Skip to content

vllm.v1.utils

T module-attribute 

T = TypeVar('T')

logger module-attribute 

logger = init_logger(__name__)

APIServerProcessManager

Manages a group of API server processes.

Handles creation, monitoring, and termination of API server worker processes. Also monitors extra processes to check if they are healthy.

Source code in vllm/v1/utils.py 
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
class APIServerProcessManager:
    """Manages a group of API server processes.

    Handles creation, monitoring, and termination of API server worker
    processes. Also monitors extra processes to check if they are healthy.
    """

    def __init__(
        self,
        target_server_fn: Callable,
        listen_address: str,
        sock: Any,
        args: argparse.Namespace,
        num_servers: int,
        input_addresses: list[str],
        output_addresses: list[str],
        stats_update_address: Optional[str] = None,
    ):
        """Initialize and start API server worker processes.

        Args:
            target_server_fn: Function to call for each API server process
            listen_address: Address to listen for client connections
            sock: Socket for client connections
            args: Command line arguments
            num_servers: Number of API server processes to start
            input_addresses: Input addresses for each API server
            output_addresses: Output addresses for each API server
            stats_update_address: Optional stats update address 
        """
        self.listen_address = listen_address
        self.sock = sock
        self.args = args

        # Start API servers
        spawn_context = multiprocessing.get_context("spawn")
        self.processes: list[BaseProcess] = []

        for i, in_addr, out_addr in zip(range(num_servers), input_addresses,
                                        output_addresses):
            client_config = {
                "input_address": in_addr,
                "output_address": out_addr,
                "client_index": i
            }
            if stats_update_address is not None:
                client_config["stats_update_address"] = stats_update_address

            proc = spawn_context.Process(target=target_server_fn,
                                         name=f"ApiServer_{i}",
                                         args=(listen_address, sock, args,
                                               client_config))
            self.processes.append(proc)
            proc.start()

        logger.info("Started %d API server processes", len(self.processes))

        # Shutdown only the API server processes on garbage collection
        # The extra processes are managed by their owners
        self._finalizer = weakref.finalize(self, shutdown, self.processes)

    def close(self) -> None:
        self._finalizer()

_finalizer instance-attribute 

_finalizer = finalize(self, shutdown, processes)

args instance-attribute 

args = args

listen_address instance-attribute 

listen_address = listen_address

processes instance-attribute 

processes: list[BaseProcess] = []

sock instance-attribute 

sock = sock

__init__ 

__init__(
    target_server_fn: Callable,
    listen_address: str,
    sock: Any,
    args: Namespace,
    num_servers: int,
    input_addresses: list[str],
    output_addresses: list[str],
    stats_update_address: Optional[str] = None,
)

Initialize and start API server worker processes.

Parameters:

Name Type Description Default
target_server_fn Callable

Function to call for each API server process

 required
listen_address str

Address to listen for client connections

 required
sock Any

Socket for client connections

 required
args Namespace

Command line arguments

 required
num_servers int

Number of API server processes to start

 required
input_addresses list[str]

Input addresses for each API server

 required
output_addresses list[str]

Output addresses for each API server

 required
stats_update_address Optional[str]

Optional stats update address

 None
Source code in vllm/v1/utils.py 
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
def __init__(
    self,
    target_server_fn: Callable,
    listen_address: str,
    sock: Any,
    args: argparse.Namespace,
    num_servers: int,
    input_addresses: list[str],
    output_addresses: list[str],
    stats_update_address: Optional[str] = None,
):
    """Initialize and start API server worker processes.

