tokens, max_tokens, num_beams, temperature, top_p, _, _ = requests[0]["batch_key"]
json_schema_parser = requests[0].get("json_schema_parser")
use_constrained_tokens = json_schema_parser is not None
+ do_sample = (num_beams == 1) and temperature > 0.0
generation_kwargs = dict(
inputs=batch_input_ids,
attention_mask=attention_mask,
@@ -1026,15 +1126,24 @@ def _process_batched_requests(self, requests: List[Dict[str, Any]]) -> None:
max_new_tokens=max_tokens,
use_cache=True,
num_beams=num_beams,
- do_sample=(num_beams == 1 and not use_constrained_tokens),
- temperature=temperature,
- top_p=top_p,
- top_k=0,
+ num_return_sequences=1,
+ do_sample=do_sample,
+ temperature=temperature if do_sample else None,
+ top_p=top_p if do_sample else None,
+ top_k=0 if do_sample else None,
repetition_penalty=(1.0 if use_constrained_tokens else 1.2),
no_repeat_ngram_size=(0 if use_constrained_tokens else 3),
pad_token_id=self.tokenizer.pad_token_id,
- stopping_criteria=StoppingCriteriaList([TimeoutCriteria(float(self._generation_timeout_secs))]),
+ renormalize_logits=False,
+ stopping_criteria=StoppingCriteriaList(
+ [timeout_criteria := TimeoutCriteria(float(self._generation_timeout_secs))]
+ ),
)
+ if use_constrained_tokens:
+ generation_kwargs["logits_processor"] = LogitsProcessorList([
+ VocabSafetyLogitsProcessor(self._get_actual_vocab_size(self.model)),
+ Float32LogitsProcessor(),
+ ])
if past_key_values is not None:
generation_kwargs["past_key_values"] = past_key_values
cache_position = torch.arange(
@@ -1044,9 +1153,11 @@ def _process_batched_requests(self, requests: List[Dict[str, Any]]) -> None:
)
generation_kwargs["cache_position"] = cache_position
if use_constrained_tokens:
- generation_kwargs["prefix_allowed_tokens_fn"] = self._build_transformers_prefix_allowed_tokens_fn(
- self.tokenizer,
+ generation_kwargs = self._constrained_decoder.apply_grammar_constraint(
+ generation_kwargs,
json_schema_parser,
+ tokenizer=self.tokenizer,
+ vocab_size=getattr(self.model.config, "vocab_size", None),
)
if self._assistant_model is not None:
generation_kwargs["assistant_model"] = self._assistant_model
@@ -1058,8 +1169,9 @@ def _process_batched_requests(self, requests: List[Dict[str, Any]]) -> None:
generation_start = time.monotonic()
outputs = self.model.generate(**generation_kwargs)
total_generation_ms = (time.monotonic() - generation_start) * 1000.0
+ sequences = outputs.sequences if hasattr(outputs, "sequences") else outputs
for idx, req in enumerate(requests):
- completion_ids = outputs[idx][prompt_lens[idx]:]
+ completion_ids = sequences[idx][prompt_lens[idx]:]
generated_text = self.tokenizer.decode(completion_ids, skip_special_tokens=True)
logger.debug("Decoded raw output (batched): %s",generated_text[:200])
if use_constrained_tokens:
@@ -1100,13 +1212,19 @@ def _process_batched_requests(self, requests: List[Dict[str, Any]]) -> None:
completion_token_num=completion_token_num,
ttft_ms=-1,
tpot_ms=int(tpot_milliseconds),
+ timed_out=timeout_criteria._triggered,
))
+ if "request_received_time" in req:
+ queue_time_ms = (time.monotonic() - req["request_received_time"]) * 1000.0
+ cms_gen_request_latency_milliseconds.labels(handler="cms_service").observe(total_generation_ms + queue_time_ms)
+ cms_num_of_gen_jobs_running.labels(handler="cms_service").dec()
except Exception as e:
logger.error("Batched generation failed")
logger.exception(e)
for req in requests:
if not req["future"].done():
req["future"].set_exception(e)
+ cms_num_of_gen_jobs_running.labels(handler="cms_service").dec()
def _ensure_non_empty_inputs(
self,
@@ -1153,12 +1271,37 @@ def _ensure_non_empty_inputs(
return rebuilt, rebuilt.attention_mask, None, full_prompt_len, None
+class VocabSafetyLogitsProcessor(LogitsProcessor):
+ """Masks token IDs that exceed the model's actual vocabulary size."""
+
+ def __init__(self, vocab_size: int) -> None:
+ self._vocab_size = max(vocab_size, 0)
+
+ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+ if self._vocab_size <= 0 or scores.shape[-1] <= self._vocab_size:
+ return scores
+ scores[..., self._vocab_size:].fill_(-1e10)
+
+ return scores
+
+
+class Float32LogitsProcessor(LogitsProcessor):
+ """Cast logits to prevent multinomial sampling errors on bfloat16 or float16 models."""
+
+ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.Tensor:
+ out: torch.Tensor = scores.to(torch.float32)
+ out.nan_to_num_(nan=-1e10, posinf=1e10, neginf=-1e10)
+
+ return out
+
+
class TimeoutCriteria(StoppingCriteria):
"""Stop generation when the timeout is reached."""
def __init__(self, timeout_in_secs: float) -> None:
self._timeout_in_secs = timeout_in_secs
self._deadline = time.monotonic() + timeout_in_secs
+ self._triggered = False
def __call__(
self, input_ids: torch.LongTensor,
@@ -1167,6 +1310,7 @@ def __call__(
) -> bool:
now = time.monotonic()
if now >= self._deadline:
+ self._triggered = True
logger.warning(f"Generation timed out after {str(self._timeout_in_secs)} seconds")
return True
return False
diff --git a/app/processors/constrained_decoder.py b/app/processors/constrained_decoder.py
new file mode 100644
index 00000000..1b0df27a
--- /dev/null
+++ b/app/processors/constrained_decoder.py
@@ -0,0 +1,300 @@
+import json
+import hashlib
+import logging
+from typing import Any, Dict, Optional
+
+from transformers import LogitsProcessor
+from app.domain import LLMDecodingBackend
+from app.exception import ConfigurationException, ClientException, ExtraDependencyRequiredException
+
+try:
+ from lmformatenforcer import JsonSchemaParser
+except ImportError:
+ JsonSchemaParser = None # type: ignore[assignment]
+
+try:
+ from lmformatenforcer.integrations.transformers import build_transformers_prefix_allowed_tokens_fn
+except ImportError:
+ build_transformers_prefix_allowed_tokens_fn = None # type: ignore[assignment]
+
+try:
+ import xgrammar as xgr
+except ImportError:
+ xgr = None # type: ignore[assignment]
+
+try:
+ import llguidance
+ from llguidance import LLMatcher
+ from llguidance.hf import from_tokenizer as lg_from_tokenizer
+ from llguidance.torch import (
+ allocate_token_bitmask,
+ fill_next_token_bitmask,
+ apply_token_bitmask_inplace,
+ )
+except ImportError:
+ llguidance = None # type: ignore[assignment]
+ LLMatcher = None # type: ignore[assignment,misc]
+ lg_from_tokenizer = None # type: ignore[assignment]
+ allocate_token_bitmask = None # type: ignore[assignment]
+ fill_next_token_bitmask = None # type: ignore[assignment]
+ apply_token_bitmask_inplace = None # type: ignore[assignment]
+
+from app.utils import dump_pydantic_object_to_dict
+
+logger = logging.getLogger("cms")
+
+
+class ConstrainedDecoder:
+ """Encapsulates constrained decoding logic for JSON schema enforcement."""
