Merge tag 'v0.3.3' into vllm-v0.3.2-dtk23.10 and add gfx

tests/lora/test_gemma.py

+import vllm
+from vllm.lora.request import LoRARequest
+
+MODEL_PATH = "google/gemma-7b"
+
+
+def do_sample(llm, lora_path: str, lora_id: int) -> str:
+    prompts = [
+        "Quote: Imagination is",
+        "Quote: Be yourself;",
+        "Quote: So many books,",
+    ]
+    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=32)
+    outputs = llm.generate(
+        prompts,
+        sampling_params,
+        lora_request=LoRARequest(str(lora_id), lora_id, lora_path)
+        if lora_id else None)
+    # Print the outputs.
+    generated_texts = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text.strip()
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    return generated_texts
+
+
+def test_gemma_lora(gemma_lora_files):
+    llm = vllm.LLM(MODEL_PATH,
+                   max_model_len=1024,
+                   enable_lora=True,
+                   max_loras=4)
+
+    expected_lora_output = [
+        "more important than knowledge.\nAuthor: Albert Einstein\n",
+        "everyone else is already taken.\nAuthor: Oscar Wilde\n",
+        "so little time\nAuthor: Frank Zappa\n",
+    ]
+
+    output1 = do_sample(llm, gemma_lora_files, lora_id=1)
+    for i in range(len(expected_lora_output)):
+        assert output1[i].startswith(expected_lora_output[i])
+    output2 = do_sample(llm, gemma_lora_files, lora_id=2)
+    for i in range(len(expected_lora_output)):
+        assert output2[i].startswith(expected_lora_output[i])

tests/lora/test_layers.py

@@ -279,7 +279,7 @@ def test_embeddings_with_new_embeddings(dist_init, num_loras, device) -> None:
             256,
             org_num_embeddings=512)
         expanded_embedding.weight.data[:512, :] = embedding_data
-        # We need to deepcopy the embedding as it will be modifed
+        # We need to deepcopy the embedding as it will be modified
         # in place
         lora_embedding = VocabParallelEmbeddingWithLoRA(
             deepcopy(expanded_embedding))

tests/lora/test_llama.py

@@ -15,7 +15,7 @@ def do_sample(llm, lora_path: str, lora_id: int):
         "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_95 (one_mora VARCHAR, gloss VARCHAR, accented_mora VARCHAR)\n\n question: What is the one mora for a low tone mora with a gloss of /˩okiru/ [òkìɽɯ́]? [/user] [assistant]",
         "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE candidate (people_id VARCHAR, unsure_rate INTEGER); CREATE TABLE people (sex VARCHAR, people_id VARCHAR)\n\n question: which gender got the highest average uncertain ratio. [/user] [assistant]",
         "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_60 (pick INTEGER, former_wnba_team VARCHAR)\n\n question: What pick was a player that previously played for the Minnesota Lynx? [/user] [assistant]",
-        "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the womens doubles for werner schlager [/user] [assistant]"
+        "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]"
     ]
     sampling_params = vllm.SamplingParams(temperature=0,
                                           max_tokens=256,
@@ -53,7 +53,7 @@ def test_llama_lora(sql_lora_files, tp_size):
         "\n\n answer: 1\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_96 (one_mora VARCHAR, gloss VARCHAR, accented_mora VARCHAR)\n\n question: What is the one mora for a high tone mora with a gloss of /˧kot/ [kòt]? [/user] [assistant]\n\n answer: 2\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_97 (one_mora VARCHAR, gloss VARCHAR, accented_mora VARCHAR)\n\n question: What is the one mora for a high tone mora with a gloss of /˧kot/ [kòt]? [/user] [assistant]\n\n answer: 2\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_98 (one_mora VARCHAR, gloss VARCHAR, accented_mora VARCHAR)\n\n question: What is the one m",
         " Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE candidate (people_id VARCHAR, unsure_rate INTEGER); CREATE TABLE people (sex VARCHAR, people_id VARCHAR)\n\n question: which gender got the highest average uncertain ratio. ",
         " Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_60 (pick INTEGER, former_wnba_team VARCHAR)\n\n question: What pick was a player that previously played for the Minnesota Lynx? ",
-        "\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the womens doubles for werner schlager [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the womens doubles for werner schlager [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the womens doubles for werner schlager [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE",
+        "\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE",
     ]
     expected_lora_output = [
         "  SELECT icao FROM table_name_74 WHERE airport = 'lilongwe international airport' ",

tests/lora/test_punica.py

@@ -44,8 +44,8 @@ def _lora_ref_impl(
 
 H1 = H2 = [
     128, 256, 512, 1024, 1280, 2048, 2560, 2752, 3072, 3456, 3584, 4096, 5120,
-    5504, 5632, 6912, 7168, 8192, 9216, 10240, 11008, 13824, 14336, 32000,
-    32256, 32512, 32768, 33024
+    5504, 5632, 6144, 6912, 7168, 8192, 9216, 10240, 11008, 13824, 14336,
+    24576, 32000, 32256, 32512, 32768, 33024
 ]
 SEED = [0xabcdabcd987]
 

tests/metrics/test_metrics.py

 import pytest
-import vllm.engine.metrics
 
 MODELS = [
     "facebook/opt-125m",
@@ -9,14 +8,17 @@ MODELS = [
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["float"])
 @pytest.mark.parametrize("max_tokens", [128])
-def test_metrics(
+def test_metric_counter_prompt_tokens(
     vllm_runner,
     example_prompts,
     model: str,
     dtype: str,
     max_tokens: int,
 ) -> None:
-    vllm_model = vllm_runner(model, dtype=dtype, disable_log_stats=False)
+    vllm_model = vllm_runner(model,
+                             dtype=dtype,
+                             disable_log_stats=False,
+                             gpu_memory_utilization=0.4)
     tokenizer = vllm_model.model.get_tokenizer()
     prompt_token_counts = [len(tokenizer.encode(p)) for p in example_prompts]
     # This test needs at least 2 prompts in a batch of different lengths to verify their token count is correct despite padding.
@@ -26,8 +28,41 @@ def test_metrics(
     vllm_prompt_token_count = sum(prompt_token_counts)
 
     _ = vllm_model.generate_greedy(example_prompts, max_tokens)
-    metric_count = vllm.engine.metrics.counter_prompt_tokens.get_value({})
+    stat_logger = vllm_model.model.llm_engine.stat_logger
+    metric_count = stat_logger.metrics.counter_prompt_tokens.labels(
+        **stat_logger.labels)._value.get()
 
     assert vllm_prompt_token_count == metric_count, (
         f"prompt token count: {vllm_prompt_token_count!r}\nmetric: {metric_count!r}"
     )
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("max_tokens", [128])
+def test_metric_counter_generation_tokens(
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+) -> None:
+    vllm_model = vllm_runner(model,
+                             dtype=dtype,
+                             disable_log_stats=False,
+                             gpu_memory_utilization=0.4)
+    vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+    tokenizer = vllm_model.model.get_tokenizer()
+    stat_logger = vllm_model.model.llm_engine.stat_logger
+    metric_count = stat_logger.metrics.counter_generation_tokens.labels(
+        **stat_logger.labels)._value.get()
+    vllm_generation_count = 0
+    for i in range(len(example_prompts)):
+        vllm_output_ids, vllm_output_str = vllm_outputs[i]
+        prompt_ids = tokenizer.encode(example_prompts[i])
+        # vllm_output_ids contains both prompt tokens and generation tokens. We're interested only in the count of the generation tokens.
+        vllm_generation_count += len(vllm_output_ids) - len(prompt_ids)
+
+    assert vllm_generation_count == metric_count, (
+        f"generation token count: {vllm_generation_count!r}\nmetric: {metric_count!r}"
+    )

tests/models/test_marlin.py

+"""Compare the outputs of a GPTQ model to a Marlin model.
+
+Note: GPTQ and Marlin do not have bitwise correctness. 
+As a result, in this test, we just confirm that the top selected tokens of the 
+Marlin/GPTQ models are in the top 3 selections of each other.
+
+Note: Marlin internally uses locks to synchronize the threads. This can
+result in very slight nondeterminism for Marlin. As a result, we re-run the test
+up to 3 times to see if we pass.
+
+Run `pytest tests/models/test_marlin.py --forked`.
+"""
+
+import pytest
+import torch
+from dataclasses import dataclass
+from vllm.model_executor.layers.quantization import _QUANTIZATION_CONFIG_REGISTRY
+
+capability = torch.cuda.get_device_capability()
+capability = capability[0] * 10 + capability[1]
+marlin_not_supported = (
+    capability < _QUANTIZATION_CONFIG_REGISTRY["marlin"].get_min_capability())
+
+
+@dataclass
+class ModelPair:
+    model_marlin: str
+    model_gptq: str
+
+
+model_pairs = [
+    ModelPair(model_marlin="nm-testing/zephyr-beta-7b-marlin-g128",
+              model_gptq="nm-testing/zephyr-beta-7b-gptq-g128"),
+    ModelPair(model_marlin="robertgshaw2/zephyr-7b-beta-channelwise-marlin",
+              model_gptq="robertgshaw2/zephyr-7b-beta-channelwise-gptq"),
+    ModelPair(model_marlin="robertgshaw2/TinyLlama-1.1B-Chat-v1.0-g128-marlin",
+              model_gptq="robertgshaw2/TinyLlama-1.1B-Chat-v1.0-g128-gptq")
+]
+
+
+@pytest.mark.flaky(reruns=2)
+@pytest.mark.skipif(marlin_not_supported,
+                    reason="Marlin is not supported on this GPU type.")
+@pytest.mark.parametrize("model_pair", model_pairs)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("num_logprobs", [3])
+def test_models(
+    vllm_runner,
+    example_prompts,
+    model_pair: ModelPair,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+) -> None:
+    marlin_model = vllm_runner(model_pair.model_marlin, dtype=dtype)
+    marlin_outputs = marlin_model.generate_greedy_logprobs(
+        example_prompts, max_tokens, num_logprobs)
+
+    # Note: not sure why, but deleting just the model on Ada Lovelace
+    #   does not free the GPU memory. On Ampere, deleting the just model
+    #   frees the memory.
+    del marlin_model.model.llm_engine.driver_worker
+    del marlin_model
+
+    gptq_model = vllm_runner(model_pair.model_gptq, dtype=dtype)
+    gptq_outputs = gptq_model.generate_greedy_logprobs(example_prompts,
+                                                       max_tokens,
+                                                       num_logprobs)
+
+    # Note: not sure why, but deleting just the model on Ada Lovelace
+    #   does not free the GPU memory. On Ampere, deleting the just model
+    #   frees the memory.
+    del gptq_model.model.llm_engine.driver_worker
+    del gptq_model
+
+    # loop through the prompts
+    for prompt_idx in range(len(example_prompts)):
+        gptq_output_ids, gptq_output_str, gptq_logprobs = gptq_outputs[
+            prompt_idx]
+        marlin_output_ids, marlin_output_str, marlin_logprobs = marlin_outputs[
+            prompt_idx]
+
+        for idx, (gptq_output_id, marlin_output_id) in enumerate(
+                zip(gptq_output_ids, marlin_output_ids)):
+            # If sequence is not an exact match,
+            if marlin_output_id != gptq_output_id:
+                # Each predicted token must be in top 5 of the other's
+                assert gptq_output_id in marlin_logprobs[idx], (
+                    f"Test{prompt_idx}:\nGPTQ:\t{gptq_output_str!r}\nMarlin:\t{marlin_output_str!r}"
+                )
+                assert marlin_output_id in gptq_logprobs[idx], (
+                    f"Test{prompt_idx}:\nGPTQ:\t{gptq_output_str!r}\nMarlin:\t{marlin_output_str!r}"
+                )
+
+                # Break out since sequences will now diverge.
+                break

tests/models/test_models.py

@@ -19,6 +19,7 @@ MODELS = [
     "microsoft/phi-2",
     "stabilityai/stablelm-3b-4e1t",
     "allenai/OLMo-1B",
+    "bigcode/starcoder2-3b",
 ]
 
 

vllm/__init__.py

@@ -9,7 +9,7 @@ from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.sampling_params import SamplingParams
 from vllm.version import __dcu_version__
 
-__version__ = "0.3.2"
+__version__ = "0.3.3"
 
 __all__ = [
     "LLM",

vllm/config.py

@@ -8,7 +8,7 @@ from transformers import PretrainedConfig
 
 from vllm.logger import init_logger
 from vllm.transformers_utils.config import get_config
-from vllm.utils import get_cpu_memory, is_hip, get_nvcc_cuda_version
+from vllm.utils import get_cpu_memory, is_hip, is_neuron, get_nvcc_cuda_version
 
 logger = init_logger(__name__)
 
@@ -155,15 +155,21 @@ class ModelConfig:
         self.tokenizer_mode = tokenizer_mode
 
     def _verify_quantization(self) -> None:
-        supported_quantization = ["awq", "gptq", "squeezellm"]
-        rocm_not_supported_quantization = ["awq"]
+        supported_quantization = ["awq", "gptq", "squeezellm", "marlin"]
+        rocm_not_supported_quantization = ["awq", "marlin"]
         if self.quantization is not None:
             self.quantization = self.quantization.lower()
 