+
+ def __init__(
+ self,
+ tokenizer: Optional[Any] = None,
+ vocab_size: Optional[int] = None,
+ backend: str = LLMDecodingBackend.LM_FORMAT_ENFORCER.value,
+ ) -> None:
+ if build_transformers_prefix_allowed_tokens_fn is None and xgr is None and llguidance is None:
+ logger.error("Cannot import lm-format-enforcer, xgrammar or llguidance. Please install it with `pip install '.[llm]'`.")
+ raise ExtraDependencyRequiredException(
+ "Cannot import lm-format-enforcer, xgrammar or llguidance. Please install it with `pip install '.[llm]'`."
+ )
+ self.tokenizer = tokenizer
+ self.vocab_size = vocab_size
+ self.build_transformers_prefix_allowed_tokens_fn = build_transformers_prefix_allowed_tokens_fn
+ self.backend = backend
+ self._compiled_cache: Dict[str, Any] = {}
+ self._compiled_cache_maxsize = 256
+
+ @staticmethod
+ def get_parser(
+ schema: Dict[str, Any],
+ model_service: Optional[Any] = None,
+ source: str = "schema",
+ backend: str = LLMDecodingBackend.LM_FORMAT_ENFORCER.value,
+ ) -> Any:
+ """
+ Compiles a JSON schema into a parser or grammar object based on the specified backend.
+
+ Args:
+ schema (Dict[str, Any]): The JSON schema to compile.
+ model_service (Optional[Any]): An instance of the model service.
+ source (str): A string indicating the source of the schema.
+ backend (str): The backend to use for constrained decoding, either "lm-format-enforcer", "xgrammar" or "llguidance"
+
+ Returns:
+ Any: A compiled parser or grammar object used for constrained decoding.
+ """
+ if backend == LLMDecodingBackend.LLGUIDANCE.value:
+ if LLMatcher is None:
+ raise ClientException("llguidance is not installed")
+ if model_service is None or not hasattr(model_service, "tokenizer"):
+ raise ClientException("model_service with tokenizer is required for llguidance backend")
+ logger.debug("Using llguidance backend for constrained decoding (source=%s)", source)
+ try:
+ grammar = LLMatcher.grammar_from_json_schema(json.dumps(schema))
+ logger.debug("llguidance grammar compiled: %s chars", len(grammar))
+ return grammar
+ except Exception as exc:
+ logger.debug("llguidance schema compilation failed: %s", exc)
+ raise ClientException(f"Invalid JSON schema for llguidance ({source})") from exc
+ elif backend == LLMDecodingBackend.XGRAMMAR.value:
+ if xgr is None:
+ raise ClientException("xgrammar is not installed")
+ if model_service is None or not hasattr(model_service, "tokenizer"):
+ raise ClientException("model_service with tokenizer is required for xgrammar backend")
+ logger.debug("Using xgrammar backend for constrained decoding (source=%s)", source)
+ tokenizer_info = xgr.TokenizerInfo.from_huggingface(
+ model_service.tokenizer,
+ vocab_size=model_service.model.config.vocab_size, # type: ignore
+ )
+ logger.debug(
+ "xgrammar TokenizerInfo created: vocab_size=%s",
+ tokenizer_info.vocab_size,
+ )
+ compiler = xgr.GrammarCompiler(tokenizer_info)
+ logger.debug("xgrammar GrammarCompiler created")
+ try:
+ compiled = compiler.compile_json_schema(json.dumps(schema))
+ logger.debug(
+ "xgrammar CompiledGrammar created: type=%s",
+ type(compiled).__name__,
+ )
+ return compiled
+ except Exception as exc:
+ logger.debug("xgrammar schema compilation failed: %s", exc)
+ raise ClientException(f"Invalid JSON schema for xgrammar ({source})") from exc
+ else:
+ logger.debug("Using lm_format_enforcer backend for constrained decoding (source=%s)", source)
+ try:
+ parser = JsonSchemaParser(schema)
+ setattr(parser, "schema", schema)
+ logger.debug("lmfe JsonSchemaParser created successfully")
+ return parser
+ except Exception as exc:
+ logger.debug("lmfe parser creation failed: %s", exc)
+ raise ClientException(f"Invalid JSON schema ({source})") from exc
+
+ def get_schema_hash(self, json_schema_parser: Optional[Any]) -> Optional[str]:
+ if json_schema_parser is None:
+ return None
+ if self.backend == LLMDecodingBackend.LM_FORMAT_ENFORCER.value:
+ schema_dict = dump_pydantic_object_to_dict(json_schema_parser.context.model_class)
+ return hashlib.sha256(json.dumps(schema_dict).encode("utf-8")).hexdigest()
+ if self.backend == LLMDecodingBackend.XGRAMMAR.value:
+ grammar = getattr(json_schema_parser, "grammar", json_schema_parser)
+ schema_json = grammar.serialize_json()
+ schema_hash = hashlib.sha256(schema_json.encode("utf-8")).hexdigest()
+ logger.debug("xgrammar schema hash computed: %s", schema_hash)
+ return schema_hash
+ if self.backend == LLMDecodingBackend.LLGUIDANCE.value:
+ schema_hash = hashlib.sha256(json_schema_parser.encode("utf-8")).hexdigest()
+ logger.debug("llguidance schema hash computed: %s", schema_hash)
+ return schema_hash
+ return None
+
+ def apply_grammar_constraint(
+ self,
+ generation_kwargs: Dict[str, Any],
+ json_schema_parser: Any,
+ tokenizer: Optional[Any] = None,
+ vocab_size: Optional[int] = None,
+ ) -> Dict[str, Any]:
+ """
+ Applies grammar constraints to the generation kwargs based on the specified backend.
+
+ Args:
+ generation_kwargs (Dict[str, Any]): The original generation keyword arguments.
+ json_schema_parser (Any): The compiled schema parser/grammar object.
+ tokenizer (Optional[Any]): The tokenizer to use for prefix allowed tokens.
+ vocab_size (Optional[int]): The vocabulary size to validate against.
+
+ Returns:
+ Dict[str, Any]: The modified generation kwargs with grammar constraints applied.