         # Parse quantization method from the HF model config, if available.
         hf_quant_config = getattr(self.hf_config, "quantization_config", None)
         if hf_quant_config is not None:
+
             hf_quant_method = str(hf_quant_config["quant_method"]).lower()
+            # If the GPTQ model is serialized in marlin format, use marlin.
+            if (hf_quant_method == "gptq"
+                    and "is_marlin_format" in hf_quant_config
+                    and hf_quant_config["is_marlin_format"]):
+                hf_quant_method = "marlin"
             if self.quantization is None:
                 self.quantization = hf_quant_method
             elif self.quantization != hf_quant_method:
@@ -183,7 +189,9 @@ class ModelConfig:
                 raise ValueError(
                     f"{self.quantization} quantization is currently not supported "
                     f"in ROCm.")
-            logger.warning(f"{self.quantization} quantization is not fully "
+            if self.quantization != "marlin":
+                logger.warning(
+                    f"{self.quantization} quantization is not fully "
                     "optimized yet. The speed can be slower than "
                     "non-quantized models.")
 
@@ -308,6 +316,10 @@ class CacheConfig:
         self.num_gpu_blocks = None
         self.num_cpu_blocks = None
 
+    def metrics_info(self):
+        # convert cache_config to dict(key: str, value:str) for prometheus metrics info
+        return {key: str(value) for key, value in self.__dict__.items()}
+
     def _verify_args(self) -> None:
         if self.gpu_memory_utilization > 1.0:
             raise ValueError(
@@ -319,7 +331,7 @@ class CacheConfig:
             pass
         elif self.cache_dtype == "fp8_e5m2":
             nvcc_cuda_version = get_nvcc_cuda_version()
-            if nvcc_cuda_version < Version("11.8"):
+            if nvcc_cuda_version and nvcc_cuda_version < Version("11.8"):
                 raise ValueError(
                     "FP8 is not supported when cuda version is lower than 11.8."
                 )
@@ -380,13 +392,21 @@ class ParallelConfig:
         disable_custom_all_reduce: bool = False,
     ) -> None:
         self.pipeline_parallel_size = pipeline_parallel_size
+        if is_neuron():
+            # For Neuron device support, here we assign TP=1 to avoid sharding within vLLM directly.
+            # Transformer-neuronx would take neuron_tp_degree attribute, and distribute the workload
+            # to multiple NeuronCores.
+            self.tensor_parallel_size = 1
+            self.neuron_tp_degree = tensor_parallel_size
+        else:
             self.tensor_parallel_size = tensor_parallel_size
         self.worker_use_ray = worker_use_ray
         self.max_parallel_loading_workers = max_parallel_loading_workers
         self.disable_custom_all_reduce = disable_custom_all_reduce
 
-        self.world_size = pipeline_parallel_size * tensor_parallel_size
-        if self.world_size > 1:
+        self.world_size = pipeline_parallel_size * self.tensor_parallel_size
+        # Ray worker is not supported for Neuron backend.
+        if self.world_size > 1 and not is_neuron():
             self.worker_use_ray = True
         self._verify_args()
 
@@ -465,8 +485,29 @@ class SchedulerConfig:
 
 class DeviceConfig:
 
-    def __init__(self, device: str = "cuda") -> None:
-        self.device = torch.device(device)
+    def __init__(self, device: str = "auto") -> None:
+        if device == "auto":
+            # Automated device type detection
+            if torch.cuda.is_available():
+                self.device_type = "cuda"
+            elif is_neuron():
+                self.device_type = "neuron"
+            else:
+                raise RuntimeError("No supported device detected.")
+        else:
+            # Device type is assigned explicitly
+            self.device_type = device
+
+        # Some device types require processing inputs on CPU
+        if self.device_type in ["neuron"]:
+            self.device = torch.device("cpu")
+        else:
+            # Set device with device type
+            self.device = torch.device(self.device_type)
+
+    @property
+    def is_neuron(self):
+        return self.device_type == "neuron"
 
 
 @dataclass

vllm/core/block_manager.py

@@ -178,7 +178,7 @@ class BlockSpaceManager:
         if len(block_table) < len(logical_blocks):
             if (self.block_sliding_window
                     and len(block_table) >= self.block_sliding_window):
-                # re-use a block
+                # reuse a block
                 block_table.append(block_table[len(block_table) %
                                                self.block_sliding_window])
             else:

vllm/core/scheduler.py

@@ -158,7 +158,7 @@ class Scheduler:
         return len(self.waiting) + len(self.running) + len(self.swapped)
 
     def _schedule(self) -> SchedulerOutputs:
-        # Blocks that need to be swaped or copied before model execution.
+        # Blocks that need to be swapped or copied before model execution.
         blocks_to_swap_in: Dict[int, int] = {}
         blocks_to_swap_out: Dict[int, int] = {}
         blocks_to_copy: Dict[int, List[int]] = {}

vllm/engine/arg_utils.py

@@ -44,7 +44,7 @@ class EngineArgs:
     lora_extra_vocab_size: int = 256
     lora_dtype = 'auto'
     max_cpu_loras: Optional[int] = None
-    device: str = 'cuda'
+    device: str = 'auto'
 
     def __post_init__(self):
         if self.tokenizer is None:
@@ -171,7 +171,7 @@ class EngineArgs:
         parser.add_argument('--block-size',
                             type=int,
                             default=EngineArgs.block_size,
-                            choices=[8, 16, 32],
+                            choices=[8, 16, 32, 128],
                             help='token block size')
         parser.add_argument('--seed',
                             type=int,
@@ -264,13 +264,11 @@ class EngineArgs:
             help=('Maximum number of LoRAs to store in CPU memory. '
                   'Must be >= than max_num_seqs. '
                   'Defaults to max_num_seqs.'))
-        parser.add_argument(
-            "--device",
+        parser.add_argument("--device",
                             type=str,
                             default=EngineArgs.device,
-            choices=["cuda"],
-            help=('Device type for vLLM execution. '
-                  'Currently, only CUDA-compatible devices are supported.'))
+                            choices=["auto", "cuda", "neuron"],
+                            help='Device type for vLLM execution.')
         return parser
 
     @classmethod

vllm/engine/async_llm_engine.py

@@ -333,6 +333,9 @@ class AsyncLLMEngine:
         return (self.background_loop is not None
                 and not self.background_loop.done())
 
+    def get_tokenizer(self):
+        return self.engine.tokenizer.tokenizer
+
     def start_background_loop(self) -> None:
         """Start the background loop."""
         if self.is_running:

vllm/engine/llm_engine.py

@@ -3,6 +3,7 @@ from collections import defaultdict
 import os
 import time
 import pickle
+import importlib
 from typing import (TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple,
                     Union)
 