+ """
+ tokenizer = tokenizer or self.tokenizer
+ vocab_size = vocab_size if vocab_size is not None else self.vocab_size
+
+ if json_schema_parser is None:
+ return generation_kwargs
+
+ schema_hash = self.get_schema_hash(json_schema_parser)
+ cache_key = f"{self.backend}:{schema_hash}:{vocab_size}"
+ cached = self._compiled_cache.get(cache_key)
+
+ if self.backend == LLMDecodingBackend.LM_FORMAT_ENFORCER.value:
+ build_fn = self.build_transformers_prefix_allowed_tokens_fn
+ if build_fn is None:
+ raise ConfigurationException(
+ "lm-format-enforcer is required for JSON schema enforcement"
+ )
+ if cached is None:
+ cached = json_schema_parser
+ self._compiled_cache[cache_key] = cached
+ if len(self._compiled_cache) > self._compiled_cache_maxsize:
+ self._compiled_cache.pop(next(iter(self._compiled_cache)))
+ logger.debug("Applying lm-format-enforcer constrained decoding via prefix_allowed_tokens_fn")
+ generation_kwargs["prefix_allowed_tokens_fn"] = build_fn(tokenizer, cached)
+ elif self.backend == LLMDecodingBackend.XGRAMMAR.value:
+ if xgr is None:
+ raise ConfigurationException(
+ "xgrammar is required for JSON schema enforcement"
+ )
+ if not hasattr(json_schema_parser, "tokenizer_info"):
+ raise ConfigurationException(
+ "xgrammar backend requires a CompiledGrammar object with tokenizer_info"
+ )
+ if cached is None:
+ cached = json_schema_parser
+ self._compiled_cache[cache_key] = cached
+ if len(self._compiled_cache) > self._compiled_cache_maxsize:
+ self._compiled_cache.pop(next(iter(self._compiled_cache)))
+ compiled_vocab_size = cached.tokenizer_info.vocab_size
+ logger.debug(
+ "Applying xgrammar constrained decoding via LogitsProcessor "
+ "(compiled_vocab_size=%s, model_vocab_size=%s)",
+ compiled_vocab_size,
+ vocab_size,
+ )
+ if vocab_size is not None and compiled_vocab_size != vocab_size:
+ logger.warning(
+ "xgrammar vocab_size mismatch: compiled=%s vs model=%s. "
+ "This may cause AssertionErrors during generation.",
+ compiled_vocab_size,
+ vocab_size,
+ )
+ try:
+ logits_processor = xgr.contrib.hf.LogitsProcessor(cached)
+ logger.debug(
+ "xgrammar LogitsProcessor created: vocab_size=%s",
+ logits_processor.full_vocab_size,
+ )
+ generation_kwargs.setdefault("logits_processor", [])
+ generation_kwargs["logits_processor"].append(logits_processor)
+ except Exception as exc:
+ logger.error("Failed to create xgrammar LogitsProcessor: %s", exc)
+ raise ConfigurationException(
+ f"xgrammar LogitsProcessor initialization failed: {exc}. "
+ "This may indicate model/tokenizer incompatibility. "
+ "Try using lm-format-enforcer backend instead."
+ ) from exc
+ elif self.backend == LLMDecodingBackend.LLGUIDANCE.value:
+ if llguidance is None:
+ raise ConfigurationException("llguidance is required for JSON schema enforcement")
+ if tokenizer is None:
+ raise ConfigurationException("llguidance backend requires a tokenizer")
+ if cached is None:
+ cached = json_schema_parser
+ self._compiled_cache[cache_key] = cached
+ if len(self._compiled_cache) > self._compiled_cache_maxsize:
+ self._compiled_cache.pop(next(iter(self._compiled_cache)))
+ logger.debug(
+ "Applying llguidance constrained decoding via LogitsProcessor "
+ "(model_vocab_size=%s)",
+ vocab_size,
+ )
+ try:
+ logits_processor = _LLGuidanceLogitsProcessor(cached, tokenizer, vocab_size)
+ logger.debug("llguidance LogitsProcessor created")
+ generation_kwargs.setdefault("logits_processor", [])
+ generation_kwargs["logits_processor"].append(logits_processor)
+ except Exception as exc:
+ logger.error("Failed to create llguidance LogitsProcessor: %s", exc)
+ raise ConfigurationException(
+ f"llguidance LogitsProcessor initialisation failed: {exc}. "
+ "This may indicate model/tokenizer incompatibility. "
+ "Try using lm-format-enforcer or xgrammar backend instead."
+ ) from exc
+ else:
+ raise ConfigurationException(
+ f"Unsupported grammar constraint type: {type(json_schema_parser).__name__}"
+ )
+ return generation_kwargs
+
+
+class _LLGuidanceLogitsProcessor(LogitsProcessor):
+ """Wraps an llguidance LLMatcher as a transformers LogitsProcessor."""
+
+ def __init__(self, grammar: str, hf_tokenizer: Any, vocab_size: Optional[int]) -> None:
+ self._ll_tokenizer = lg_from_tokenizer(hf_tokenizer, n_vocab=vocab_size)
+ self._matcher = LLMatcher(self._ll_tokenizer, grammar)
+ self._consumed = 0
+ self._started = False
+ self._bitmask = allocate_token_bitmask(1, self._ll_tokenizer.vocab_size)
+
+ def __call__(self, input_ids: Any, scores: Any) -> Any:
+ seq = input_ids[0].tolist()
+ if not self._started:
+ self._prompt_len = len(seq)
+ self._started = True
+ self._consumed = len(seq)
+ else:
+ new_tokens = seq[self._consumed:]
+ if new_tokens:
+ self._matcher.consume_tokens(new_tokens)
+ self._consumed = len(seq)
+ bitmask = self._bitmask
+ fill_next_token_bitmask(self._matcher, bitmask)
+ if scores.device != bitmask.device:
+ bitmask = bitmask.to(scores.device)
+ apply_token_bitmask_inplace(scores, bitmask)
+ return scores
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 8f768545..f228b8e5 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -78,5 +78,7 @@ RUN /.venv/bin/python -m ensurepip && \
WORKDIR /app
EXPOSE 8000
-USER cms:cms
+
+# hadolint ignore=DL3066
+USER ${CMS_UID}:${CMS_GID}
CMD ["bash", "-c", "./entrypoint.sh"]
diff --git a/pyproject.toml b/pyproject.toml
index cdcc0003..58597a81 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -91,6 +91,9 @@ llm = [
"langchain-core~=1.2.9; python_version >= '3.11'",
"langchain-nvidia-ai-endpoints~=1.0.4; python_version >= '3.11'",
"lm-format-enforcer~=0.11.3",
+ "xgrammar~=0.1.29",
+ "llguidance<1.4.0",
+
]
mcp = [
"mcp[cli]==1.26.0",
@@ -98,7 +101,7 @@ mcp = [
"loguru~=0.7.3",
]
vllm = [
- "vllm<0.15.0",
+ "vllm>0.11.0,<0.15.0; sys_platform == 'linux'",
]
# For pip versions not supporting PEP 735
@@ -137,6 +140,8 @@ llm = [
"langchain-core~=1.2.9; python_version >= '3.11'",
"langchain-nvidia-ai-endpoints~=1.0.4; python_version >= '3.11'",
"lm-format-enforcer~=0.11.3",
+ "xgrammar~=0.1.29",
+ "llguidance<1.4.0",
]
mcp = [
"mcp[cli]==1.26.0",
@@ -144,7 +149,7 @@ mcp = [
"loguru~=0.7.3",
]
vllm = [
- "vllm<0.15.0",
+ "vllm>0.11.0,<0.15.0; sys_platform == 'linux'",
]
[tool.setuptools]
diff --git a/tests/app/api/test_serving_hf_llm.py b/tests/app/api/test_serving_hf_llm.py
index a8e924b1..db4b5d4c 100644
--- a/tests/app/api/test_serving_hf_llm.py
+++ b/tests/app/api/test_serving_hf_llm.py
@@ -19,6 +19,7 @@
config.ENABLE_EVALUATION_APIS = "true"
config.ENABLE_PREVIEWS_APIS = "true"
config.AUTH_USER_ENABLED = "false"
+config.DECODING_BACKEND = "lm_format_enforcer"
disable_rate_limits(config)
@@ -43,7 +44,6 @@ def llm_app(llm_model_service):
yield app
app.dependency_overrides.clear()
-
@pytest.fixture(scope="function")
def client(llm_model_service):
llm_model_service.model_name = "HuggingFace LLM model"
diff --git a/tests/app/model_services/test_huggingface_llm_model.py b/tests/app/model_services/test_huggingface_llm_model.py
index ce8ed2ac..7934f2d3 100644
--- a/tests/app/model_services/test_huggingface_llm_model.py
+++ b/tests/app/model_services/test_huggingface_llm_model.py
@@ -2,12 +2,19 @@
import pytest
import torch
from concurrent.futures import Future
+from types import SimpleNamespace
from unittest.mock import MagicMock, patch
from tests.app.conftest import MODEL_PARENT_DIR
from transformers import PreTrainedModel, PreTrainedTokenizerBase
from app import __version__
from app.domain import ModelType, GenerationResult
-from app.model_services.huggingface_llm_model import HuggingFaceLlmModel, TimeoutCriteria
+from app.model_services.huggingface_llm_model import (
+ HuggingFaceLlmModel,
+ TimeoutCriteria,
+ Float32LogitsProcessor,
+ VocabSafetyLogitsProcessor,
+)
+from app.processors.constrained_decoder import _LLGuidanceLogitsProcessor
from app.exception import GenerationException
@@ -96,7 +103,7 @@ def test_generate(huggingface_llm_model, ensure_full_sentences, expected_output)
prompt="Alright?",
min_tokens=50,
max_tokens=128,
- num_beams=2,
+ num_beams=1,
temperature=0.5,
top_p=0.8,
stop_sequences=["[STOP]"],
@@ -117,8 +124,8 @@ def test_generate(huggingface_llm_model, ensure_full_sentences, expected_output)
assert call_kwargs["min_new_tokens"] == 50
assert call_kwargs["max_new_tokens"] == 128
assert call_kwargs["use_cache"] is True
- assert call_kwargs["num_beams"] == 2
- assert call_kwargs["do_sample"] is False
+ assert call_kwargs["num_beams"] == 1
+ assert call_kwargs["do_sample"] is True
assert call_kwargs["temperature"] == 0.5
assert call_kwargs["top_p"] == 0.8
assert call_kwargs["repetition_penalty"] == 1.2
@@ -153,35 +160,74 @@ def test_generate_with_structured_output(huggingface_llm_model):
huggingface_llm_model.model = model
captured = {}
json_schema_parser = MagicMock()
- huggingface_llm_model._get_schema_hash = MagicMock(return_value="schema_hash")
- prefix_fn = MagicMock()
-
- def _submit(request):
- captured.update(request)
- future = Future()
- request["future"] = future
- with patch.object(
- huggingface_llm_model,
- "_build_transformers_prefix_allowed_tokens_fn",
- return_value=prefix_fn,
- ):
- model.generate(prefix_allowed_tokens_fn=prefix_fn)
- future.set_result(model.generate.return_value)
- return future
+ with patch("app.processors.constrained_decoder.ConstrainedDecoder.get_schema_hash", return_value="schema_hash"):
+ prefix_fn = MagicMock()
+
+ def _submit(request):
+ captured.update(request)
+ future = Future()
+ request["future"] = future
+ with patch.object(
+ huggingface_llm_model._constrained_decoder,
+ "build_transformers_prefix_allowed_tokens_fn",
+ return_value=prefix_fn,
+ ):
+ model.generate(prefix_allowed_tokens_fn=prefix_fn)
+ future.set_result(model.generate.return_value)
+ return future
+
+ huggingface_llm_model._micro_batch_scheduler.submit = _submit
+
+ result = huggingface_llm_model.generate(
+ prompt="This is a test prompt",
+ min_tokens=1,
+ max_tokens=2,
+ json_schema_parser=json_schema_parser,
+ )
- huggingface_llm_model._micro_batch_scheduler.submit = _submit
+ assert result.text == "Yeah."
+ assert captured["json_schema_parser"] == json_schema_parser
+ assert captured["batch_key"][-2] == "schema_hash"
+ assert model.generate.call_args.kwargs["prefix_allowed_tokens_fn"] == prefix_fn
- result = huggingface_llm_model.generate(
- prompt="This is a test prompt",
+
+@pytest.mark.parametrize("num_beams, temperature, expected_do_sample", [
+ (1, 0.0, False),
+ (1, 0.5, True),
+ (2, 0.0, False),
+ (2, 0.5, False),
+])
+def test_generate_with_or_without_sampling(huggingface_llm_model, num_beams, temperature, expected_do_sample):
+ huggingface_llm_model.init_model()
+ huggingface_llm_model._micro_batch_scheduler._batch_wait_milliseconds = 1
+ huggingface_llm_model.model = MagicMock()
+ huggingface_llm_model.tokenizer = MagicMock()
+ huggingface_llm_model._assistant_model = MagicMock()
+ huggingface_llm_model._assistant_tokenizer = MagicMock()
+ inputs = _TokenBatch(length=2)
+ huggingface_llm_model.tokenizer.return_value = inputs
+ huggingface_llm_model.tokenizer.pad_token_id = 2
+ huggingface_llm_model.tokenizer.vocab_size = 2
+ huggingface_llm_model._assistant_tokenizer.vocab_size = 2
+ outputs = [MagicMock(shape=[2])]
+ huggingface_llm_model.model.generate.return_value = outputs
+ completion_ids = MagicMock()
+ completion_ids.shape = [2]
+ outputs[0].__getitem__.return_value = completion_ids
+ huggingface_llm_model.tokenizer.decode.return_value = "Yeah."
+ huggingface_llm_model.tokenizer.apply_chat_template.return_value = "chat template text"
+
+ huggingface_llm_model.generate(
+ prompt="Alright?",
min_tokens=1,
max_tokens=2,
- json_schema_parser=json_schema_parser,
+ num_beams=num_beams,
+ temperature=temperature,
+ top_p=0.8,
)
- assert result.text == "Yeah."