@@ -20,7 +21,8 @@ from vllm.sequence import (SamplerOutput, Sequence, SequenceGroup,
                            SequenceGroupOutput, SequenceOutput, SequenceStatus)
 from vllm.transformers_utils.tokenizer import (detokenize_incrementally,
                                                TokenizerGroup)
-from vllm.utils import Counter, set_cuda_visible_devices, get_ip, get_open_port, get_distributed_init_method
+from vllm.utils import (Counter, set_cuda_visible_devices, get_ip,
+                        get_open_port, get_distributed_init_method)
 
 if ray:
     from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
@@ -31,6 +33,12 @@ if TYPE_CHECKING:
 logger = init_logger(__name__)
 _LOCAL_LOGGING_INTERVAL_SEC = 5
 
+# A map between the device type (in device config) to its worker module.
+DEVICE_TO_WORKER_MODULE_MAP = {
+    "cuda": "vllm.worker.worker",
+    "neuron": "vllm.worker.neuron_worker",
+}
+
 # If the env var is set, it uses the Ray's compiled DAG API
 # which optimizes the control plane overhead.
 # Run VLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
@@ -128,7 +136,9 @@ class LLMEngine:
         # Metric Logging.
         if self.log_stats:
             self.stat_logger = StatLogger(
-                local_interval=_LOCAL_LOGGING_INTERVAL_SEC)
+                local_interval=_LOCAL_LOGGING_INTERVAL_SEC,
+                labels=dict(model_name=model_config.model))
+            self.stat_logger.info("cache_config", self.cache_config)
 
         self.forward_dag = None
         if USE_RAY_COMPILED_DAG:
@@ -137,10 +147,17 @@ class LLMEngine:
     def get_tokenizer_for_seq(self, sequence: Sequence):
         return self.tokenizer.get_lora_tokenizer(sequence.lora_request)
 
+    def _dispatch_worker(self):
+        worker_module = DEVICE_TO_WORKER_MODULE_MAP[
+            self.device_config.device_type]
+        imported_worker = importlib.import_module(worker_module)
+        Worker = imported_worker.Worker
+        return Worker
+
     def _init_workers(self):
         # Lazy import the Worker to avoid importing torch.cuda/xformers
         # before CUDA_VISIBLE_DEVICES is set in the Worker
-        from vllm.worker.worker import Worker
+        Worker = self._dispatch_worker()
 
         assert self.parallel_config.world_size == 1, (
             "Ray is required if parallel_config.world_size > 1.")
@@ -242,7 +259,7 @@ class LLMEngine:
 
         # Lazy import the Worker to avoid importing torch.cuda/xformers
         # before CUDA_VISIBLE_DEVICES is set in the Worker
-        from vllm.worker.worker import Worker
+        Worker = self._dispatch_worker()
 
         # Initialize torch distributed process group for the workers.
         model_config = copy.deepcopy(self.model_config)
@@ -283,7 +300,10 @@ class LLMEngine:
             is_driver_worker=True,
         )
 
-        self._run_workers("init_model", cupy_port=get_open_port())
+        # don't use cupy for eager mode
+        self._run_workers("init_model",
+                          cupy_port=get_open_port()
+                          if not model_config.enforce_eager else None)
         self._run_workers(
             "load_model",
             max_concurrent_workers=self.parallel_config.
@@ -464,8 +484,9 @@ class LLMEngine:
             prompt_token_ids[:prefix_pos], lora_request.lora_int_id
             if lora_request else 0) if prefix_pos is not None else None
 
-        # Defensive copy of SamplingParams, which are used by the sampler
-        sampling_params = copy.deepcopy(sampling_params)
+        # Defensive copy of SamplingParams, which are used by the sampler,
+        # this doesn't deep-copy LogitsProcessor objects
+        sampling_params = sampling_params.clone()
 
         # Create the sequence group.
         seq_group = SequenceGroup(request_id, [seq], sampling_params,
@@ -872,6 +893,9 @@ class LLMEngine:
                 num_prompt_tokens = sum(
                     len(seq_group.prompt_token_ids)
                     for seq_group in scheduler_outputs.scheduled_seq_groups)
+                num_generation_tokens = sum(
+                    seq_group.num_seqs()
+                    for seq_group in scheduler_outputs.scheduled_seq_groups)
             else:
                 num_generation_tokens = scheduler_outputs.num_batched_tokens
 
@@ -956,7 +980,10 @@ class LLMEngine:
     def _finalize_sequence(self, seq: Sequence,
                            sampling_params: SamplingParams,
                            stop_string: str) -> None:
-        if not sampling_params.include_stop_str_in_output and stop_string:
+        if sampling_params.include_stop_str_in_output:
+            return
+
+        if stop_string and seq.output_text.endswith(stop_string):
             # Truncate the output text so that the stop string is
             # not included in the output.
             seq.output_text = seq.output_text[:-len(stop_string)]

vllm/engine/metrics.py

 from vllm.logger import init_logger
-from aioprometheus import Counter, Gauge, Histogram
+from prometheus_client import Counter, Gauge, Histogram, Info, REGISTRY, disable_created_metrics
 
 import time
 import numpy as np
-from typing import List
+from typing import Dict, List
 from dataclasses import dataclass
 
 logger = init_logger(__name__)
 
-labels = {}
-
-
-def add_global_metrics_labels(**kwargs):
-    labels.update(kwargs)
-
+disable_created_metrics()
 