- assert captured["json_schema_parser"] == json_schema_parser
- assert captured["batch_key"][-2] == "schema_hash"
- assert model.generate.call_args.kwargs["prefix_allowed_tokens_fn"] == prefix_fn
+ call_kwargs = huggingface_llm_model.model.generate.call_args.kwargs
+ assert call_kwargs["do_sample"] is expected_do_sample
@pytest.mark.parametrize("ensure_full_sentences, stream_chunks, stop_sequences, expected_output, report_called", [
@@ -204,20 +250,20 @@ async def test_generate_async(
huggingface_llm_model._assistant_tokenizer = MagicMock()
mock_send_metrics = MagicMock()
inputs = _TokenBatch(length=2)
-
+
def mock_tokenizer_call(*args, **kwargs):
if args and args[0] == "Alright?":
return inputs
return _TokenBatch(length=2)
-
+
huggingface_llm_model.tokenizer.side_effect = mock_tokenizer_call
huggingface_llm_model.tokenizer.vocab_size = 2
huggingface_llm_model._assistant_tokenizer.vocab_size = 2
streamer = FakeAsyncTextIteratorStreamer(stream_chunks)
-
+
with patch("app.model_services.huggingface_llm_model.AsyncTextIteratorStreamer", return_value=streamer):
huggingface_llm_model.model.generate.return_value = MagicMock(shape=[2])
- mock_future = MagicMock()
+ mock_future = Future()
huggingface_llm_model._text_generator.submit = MagicMock(return_value=mock_future)
results = []
@@ -274,7 +320,7 @@ def _mock_tokenizer_call(*args, **kwargs):
with patch(
"app.model_services.huggingface_llm_model.AsyncTextIteratorStreamer", return_value=streamer
) as mock_streamer:
- huggingface_llm_model._text_generator.submit = MagicMock(return_value=MagicMock())
+ huggingface_llm_model._text_generator.submit = MagicMock(return_value=Future())
results = []
async for chunk in huggingface_llm_model.generate_async(prompt="Alright?"):
if isinstance(chunk, str):
@@ -283,12 +329,122 @@ def _mock_tokenizer_call(*args, **kwargs):
submit_kwargs = huggingface_llm_model._text_generator.submit.call_args.kwargs
mock_streamer.assert_called_once()
assert "".join(results) == "OK"
- assert mock_streamer.call_args.kwargs["timeout"] == 2
+ assert mock_streamer.call_args.kwargs["timeout"] is None
assert "stopping_criteria" in submit_kwargs
assert len(submit_kwargs["stopping_criteria"]) == 1
assert isinstance(submit_kwargs["stopping_criteria"][0], TimeoutCriteria)
+@pytest.mark.asyncio
+async def test_generate_async_with_xgrammar(huggingface_llm_model):
+ huggingface_llm_model.init_model()
+ huggingface_llm_model._constrained_decoder.backend = "xgrammar"
+ huggingface_llm_model._generation_timeout_secs = 2
+ huggingface_llm_model.model = MagicMock()
+ huggingface_llm_model.model.config.vocab_size = 2
+ huggingface_llm_model.tokenizer = MagicMock()
+ huggingface_llm_model.tokenizer.pad_token_id = 2
+ inputs = _TokenBatch(length=2)
+
+ def _mock_tokenizer_call(*args, **kwargs):
+ if args and args[0] == "A 28-year-old woman presented in 1994 with musculoskeletal manifestation of systemic lupus erythematosus (SLE)":
+ return inputs
+ return _TokenBatch(length=2)
+
+ huggingface_llm_model.tokenizer.side_effect = _mock_tokenizer_call
+
+ class _FakeCompiledGrammar:
+ tokenizer_info = SimpleNamespace(vocab_size=2)
+
+ def serialize_json(self):
+ return "{}"
+
+ fake_logits_processor = SimpleNamespace(full_vocab_size=2)
+ fake_xgr = SimpleNamespace(
+ contrib=SimpleNamespace(
+ hf=SimpleNamespace(
+ LogitsProcessor=MagicMock(return_value=fake_logits_processor),
+ ),
+ ),
+ )
+ streamer = FakeAsyncTextIteratorStreamer(['{"answer": 28}'])
+ generation_future = Future()
+ generation_future.set_result(None)
+
+ with patch("app.processors.constrained_decoder.xgr", fake_xgr), patch(
+ "app.model_services.huggingface_llm_model.AsyncTextIteratorStreamer", return_value=streamer
+ ) as mock_streamer:
+ huggingface_llm_model._text_generator.submit = MagicMock(return_value=generation_future)
+ results = []
+ async for chunk in huggingface_llm_model.generate_async(
+ prompt="Alright?",
+ json_schema_parser=_FakeCompiledGrammar(),
+ ):
+ if isinstance(chunk, str):
+ results.append(chunk)
+
+ submit_kwargs = huggingface_llm_model._text_generator.submit.call_args.kwargs
+ mock_streamer.assert_called_once()
+ assert "".join(results) == '{"answer":28}'
+ assert mock_streamer.call_args.kwargs["timeout"] is None
+ assert isinstance(submit_kwargs["logits_processor"][0], VocabSafetyLogitsProcessor)
+ assert isinstance(submit_kwargs["logits_processor"][1], Float32LogitsProcessor)
+ assert submit_kwargs["logits_processor"][-1] == fake_logits_processor
+
+
+@pytest.mark.asyncio
+async def test_generate_async_with_llguidance(huggingface_llm_model):
+ huggingface_llm_model.init_model()
+ huggingface_llm_model._constrained_decoder.backend = "llguidance"
+ huggingface_llm_model._generation_timeout_secs = 2
+ huggingface_llm_model.model = MagicMock()
+ huggingface_llm_model.model.config.vocab_size = 2
+ huggingface_llm_model.tokenizer = MagicMock()
+ huggingface_llm_model.tokenizer.pad_token_id = 2
+ inputs = _TokenBatch(length=2)
+
+ def _mock_tokenizer_call(*args, **kwargs):
+ if args and args[0] == "A 28-year-old woman presented in 1994 with musculoskeletal manifestation of systemic lupus erythematosus (SLE)":
+ return inputs
+ return _TokenBatch(length=2)
+
+ huggingface_llm_model.tokenizer.side_effect = _mock_tokenizer_call
+
+ fake_llguidance = SimpleNamespace()
+ fake_lg_from_tokenizer = MagicMock(return_value=SimpleNamespace(vocab_size=2))
+ fake_ll_matcher = MagicMock()
+
+ streamer = FakeAsyncTextIteratorStreamer(['{"answer": 28}'])
+ generation_future = Future()
+ generation_future.set_result(None)
+
+ with patch("app.processors.constrained_decoder.llguidance", fake_llguidance), patch(
+ "app.processors.constrained_decoder.lg_from_tokenizer", fake_lg_from_tokenizer
+ ), patch(
+ "app.processors.constrained_decoder.LLMatcher", fake_ll_matcher
+ ), patch(
+ "app.model_services.huggingface_llm_model.AsyncTextIteratorStreamer", return_value=streamer
+ ) as mock_streamer:
+ huggingface_llm_model._text_generator.submit = MagicMock(return_value=generation_future)
+ results = []