 # The begin-* and end* here are used by the documentation generator
 # to extract the metrics definitions.
 
+
 # begin-metrics-definitions
-gauge_avg_prompt_throughput = Gauge("vllm:avg_prompt_throughput_toks_per_s",
-                                    "Average prefill throughput in tokens/s.")
-gauge_avg_generation_throughput = Gauge(
-    "vllm:avg_generation_throughput_toks_per_s",
-    "Average generation throughput in tokens/s.")
-counter_prompt_tokens = Counter("vllm:prompt_tokens_total",
-                                "Number of prefill tokens processed.")
-counter_generation_tokens = Counter("vllm:generation_tokens_total",
-                                    "Number of generation tokens processed.")
-
-gauge_scheduler_running = Gauge(
-    "vllm:num_requests_running",
-    "Number of requests currently running on GPU.")
-gauge_scheduler_swapped = Gauge("vllm:num_requests_swapped",
-                                "Number of requests swapped to CPU.")
-gauge_scheduler_waiting = Gauge("vllm:num_requests_waiting",
-                                "Number of requests waiting to be processed.")
-
-gauge_gpu_cache_usage = Gauge(
-    "vllm:gpu_cache_usage_perc",
-    "GPU KV-cache usage. 1 means 100 percent usage.")
-gauge_cpu_cache_usage = Gauge(
-    "vllm:cpu_cache_usage_perc",
-    "CPU KV-cache usage. 1 means 100 percent usage.")
-
-histogram_time_to_first_token = Histogram(
-    "vllm:time_to_first_token_seconds",
-    "Histogram of time to first token in seconds.",
+class Metrics:
+
+    def __init__(self, labelnames: List[str]):
+        # Unregister any existing vLLM collectors
+        for collector in list(REGISTRY._collector_to_names):
+            if hasattr(collector, "_name") and "vllm" in collector._name:
+                REGISTRY.unregister(collector)
+
+        self.info_cache_config = Info(
+            name='vllm:cache_config',
+            documentation='information of cache_config')
+
+        # System stats
+        self.gauge_scheduler_running = Gauge(
+            name="vllm:num_requests_running",
+            documentation="Number of requests currently running on GPU.",
+            labelnames=labelnames)
+        self.gauge_scheduler_swapped = Gauge(
+            name="vllm:num_requests_swapped",
+            documentation="Number of requests swapped to CPU.",
+            labelnames=labelnames)
+        self.gauge_scheduler_waiting = Gauge(
+            name="vllm:num_requests_waiting",
+            documentation="Number of requests waiting to be processed.",
+            labelnames=labelnames)
+        self.gauge_gpu_cache_usage = Gauge(
+            name="vllm:gpu_cache_usage_perc",
+            documentation="GPU KV-cache usage. 1 means 100 percent usage.",
+            labelnames=labelnames)
+        self.gauge_cpu_cache_usage = Gauge(
+            name="vllm:cpu_cache_usage_perc",
+            documentation="CPU KV-cache usage. 1 means 100 percent usage.",
+            labelnames=labelnames)
+
+        # Raw stats from last model iteration
+        self.counter_prompt_tokens = Counter(
+            name="vllm:prompt_tokens_total",
+            documentation="Number of prefill tokens processed.",
+            labelnames=labelnames)
+        self.counter_generation_tokens = Counter(
+            name="vllm:generation_tokens_total",
+            documentation="Number of generation tokens processed.",
+            labelnames=labelnames)
+        self.histogram_time_to_first_token = Histogram(
+            name="vllm:time_to_first_token_seconds",
+            documentation="Histogram of time to first token in seconds.",
+            labelnames=labelnames,
             buckets=[
-        0.001, 0.005, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.25, 0.5, 0.75, 1.0,
-        2.5, 5.0, 7.5, 10.0
+                0.001, 0.005, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.25, 0.5,
+                0.75, 1.0, 2.5, 5.0, 7.5, 10.0
             ])
-histogram_time_per_output_tokens = Histogram(
-    "vllm:time_per_output_token_seconds",
-    "Histogram of time per output token in seconds.",
+        self.histogram_time_per_output_token = Histogram(
+            name="vllm:time_per_output_token_seconds",
+            documentation="Histogram of time per output token in seconds.",
+            labelnames=labelnames,
             buckets=[
-        0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75, 1.0, 2.5
+                0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75,
+                1.0, 2.5
             ])
-histogram_e2e_request_latency = Histogram(
-    "vllm:e2e_request_latency_seconds",
-    "Histogram of end to end request latency in seconds.",
+        self.histogram_e2e_request_latency = Histogram(
+            name="vllm:e2e_request_latency_seconds",
+            documentation="Histogram of end to end request latency in seconds.",
+            labelnames=labelnames,
             buckets=[1.0, 2.5, 5.0, 10.0, 15.0, 20.0, 30.0, 40.0, 50.0, 60.0])
+
+        # Legacy metrics
+        self.gauge_avg_prompt_throughput = Gauge(
+            name="vllm:avg_prompt_throughput_toks_per_s",
+            documentation="Average prefill throughput in tokens/s.",
+            labelnames=labelnames,
+        )
+        self.gauge_avg_generation_throughput = Gauge(
+            name="vllm:avg_generation_throughput_toks_per_s",
+            documentation="Average generation throughput in tokens/s.",
+            labelnames=labelnames,
+        )
+
+
 # end-metrics-definitions
 
 
@@ -87,7 +119,7 @@ class Stats:
 class StatLogger:
     """StatLogger is used LLMEngine to log to Promethus and Stdout."""
 
-    def __init__(self, local_interval: float) -> None:
+    def __init__(self, local_interval: float, labels: Dict[str, str]) -> None:
         # Metadata for logging locally.
         self.last_local_log = time.monotonic()
         self.local_interval = local_interval
@@ -96,6 +128,14 @@ class StatLogger:
         self.num_prompt_tokens: List[int] = []
         self.num_generation_tokens: List[int] = []
 
+        # Prometheus metrics
+        self.labels = labels
+        self.metrics = Metrics(labelnames=list(labels.keys()))
+
+    def info(self, type: str, obj: object) -> None:
+        if type == "cache_config":
+            self.metrics.info_cache_config.info(obj.metrics_info())
+
     def _get_throughput(self, tracked_stats: List[int], now: float) -> float:
         return float(np.sum(tracked_stats) / (now - self.last_local_log))
 
@@ -105,23 +145,33 @@ class StatLogger:
 
     def _log_prometheus(self, stats: Stats) -> None:
         # Set system stat gauges.
-        gauge_scheduler_running.set(labels, stats.num_running)
-        gauge_scheduler_swapped.set(labels, stats.num_swapped)
-        gauge_scheduler_waiting.set(labels, stats.num_waiting)
-        gauge_gpu_cache_usage.set(labels, stats.gpu_cache_usage)
-        gauge_cpu_cache_usage.set(labels, stats.cpu_cache_usage)
+        self.metrics.gauge_scheduler_running.labels(**self.labels).set(
+            stats.num_running)
+        self.metrics.gauge_scheduler_swapped.labels(**self.labels).set(
+            stats.num_swapped)
+        self.metrics.gauge_scheduler_waiting.labels(**self.labels).set(
+            stats.num_waiting)
+        self.metrics.gauge_gpu_cache_usage.labels(**self.labels).set(
+            stats.gpu_cache_usage)
+        self.metrics.gauge_cpu_cache_usage.labels(**self.labels).set(
+            stats.cpu_cache_usage)
 
         # Add to token counters.
-        counter_prompt_tokens.add(labels, stats.num_prompt_tokens)
-        counter_generation_tokens.add(labels, stats.num_generation_tokens)
+        self.metrics.counter_prompt_tokens.labels(**self.labels).inc(
+            stats.num_prompt_tokens)
+        self.metrics.counter_generation_tokens.labels(**self.labels).inc(
+            stats.num_generation_tokens)
 