+ async for chunk in huggingface_llm_model.generate_async(
+ prompt="Alright?",
+ json_schema_parser='{"answer": 28}',
+ ):
+ if isinstance(chunk, str):
+ results.append(chunk)
+
+ submit_kwargs = huggingface_llm_model._text_generator.submit.call_args.kwargs
+ mock_streamer.assert_called_once()
+ assert "".join(results) == '{"answer":28}'
+ assert mock_streamer.call_args.kwargs["timeout"] is None
+ assert isinstance(submit_kwargs["logits_processor"][0], VocabSafetyLogitsProcessor)
+ assert isinstance(submit_kwargs["logits_processor"][1], Float32LogitsProcessor)
+ assert isinstance(submit_kwargs["logits_processor"][-1], _LLGuidanceLogitsProcessor)
+ fake_lg_from_tokenizer.assert_called_once()
+ fake_ll_matcher.assert_called_once()
+
+
@pytest.mark.asyncio
async def test_generate_async_with_generation_exception(huggingface_llm_model):
huggingface_llm_model.init_model()
@@ -503,6 +659,7 @@ def test_load_model_quantization_check():
class FakeAsyncTextIteratorStreamer:
def __init__(self, chunks):
self._chunks = chunks
+ self._iter = iter(self._chunks)
def __aiter__(self):
self._iter = iter(self._chunks)
@@ -513,3 +670,66 @@ async def __anext__(self):
return next(self._iter)
except StopIteration:
raise StopAsyncIteration
+
+
+class TestLogitsProcessor:
+ def test_mask_out_of_range_ids(self):
+ logits = torch.zeros(1, 5)
+ logits[0, 3] = 10.0
+ logits[0, 4] = 10.0
+ processor = VocabSafetyLogitsProcessor(vocab_size=3)
+ out = processor(torch.tensor([[0]]), logits)
+ assert out[0, 0] == 0
+ assert out[0, 1] == 0
+ assert out[0, 2] == 0
+ assert out[0, 3] == -1e10
+ assert out[0, 4] == -1e10
+
+ def test_no_masking_for_logits_equal_or_smaller_than_vocab_size(self):
+ logits = torch.zeros(1, 4)
+ logits[0, 3] = 10.0
+ processor = VocabSafetyLogitsProcessor(vocab_size=4)
+ out = processor(torch.tensor([[0]]), logits)
+ assert out[0, 3] == 10.0
+
+ logits = torch.zeros(1, 3)
+ logits[0, 2] = 5.0
+ processor = VocabSafetyLogitsProcessor(vocab_size=5)
+ out = processor(torch.tensor([[0]]), logits)
+ assert out[0, 2] == 5.0
+
+ def test_no_ops_on_non_positive_vocab_size(self):
+ logits = torch.zeros(1, 4)
+ logits[0, 3] = 10.0
+ processor = VocabSafetyLogitsProcessor(vocab_size=0)
+ out = processor(torch.tensor([[0]]), logits)
+ assert out[0, 3] == 10.0
+
+ def test_cast_to_float32(self):
+ logits = torch.zeros(1, 4, dtype=torch.float16)
+ logits[0, 0] = 1.0
+ processor = Float32LogitsProcessor()
+ out = processor(torch.tensor([[0]]), logits)
+ assert out.dtype == torch.float32
+ assert out[0, 0] == 1.0
+
+ def test_replace_nan_positive_and_negative_inf(self):
+ processor = Float32LogitsProcessor()
+
+ logits = torch.zeros(1, 4)
+ logits[0, 1] = float("nan")
+ out = processor(torch.tensor([[0]]), logits)
+ assert not torch.isnan(out).any()
+ assert out[0, 1] == -1e10
+
+ logits = torch.zeros(1, 4)
+ logits[0, 2] = float("inf")
+ out = processor(torch.tensor([[0]]), logits)
+ assert not torch.isinf(out).any()
+ assert out[0, 2] == 1e10
+
+ logits = torch.zeros(1, 4)
+ logits[0, 3] = float("-inf")
+ out = processor(torch.tensor([[0]]), logits)
+ assert not torch.isinf(out).any()
+ assert out[0, 3] == -1e10
diff --git a/tests/app/processors/test_constrained_decoder.py b/tests/app/processors/test_constrained_decoder.py
new file mode 100644
index 00000000..76bed3de
--- /dev/null
+++ b/tests/app/processors/test_constrained_decoder.py
@@ -0,0 +1,279 @@
+import hashlib
+import json
+import pytest
+from types import SimpleNamespace
+from unittest.mock import MagicMock, patch
+from pydantic import BaseModel
+from app.domain import LLMDecodingBackend
+from app.exception import ClientException, ConfigurationException, ExtraDependencyRequiredException
+from app.processors.constrained_decoder import ConstrainedDecoder, _LLGuidanceLogitsProcessor
+
+
+def test_init_raises_when_no_backends():
+ with (
+ patch("app.processors.constrained_decoder.JsonSchemaParser", None),
+ patch("app.processors.constrained_decoder.build_transformers_prefix_allowed_tokens_fn", None),
+ patch("app.processors.constrained_decoder.xgr", None),
+ patch("app.processors.constrained_decoder.llguidance", None),
+ ):
+ with pytest.raises(ExtraDependencyRequiredException):
+ ConstrainedDecoder()
+
+
+def test_init_stores_backend_and_cache():
+ decoder = _make_lmfe_decoder()
+ assert decoder.backend == LLMDecodingBackend.LM_FORMAT_ENFORCER.value
+ assert decoder._compiled_cache == {}
+ assert decoder._compiled_cache_maxsize == 256
+
+
+def test_get_parser_lm_format_enforcer_invalid_schema_raises():
+ def _raise(schema):
+ raise ValueError("bad schema")
+
+ with patch("app.processors.constrained_decoder.JsonSchemaParser", _raise):
+ with pytest.raises(ClientException):
+ ConstrainedDecoder.get_parser({"type": "object"})
+
+
+def test_get_parser_xgrammar():
+ schema = {"type": "object", "properties": {"answer": {"type": "number"}}}
+ compiled = SimpleNamespace(serialize_json=lambda: "{}")
+ compiler = SimpleNamespace(compile_json_schema=MagicMock(return_value=compiled))
+ tokenizer_info = SimpleNamespace(vocab_size=2)
+ fake_xgr = SimpleNamespace(
+ TokenizerInfo=SimpleNamespace(from_huggingface=MagicMock(return_value=tokenizer_info)),
+ GrammarCompiler=MagicMock(return_value=compiler),
+ )
+ with patch("app.processors.constrained_decoder.xgr", fake_xgr):
+ result = ConstrainedDecoder.get_parser(
+ schema, model_service=_fake_model_service(), backend=LLMDecodingBackend.XGRAMMAR.value
+ )
+ assert result is compiled
+ fake_xgr.TokenizerInfo.from_huggingface.assert_called_once()
+ compiler.compile_json_schema.assert_called_once_with(json.dumps(schema))
+
+
+def test_get_parser_xgrammar_invalid_schema_raises():
+ compiler = SimpleNamespace(compile_json_schema=MagicMock(side_effect=ValueError("bad")))
+ tokenizer_info = SimpleNamespace(vocab_size=2)
+ fake_xgr = SimpleNamespace(
+ TokenizerInfo=SimpleNamespace(from_huggingface=MagicMock(return_value=tokenizer_info)),
+ GrammarCompiler=MagicMock(return_value=compiler),
+ )
+ with patch("app.processors.constrained_decoder.xgr", fake_xgr):
+ with pytest.raises(ClientException):
+ ConstrainedDecoder.get_parser(
+ {}, model_service=_fake_model_service(), backend=LLMDecodingBackend.XGRAMMAR.value
+ )
+
+
+def test_get_parser_llguidance():
+ schema = {"type": "object"}
+ grammar = '{"grammar": "grammar"}'
+ fake_llm = MagicMock(grammar_from_json_schema=MagicMock(return_value=grammar))
+ with patch("app.processors.constrained_decoder.LLMatcher", fake_llm):
+ result = ConstrainedDecoder.get_parser(
+ schema, model_service=_fake_model_service(), backend=LLMDecodingBackend.LLGUIDANCE.value
+ )
+ assert result == grammar
+ fake_llm.grammar_from_json_schema.assert_called_once_with(json.dumps(schema))
+
+
+def test_get_parser_llguidance_invalid_schema_raises():
+ fake_llm = MagicMock(grammar_from_json_schema=MagicMock(side_effect=ValueError("bad")))
+ with patch("app.processors.constrained_decoder.LLMatcher", fake_llm):
+ with pytest.raises(ClientException):
+ ConstrainedDecoder.get_parser(
+ {}, model_service=_fake_model_service(), backend=LLMDecodingBackend.LLGUIDANCE.value
+ )
+
+
+def test_get_schema_hash_lm_format_enforcer():
+ decoder = _make_lmfe_decoder()
+ parser = _FakeLmfeParser({})
+ schema_dict = parser.context.model_class.model_dump(mode="json")
+ expected = hashlib.sha256(json.dumps(schema_dict).encode("utf-8")).hexdigest()
+ assert decoder.get_schema_hash(parser) == expected
+
+
+def test_get_schema_hash_xgrammar():
+ decoder = _make_xgr_decoder()
+ grammar = SimpleNamespace(serialize_json=lambda: '{"grammar": "grammar"}')
+ expected = hashlib.sha256('{"grammar": "grammar"}'.encode("utf-8")).hexdigest()
+ assert decoder.get_schema_hash(grammar) == expected
+
+
+def test_get_schema_hash_llguidance():
+ decoder = _make_llg_decoder()
+ grammar = '{"grammar": "grammar"}'
+ expected = hashlib.sha256(grammar.encode("utf-8")).hexdigest()
+ assert decoder.get_schema_hash(grammar) == expected
+
+
+def test_get_schema_hash_unknown_backend():
+ decoder = _make_lmfe_decoder()
+ decoder.backend = "unknown"
+ assert decoder.get_schema_hash(MagicMock()) is None
+
+
+def test_apply_grammar_constraint_lmfe_sets_prefix_fn():
+ decoder = _make_lmfe_decoder()
+ prefix_fn = MagicMock()
+ build_fn = MagicMock(return_value=prefix_fn)
+ parser = _FakeLmfeParser({})
+ with patch.object(decoder, "build_transformers_prefix_allowed_tokens_fn", build_fn):
+ out = decoder.apply_grammar_constraint({"foo": "bar"}, parser, tokenizer=MagicMock(), vocab_size=10)
+ assert "prefix_allowed_tokens_fn" in out
+ assert out["prefix_allowed_tokens_fn"] is prefix_fn
+ build_fn.assert_called_once()
+
+
+def test_apply_grammar_constraint_cache_eviction():
+ decoder = _make_lmfe_decoder()
+ build_fn = MagicMock(return_value=MagicMock())
+ with patch.object(decoder, "build_transformers_prefix_allowed_tokens_fn", build_fn):
+ for i in range(300):
+ parser = _FakeLmfeParser({})
+ parser.context.model_class = _Person(name=f"name_{str(i)}", age=i)
+ decoder.apply_grammar_constraint({}, parser, tokenizer=MagicMock(), vocab_size=5)
+ assert len(decoder._compiled_cache) <= decoder._compiled_cache_maxsize
+
+
+def test_apply_grammar_constraint_xgrammar_appends_logits_processor():
+ decoder = _make_xgr_decoder()
+ fake_lp = SimpleNamespace(full_vocab_size=2)
+ fake_xgr = SimpleNamespace(
+ contrib=SimpleNamespace(hf=SimpleNamespace(LogitsProcessor=MagicMock(return_value=fake_lp)))
+ )
+ compiled = SimpleNamespace(tokenizer_info=SimpleNamespace(vocab_size=2), serialize_json=lambda: "{}")
+ with patch("app.processors.constrained_decoder.xgr", fake_xgr):
+ out = decoder.apply_grammar_constraint({}, compiled, vocab_size=2)
+ assert "logits_processor" in out
+ assert out["logits_processor"][-1] is fake_lp
+
+
+def test_apply_grammar_constraint_xgrammar_creation_failure():
+ decoder = _make_xgr_decoder()
+ fake_xgr = SimpleNamespace(
+ contrib=SimpleNamespace(hf=SimpleNamespace(LogitsProcessor=MagicMock(side_effect=RuntimeError("boom!"))))
+ )
+ compiled = SimpleNamespace(tokenizer_info=SimpleNamespace(vocab_size=2), serialize_json=lambda: "{}")
+ with patch("app.processors.constrained_decoder.xgr", fake_xgr):
+ with pytest.raises(ConfigurationException):
+ decoder.apply_grammar_constraint({}, compiled, vocab_size=2)
+
+
+def test_apply_grammar_constraint_llguidance_appends_logits_processor():
+ decoder = _make_llg_decoder()
+ grammar = '{"grammar": "grammar"}'
+ with (
+ patch("app.processors.constrained_decoder.llguidance", SimpleNamespace()),
+ patch("app.processors.constrained_decoder.lg_from_tokenizer", MagicMock(return_value=SimpleNamespace(vocab_size=2))),
+ patch("app.processors.constrained_decoder.LLMatcher", MagicMock()),
+ patch("app.processors.constrained_decoder.allocate_token_bitmask", MagicMock(return_value="bitmask")),
+ ):
+ out = decoder.apply_grammar_constraint({}, grammar, tokenizer=MagicMock(), vocab_size=2)
+ assert isinstance(out["logits_processor"][-1], _LLGuidanceLogitsProcessor)
+
+
+def test_apply_grammar_constraint_unsupported_backend():
+ decoder = _make_lmfe_decoder()
+ decoder.backend = "unsupported"
+ with pytest.raises(ConfigurationException):
+ decoder.apply_grammar_constraint({}, MagicMock())
+
+
+def test_llguidance_logits_processor_applies_bitmask():
+ scores = MagicMock()
+ scores.device = "cpu"
+ bitmask = MagicMock()
+ bitmask.device = "cpu"
+ _, mock_fill, mock_apply = _make_llguidance_processor('{"grammar": "grammar"}', scores, bitmask)
+ mock_fill.assert_called_once()
+ mock_apply.assert_called_once_with(scores, bitmask)
+
+
+def test_llguidance_logits_processor_moves_bitmask_to_scores_device():
+ scores = MagicMock()
+ scores.device = "cuda"
+ bitmask = MagicMock()
+ bitmask.device = "cpu"
+ bitmask.to.return_value = "moved"
+ _, _, mock_apply = _make_llguidance_processor('{"grammar": "grammar"}', scores, bitmask)
+ bitmask.to.assert_called_once_with(scores.device)
+ mock_apply.assert_called_once_with(scores, "moved")
+
+
+def test_llguidance_logits_processor_consumes_new_tokens():
+ scores = MagicMock()
+ scores.device = "cpu"
+ bitmask = MagicMock()
+ bitmask.device = "cpu"
+ matcher = MagicMock()