         # Observe request level latencies in histograms.
         for ttft in stats.time_to_first_tokens:
-            histogram_time_to_first_token.observe(labels, ttft)
+            self.metrics.histogram_time_to_first_token.labels(
+                **self.labels).observe(ttft)
         for tpot in stats.time_per_output_tokens:
-            histogram_time_per_output_tokens.observe(labels, tpot)
+            self.metrics.histogram_time_per_output_token.labels(
+                **self.labels).observe(tpot)
         for e2e in stats.time_e2e_requests:
-            histogram_e2e_request_latency.observe(labels, e2e)
+            self.metrics.histogram_e2e_request_latency.labels(
+                **self.labels).observe(e2e)
 
     def _log_prometheus_interval(self, prompt_throughput: float,
                                  generation_throughput: float) -> None:
@@ -130,8 +180,10 @@ class StatLogger:
         # Moving forward, we should use counters like counter_prompt_tokens, counter_generation_tokens
         # Which log raw data and calculate summaries using rate() on the grafana/prometheus side.
         # See https://github.com/vllm-project/vllm/pull/2316#discussion_r1464204666
-        gauge_avg_prompt_throughput.set(labels, prompt_throughput)
-        gauge_avg_generation_throughput.set(labels, generation_throughput)
+        self.metrics.gauge_avg_prompt_throughput.labels(
+            **self.labels).set(prompt_throughput)
+        self.metrics.gauge_avg_generation_throughput.labels(
+            **self.labels).set(generation_throughput)
 
     def log(self, stats: Stats) -> None:
         """Called by LLMEngine.

vllm/entrypoints/openai/api_server.py

@@ -6,8 +6,7 @@ import os
 import importlib
 import inspect
 
-from aioprometheus import MetricsMiddleware
-from aioprometheus.asgi.starlette import metrics
+from prometheus_client import make_asgi_app
 import fastapi
 import uvicorn
 from http import HTTPStatus
@@ -18,7 +17,6 @@ from fastapi.responses import JSONResponse, StreamingResponse, Response
 
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
-from vllm.engine.metrics import add_global_metrics_labels
 from vllm.entrypoints.openai.protocol import CompletionRequest, ChatCompletionRequest, ErrorResponse
 from vllm.logger import init_logger
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
@@ -141,8 +139,9 @@ def parse_args():
     return parser.parse_args()
 
 
-app.add_middleware(MetricsMiddleware)  # Trace HTTP server metrics
-app.add_route("/metrics", metrics)  # Exposes HTTP metrics
+# Add prometheus asgi middleware to route /metrics requests
+metrics_app = make_asgi_app()
+app.mount("/metrics", metrics_app)
 
 
 @app.exception_handler(RequestValidationError)
@@ -242,9 +241,6 @@ if __name__ == "__main__":
     openai_serving_completion = OpenAIServingCompletion(
         engine, served_model, args.lora_modules)
 
-    # Register labels for metrics
-    add_global_metrics_labels(model_name=engine_args.model)
-
     app.root_path = args.root_path
     uvicorn.run(app,
                 host=args.host,

vllm/entrypoints/openai/protocol.py

@@ -3,11 +3,13 @@
 import time
 from typing import Dict, List, Literal, Optional, Union
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, model_validator
 
 from vllm.utils import random_uuid
 from vllm.sampling_params import SamplingParams
 
+import torch
+
 
 class ErrorResponse(BaseModel):
     object: str = "error"
@@ -55,7 +57,7 @@ class UsageInfo(BaseModel):
 
 class ChatCompletionRequest(BaseModel):
     model: str
-    messages: Union[str, List[Dict[str, str]]]
+    messages: List[Dict[str, str]]
     temperature: Optional[float] = 0.7
     top_p: Optional[float] = 1.0
     n: Optional[int] = 1
@@ -63,6 +65,8 @@ class ChatCompletionRequest(BaseModel):
     seed: Optional[int] = None
     stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
     stream: Optional[bool] = False
+    logprobs: Optional[bool] = False
+    top_logprobs: Optional[int] = None
     presence_penalty: Optional[float] = 0.0
     frequency_penalty: Optional[float] = 0.0
     logit_bias: Optional[Dict[str, float]] = None
@@ -72,6 +76,7 @@ class ChatCompletionRequest(BaseModel):
     top_k: Optional[int] = -1
     ignore_eos: Optional[bool] = False
     use_beam_search: Optional[bool] = False
+    early_stopping: Optional[bool] = False
     stop_token_ids: Optional[List[int]] = Field(default_factory=list)
     skip_special_tokens: Optional[bool] = True
     spaces_between_special_tokens: Optional[bool] = True
@@ -81,8 +86,28 @@ class ChatCompletionRequest(BaseModel):
     min_p: Optional[float] = 0.0
     include_stop_str_in_output: Optional[bool] = False
     length_penalty: Optional[float] = 1.0
+    guided_json: Optional[Union[str, dict, BaseModel]] = None
+    guided_regex: Optional[str] = None
+    guided_choice: Optional[List[str]] = None
 
     def to_sampling_params(self) -> SamplingParams:
+        if self.logprobs and not self.top_logprobs:
+            raise ValueError("Top logprobs must be set when logprobs is.")
+
+        logits_processors = None
+        if self.logit_bias:
+
+            def logit_bias_logits_processor(
+                    token_ids: List[int],
+                    logits: torch.Tensor) -> torch.Tensor:
+                for token_id, bias in self.logit_bias.items():
+                    # Clamp the bias between -100 and 100 per OpenAI API spec
+                    bias = min(100, max(-100, bias))
+                    logits[int(token_id)] += bias
+                return logits
+
+            logits_processors = [logit_bias_logits_processor]
+
         return SamplingParams(
             n=self.n,
             presence_penalty=self.presence_penalty,
@@ -95,16 +120,34 @@ class ChatCompletionRequest(BaseModel):
             stop=self.stop,
             stop_token_ids=self.stop_token_ids,
             max_tokens=self.max_tokens,
+            logprobs=self.top_logprobs if self.logprobs else None,
+            prompt_logprobs=self.top_logprobs if self.echo else None,
             best_of=self.best_of,
             top_k=self.top_k,
             ignore_eos=self.ignore_eos,
             use_beam_search=self.use_beam_search,
+            early_stopping=self.early_stopping,
             skip_special_tokens=self.skip_special_tokens,
             spaces_between_special_tokens=self.spaces_between_special_tokens,
             include_stop_str_in_output=self.include_stop_str_in_output,
             length_penalty=self.length_penalty,
+            logits_processors=logits_processors,
         )
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_guided_decoding_count(cls, data):
+        guide_count = sum([
+            "guided_json" in data and data["guided_json"] is not None,
+            "guided_regex" in data and data["guided_regex"] is not None,
+            "guided_choice" in data and data["guided_choice"] is not None
+        ])
+        if guide_count > 1:
+            raise ValueError(
+                "You can only use one kind of guided decoding "
+                "('guided_json', 'guided_regex' or 'guided_choice').")
+        return data
+
 
 class CompletionRequest(BaseModel):
     model: str
@@ -129,6 +172,7 @@ class CompletionRequest(BaseModel):
     top_k: Optional[int] = -1
     ignore_eos: Optional[bool] = False
     use_beam_search: Optional[bool] = False
+    early_stopping: Optional[bool] = False
     stop_token_ids: Optional[List[int]] = Field(default_factory=list)
     skip_special_tokens: Optional[bool] = True
     spaces_between_special_tokens: Optional[bool] = True
@@ -136,10 +180,27 @@ class CompletionRequest(BaseModel):
     min_p: Optional[float] = 0.0
     include_stop_str_in_output: Optional[bool] = False
     length_penalty: Optional[float] = 1.0
+    guided_json: Optional[Union[str, dict, BaseModel]] = None
+    guided_regex: Optional[str] = None
+    guided_choice: Optional[List[str]] = None
 
     def to_sampling_params(self):
         echo_without_generation = self.echo and self.max_tokens == 0
 
+        logits_processors = None
+        if self.logit_bias:
+
+            def logit_bias_logits_processor(
+                    token_ids: List[int],
+                    logits: torch.Tensor) -> torch.Tensor:
+                for token_id, bias in self.logit_bias.items():
+                    # Clamp the bias between -100 and 100 per OpenAI API spec
+                    bias = min(100, max(-100, bias))
+                    logits[int(token_id)] += bias
+                return logits
+
+            logits_processors = [logit_bias_logits_processor]
+
         return SamplingParams(
             n=self.n,
             best_of=self.best_of,
@@ -157,13 +218,29 @@ class CompletionRequest(BaseModel):
             max_tokens=self.max_tokens if not echo_without_generation else 1,
             logprobs=self.logprobs,
             use_beam_search=self.use_beam_search,
+            early_stopping=self.early_stopping,
             prompt_logprobs=self.logprobs if self.echo else None,
             skip_special_tokens=self.skip_special_tokens,
             spaces_between_special_tokens=(self.spaces_between_special_tokens),
             include_stop_str_in_output=self.include_stop_str_in_output,
             length_penalty=self.length_penalty,
+            logits_processors=logits_processors,
         )
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_guided_decoding_count(cls, data):
+        guide_count = sum([
+            "guided_json" in data and data["guided_json"] is not None,
+            "guided_regex" in data and data["guided_regex"] is not None,
+            "guided_choice" in data and data["guided_choice"] is not None
+        ])
+        if guide_count > 1:
+            raise ValueError(
+                "You can only use one kind of guided decoding "
+                "('guided_json', 'guided_regex' or 'guided_choice').")
+        return data
+
 
 class LogProbs(BaseModel):
     text_offset: List[int] = Field(default_factory=list)
@@ -212,6 +289,7 @@ class ChatMessage(BaseModel):
 class ChatCompletionResponseChoice(BaseModel):
     index: int
     message: ChatMessage
+    logprobs: Optional[LogProbs] = None
     finish_reason: Optional[Literal["stop", "length"]] = None
 
 
@@ -232,6 +310,7 @@ class DeltaMessage(BaseModel):
 class ChatCompletionResponseStreamChoice(BaseModel):
     index: int
     delta: DeltaMessage
+    logprobs: Optional[LogProbs] = None
     finish_reason: Optional[Literal["stop", "length"]] = None
 
 

vllm/entrypoints/openai/serving_chat.py

@@ -12,6 +12,7 @@ from vllm.entrypoints.openai.protocol import (
     UsageInfo)
 from vllm.outputs import RequestOutput
 from vllm.entrypoints.openai.serving_engine import OpenAIServing, LoRA
+from vllm.model_executor.guided_decoding import get_guided_decoding_logits_processor
 
 logger = init_logger(__name__)
 
@@ -39,19 +40,13 @@ class OpenAIServingChat(OpenAIServing):
         See  https://platform.openai.com/docs/api-reference/chat/create
         for the API specification. This API mimics the OpenAI ChatCompletion API.
 
-        NOTE: Currently we do not support the following features:
+        NOTE: Currently we do not support the following feature:
             - function_call (Users should implement this by themselves)
-            - logit_bias (to be supported by vLLM engine)
         """
         error_check_ret = await self._check_model(request)
         if error_check_ret is not None:
             return error_check_ret
 
-        if request.logit_bias is not None and len(request.logit_bias) > 0:
-            # TODO: support logit_bias in vLLM engine.
-            return self.create_error_response(
-                "logit_bias is not currently supported")
-
         try:
             prompt = self.tokenizer.apply_chat_template(
                 conversation=request.messages,
@@ -68,6 +63,14 @@ class OpenAIServingChat(OpenAIServing):
                                                            prompt=prompt)
             sampling_params = request.to_sampling_params()
             lora_request = self._maybe_get_lora(request)
+            guided_decode_logits_processor = (
+                await get_guided_decoding_logits_processor(
+                    request, self.engine.get_tokenizer()))
+            if guided_decode_logits_processor:
+                if sampling_params.logits_processors is None:
+                    sampling_params.logits_processors = []
+                sampling_params.logits_processors.append(
+                    guided_decode_logits_processor)
         except ValueError as e:
             return self.create_error_response(str(e))
 
@@ -86,7 +89,7 @@ class OpenAIServingChat(OpenAIServing):
         if request.add_generation_prompt:
             return self.response_role
         else:
-            return request.messages[-1].role
+            return request.messages[-1]["role"]
 
     async def chat_completion_stream_generator(
             self, request: ChatCompletionRequest,
@@ -101,7 +104,10 @@ class OpenAIServingChat(OpenAIServing):
         role = self.get_chat_request_role(request)
         for i in range(request.n):
             choice_data = ChatCompletionResponseStreamChoice(
-                index=i, delta=DeltaMessage(role=role), finish_reason=None)
+                index=i,
+                delta=DeltaMessage(role=role),
+                logprobs=None,
+                finish_reason=None)
             chunk = ChatCompletionStreamResponse(id=request_id,
                                                  object=chunk_object_type,
                                                  created=created_time,
@@ -118,6 +124,7 @@ class OpenAIServingChat(OpenAIServing):
                         "content") and request.messages[-1].get(
                             "role") == role:
                 last_msg_content = request.messages[-1]["content"]
+
             if last_msg_content:
                 for i in range(request.n):
                     choice_data = ChatCompletionResponseStreamChoice(
@@ -129,6 +136,7 @@ class OpenAIServingChat(OpenAIServing):
                         object=chunk_object_type,
                         created=created_time,
                         choices=[choice_data],
+                        logprobs=None,
                         model=model_name)
                     data = chunk.model_dump_json(exclude_unset=True)
                     yield f"data: {data}\n\n"
@@ -145,15 +153,29 @@ class OpenAIServingChat(OpenAIServing):
                 if finish_reason_sent[i]:
                     continue
 