+ with (
+ patch("app.processors.constrained_decoder.lg_from_tokenizer", MagicMock(return_value=SimpleNamespace(vocab_size=2))),
+ patch("app.processors.constrained_decoder.LLMatcher", MagicMock(return_value=matcher)),
+ patch("app.processors.constrained_decoder.allocate_token_bitmask", MagicMock(return_value=bitmask)),
+ patch("app.processors.constrained_decoder.fill_next_token_bitmask"),
+ patch("app.processors.constrained_decoder.apply_token_bitmask_inplace")
+ ):
+ logits_processor = _LLGuidanceLogitsProcessor('{"grammar": "grammar"}', MagicMock(), 2)
+ logits_processor([SimpleNamespace(tolist=lambda: [5, 6, 7])], scores)
+ logits_processor([SimpleNamespace(tolist=lambda: [5, 6, 7, 8])], scores)
+ matcher.consume_tokens.assert_called_once_with([8])
+
+
+class _Person(BaseModel):
+ name: str
+ age: int
+
+
+class _FakeLmfeParser:
+ def __init__(self, schema):
+ self.schema = schema
+ self.context = SimpleNamespace(model_class=_Person(name="a", age=1))
+
+
+def _make_lmfe_decoder():
+ with (
+ patch("app.processors.constrained_decoder.JsonSchemaParser", lambda s: _FakeLmfeParser(s)),
+ patch("app.processors.constrained_decoder.build_transformers_prefix_allowed_tokens_fn", MagicMock()),
+ ):
+ return ConstrainedDecoder(backend=LLMDecodingBackend.LM_FORMAT_ENFORCER.value)
+
+
+def _make_xgr_decoder():
+ with patch("app.processors.constrained_decoder.xgr", SimpleNamespace()):
+ return ConstrainedDecoder(backend=LLMDecodingBackend.XGRAMMAR.value)
+
+
+def _make_llg_decoder():
+ with (
+ patch("app.processors.constrained_decoder.llguidance", SimpleNamespace()),
+ patch("app.processors.constrained_decoder.LLMatcher", MagicMock()),
+ patch("app.processors.constrained_decoder.allocate_token_bitmask", MagicMock())
+ ):
+ return ConstrainedDecoder(backend=LLMDecodingBackend.LLGUIDANCE.value)
+
+
+def _fake_model_service(vocab_size=2):
+ return SimpleNamespace(
+ tokenizer=MagicMock(),
+ model=SimpleNamespace(config=SimpleNamespace(vocab_size=vocab_size)),
+ )
+
+
+def _make_llguidance_processor(grammar, scores, bitmask):
+ matcher = MagicMock()
+ input_ids = [SimpleNamespace(tolist=lambda: [5, 6, 7])]
+ with (
+ patch("app.processors.constrained_decoder.lg_from_tokenizer", MagicMock(return_value=SimpleNamespace(vocab_size=2))),
+ patch("app.processors.constrained_decoder.LLMatcher", MagicMock(return_value=matcher)),
+ patch("app.processors.constrained_decoder.allocate_token_bitmask", MagicMock(return_value=bitmask)),
+ patch("app.processors.constrained_decoder.fill_next_token_bitmask") as mock_fill,
+ patch("app.processors.constrained_decoder.apply_token_bitmask_inplace") as mock_apply,
+ ):
+ logits_processor = _LLGuidanceLogitsProcessor(grammar, MagicMock(), 2)
+ logits_processor(input_ids, scores)
+ return logits_processor, mock_fill, mock_apply
diff --git a/tests/integration/steps/test_steps.py b/tests/integration/steps/test_steps.py
index 144c1f0c..8be3c07d 100644
--- a/tests/integration/steps/test_steps.py
+++ b/tests/integration/steps/test_steps.py
@@ -100,12 +100,12 @@ def send_post_request_jsonlines(context, request):
@when(data_table("I send a POST request with the following content where data as a file", fixture="request", orient="dict"))
def send_post_request_file(context, request):
- with tempfile.NamedTemporaryFile(mode="w+") as f:
- f.write(request[0]["data"])
+ with tempfile.NamedTemporaryFile(mode="w+b") as f:
+ f.write(request[0]["data"].encode("utf-8"))
f.seek(0)
context["response"] = requests.post(
f"{context['base_url']}{request[0]['endpoint']}",
- files=[("multi_text_file", f)],
+ files=[("multi_text_file", ("multi_text_file", f.read()))],
)
@then("the response should contain json lines")
@@ -295,4 +295,4 @@ def check_response_embeddings(context):
assert len(response_json["data"]) > 0
assert "embedding" in response_json["data"][0]
assert isinstance(response_json["data"][0]["embedding"], list)
- context["response"].close()
+ context["response"].close()
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
index 3ffc0fd8..2c8b03e8 100644
--- a/uv.lock
+++ b/uv.lock
@@ -23,7 +23,8 @@ dependencies = [
{ name = "psutil" },
{ name = "pyyaml" },
{ name = "safetensors" },
- { name = "torch" },
+ { name = "torch", version = "2.9.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'win32'" },
+ { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'win32'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/4a/8e/ac2a9566747a93f8be36ee08532eb0160558b07630a081a6056a9f89bf1d/accelerate-1.12.0.tar.gz", hash = "sha256:70988c352feb481887077d2ab845125024b2a137a5090d6d7a32b57d03a45df6", size = 398399, upload-time = "2025-11-21T11:27:46.973Z" }
wheels = [
@@ -177,14 +178,14 @@ name = "anthropic"
version = "0.71.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
- { name = "anyio" },
- { name = "distro" },
- { name = "docstring-parser" },
- { name = "httpx" },
- { name = "jiter" },
- { name = "pydantic" },
- { name = "sniffio" },
- { name = "typing-extensions" },
+ { name = "anyio", marker = "sys_platform != 'win32'" },
+ { name = "distro", marker = "sys_platform != 'win32'" },
+ { name = "docstring-parser", marker = "sys_platform != 'win32'" },
+ { name = "httpx", marker = "sys_platform != 'win32'" },
+ { name = "jiter", marker = "sys_platform != 'win32'" },
+ { name = "pydantic", marker = "sys_platform != 'win32'" },
+ { name = "sniffio", marker = "sys_platform != 'win32'" },
+ { name = "typing-extensions", marker = "sys_platform != 'win32'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/82/4f/70682b068d897841f43223df82d96ec1d617435a8b759c4a2d901a50158b/anthropic-0.71.0.tar.gz", hash = "sha256:eb8e6fa86d049061b3ef26eb4cbae0174ebbff21affa6de7b3098da857d8de6a", size = 489102, upload-time = "2025-10-16T15:54:40.08Z" }
wheels = [
@@ -210,28 +211,22 @@ name = "apache-tvm-ffi"
version = "0.1.12"
source = { registry = "https://pypi.org/simple" }
dependencies = [
- { name = "typing-extensions" },
+ { name = "typing-extensions", marker = "sys_platform != 'win32'" },
]
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