+                delta_token_ids = output.token_ids[previous_num_tokens[i]:]
+                top_logprobs = output.logprobs[
+                    previous_num_tokens[i]:] if output.logprobs else None
+
+                if request.logprobs:
+                    logprobs = self._create_logprobs(
+                        token_ids=delta_token_ids,
+                        top_logprobs=top_logprobs,
+                        num_output_top_logprobs=request.logprobs,
+                        initial_text_offset=len(previous_texts[i]),
+                    )
+                else:
+                    logprobs = None
+
                 delta_text = output.text[len(previous_texts[i]):]
                 previous_texts[i] = output.text
                 previous_num_tokens[i] = len(output.token_ids)
-
                 if output.finish_reason is None:
                     # Send token-by-token response for each request.n
                     choice_data = ChatCompletionResponseStreamChoice(
                         index=i,
                         delta=DeltaMessage(content=delta_text),
+                        logprobs=logprobs,
                         finish_reason=None)
                     chunk = ChatCompletionStreamResponse(
                         id=request_id,
@@ -174,6 +196,7 @@ class OpenAIServingChat(OpenAIServing):
                     choice_data = ChatCompletionResponseStreamChoice(
                         index=i,
                         delta=DeltaMessage(content=delta_text),
+                        logprobs=logprobs,
                         finish_reason=output.finish_reason)
                     chunk = ChatCompletionStreamResponse(
                         id=request_id,
@@ -208,11 +231,25 @@ class OpenAIServingChat(OpenAIServing):
         assert final_res is not None
 
         choices = []
+
         role = self.get_chat_request_role(request)
         for output in final_res.outputs:
+            token_ids = output.token_ids
+            top_logprobs = output.logprobs
+
+            if request.logprobs:
+                logprobs = self._create_logprobs(
+                    token_ids=token_ids,
+                    top_logprobs=top_logprobs,
+                    num_output_top_logprobs=request.logprobs,
+                )
+            else:
+                logprobs = None
+
             choice_data = ChatCompletionResponseChoice(
                 index=output.index,
                 message=ChatMessage(role=role, content=output.text),
+                logprobs=logprobs,
                 finish_reason=output.finish_reason,
             )
             choices.append(choice_data)

vllm/entrypoints/openai/serving_completion.py

@@ -5,7 +5,7 @@ from typing import AsyncGenerator, AsyncIterator, Callable, List, Optional, Dict
 from vllm.logger import init_logger
 from vllm.utils import random_uuid
 from vllm.engine.async_llm_engine import AsyncLLMEngine
-from .protocol import (
+from vllm.entrypoints.openai.protocol import (
     CompletionRequest,
     CompletionResponse,
     CompletionResponseChoice,
@@ -16,6 +16,7 @@ from .protocol import (
 )
 from vllm.outputs import RequestOutput
 from vllm.entrypoints.openai.serving_engine import OpenAIServing, LoRA
+from vllm.model_executor.guided_decoding import get_guided_decoding_logits_processor
 
 logger = init_logger(__name__)
 
@@ -95,7 +96,7 @@ async def completion_stream_generator(
                         logprobs=logprobs,
                         finish_reason=finish_reason,
                     )
-                ]).model_dump_json(exclude_unset=True)
+                ]).model_dump_json()
             yield f"data: {response_json}\n\n"
 
             if output.finish_reason is not None:  # return final usage
@@ -120,7 +121,7 @@ async def completion_stream_generator(
                         )
                     ],
                     usage=final_usage,
-                ).model_dump_json(exclude_unset=True)
+                ).model_dump_json()
                 yield f"data: {response_json}\n\n"
 
     yield "data: [DONE]\n\n"
@@ -264,10 +265,9 @@ class OpenAIServingCompletion(OpenAIServing):
         See https://platform.openai.com/docs/api-reference/completions/create
         for the API specification. This API mimics the OpenAI Completion API.
 
-        NOTE: Currently we do not support the following features:
+        NOTE: Currently we do not support the following feature:
             - suffix (the language models we currently support do not support
             suffix)
-            - logit_bias (to be supported by vLLM engine)
         """
         error_check_ret = await self._check_model(request)
         if error_check_ret is not None:
@@ -277,9 +277,6 @@ class OpenAIServingCompletion(OpenAIServing):
         if request.suffix is not None:
             return self.create_error_response(
                 "suffix is not currently supported")
-        if request.logit_bias is not None and len(request.logit_bias) > 0:
-            return self.create_error_response(
-                "logit_bias is not currently supported")
 
         model_name = request.model
         request_id = f"cmpl-{random_uuid()}"
@@ -290,6 +287,14 @@ class OpenAIServingCompletion(OpenAIServing):
         try:
             sampling_params = request.to_sampling_params()
             lora_request = self._maybe_get_lora(request)
+            guided_decode_logit_processor = (
+                await get_guided_decoding_logits_processor(
+                    request, self.engine.get_tokenizer()))
+            if guided_decode_logit_processor is not None:
+                if sampling_params.logits_processors is None:
+                    sampling_params.logits_processors = []
+                sampling_params.logits_processors.append(
+                    guided_decode_logit_processor)
             prompt_is_tokens, prompts = parse_prompt_format(request.prompt)
 
             for i, prompt in enumerate(prompts):
@@ -301,7 +306,7 @@ class OpenAIServingCompletion(OpenAIServing):
                         request, prompt=prompt)
 
                 generators.append(
-                    self.engine.generate(None,
+                    self.engine.generate(prompt,
                                          sampling_params,
                                          f"{request_id}-{i}",
                                          prompt_token_ids=input_ids,

vllm/lora/layers.py

@@ -795,6 +795,10 @@ class SamplerWithLoRA(BaseLayerWithLoRA):
         self.dtype = dtype
         self.device = device
 
+    @property
+    def logits_as_hidden_states(self):
+        return self.base_layer.logits_as_hidden_states
+
     @property
     def vocab_size(self):
         return self.base_layer.vocab_size