    Args:
        target_server_fn: Function to call for each API server process
        listen_address: Address to listen for client connections
        sock: Socket for client connections
        args: Command line arguments
        num_servers: Number of API server processes to start
        input_addresses: Input addresses for each API server
        output_addresses: Output addresses for each API server
        stats_update_address: Optional stats update address 
    """
    self.listen_address = listen_address
    self.sock = sock
    self.args = args

    # Start API servers
    spawn_context = multiprocessing.get_context("spawn")
    self.processes: list[BaseProcess] = []

    for i, in_addr, out_addr in zip(range(num_servers), input_addresses,
                                    output_addresses):
        client_config = {
            "input_address": in_addr,
            "output_address": out_addr,
            "client_index": i
        }
        if stats_update_address is not None:
            client_config["stats_update_address"] = stats_update_address

        proc = spawn_context.Process(target=target_server_fn,
                                     name=f"ApiServer_{i}",
                                     args=(listen_address, sock, args,
                                           client_config))
        self.processes.append(proc)
        proc.start()

    logger.info("Started %d API server processes", len(self.processes))

    # Shutdown only the API server processes on garbage collection
    # The extra processes are managed by their owners
    self._finalizer = weakref.finalize(self, shutdown, self.processes)

close 

close() -> None
Source code in vllm/v1/utils.py 
178
179
def close(self) -> None:
    self._finalizer()

ConstantList

Bases: Generic[T], Sequence

Source code in vllm/v1/utils.py 
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
class ConstantList(Generic[T], Sequence):

    def __init__(self, x: list[T]) -> None:
        self._x = x

    def append(self, item):
        raise Exception("Cannot append to a constant list")

    def extend(self, item):
        raise Exception("Cannot extend a constant list")

    def insert(self, item):
        raise Exception("Cannot insert into a constant list")

    def pop(self, item):
        raise Exception("Cannot pop from a constant list")

    def remove(self, item):
        raise Exception("Cannot remove from a constant list")

    def clear(self):
        raise Exception("Cannot clear a constant list")

    def index(self,
              item: T,
              start: int = 0,
              stop: Optional[int] = None) -> int:
        return self._x.index(item, start,
                             stop if stop is not None else len(self._x))

    @overload
    def __getitem__(self, item: int) -> T:
        ...

    @overload
    def __getitem__(self, s: slice, /) -> list[T]:
        ...

    def __getitem__(self, item: Union[int, slice]) -> Union[T, list[T]]:
        return self._x[item]

    @overload
    def __setitem__(self, item: int, value: T):
        ...

    @overload
    def __setitem__(self, s: slice, value: T, /):
        ...

    def __setitem__(self, item: Union[int, slice], value: Union[T, list[T]]):
        raise Exception("Cannot set item in a constant list")

    def __delitem__(self, item):
        raise Exception("Cannot delete item from a constant list")

    def __iter__(self):
        return iter(self._x)

    def __contains__(self, item):
        return item in self._x

    def __len__(self):
        return len(self._x)

    def __repr__(self):
        return f"ConstantList({self._x})"

_x instance-attribute 

_x = x

__contains__ 

__contains__(item)
Source code in vllm/v1/utils.py 
92
93
def __contains__(self, item):
    return item in self._x

__delitem__ 

__delitem__(item)
Source code in vllm/v1/utils.py 
86
87
def __delitem__(self, item):
    raise Exception("Cannot delete item from a constant list")

__getitem__ 

__getitem__(item: int) -> T

__getitem__(s: slice) -> list[T]

__getitem__(item: Union[int, slice]) -> Union[T, list[T]]
Source code in vllm/v1/utils.py 
72
73
def __getitem__(self, item: Union[int, slice]) -> Union[T, list[T]]:
    return self._x[item]

__init__ 

__init__(x: list[T]) -> None
Source code in vllm/v1/utils.py 
36
37
def __init__(self, x: list[T]) -> None:
    self._x = x

__iter__ 

__iter__()
Source code in vllm/v1/utils.py 
89
90
def __iter__(self):
    return iter(self._x)

__len__ 

__len__()
Source code in vllm/v1/utils.py 
95
96
def __len__(self):
    return len(self._x)

__repr__ 

__repr__()
Source code in vllm/v1/utils.py 
98
99
def __repr__(self):
    return f"ConstantList({self._x})"

__setitem__ 

__setitem__(item: int, value: T)

__setitem__(s: slice, value: T)

__setitem__(
    item: Union[int, slice], value: Union[T, list[T]]
)
Source code in vllm/v1/utils.py 
83
84
def __setitem__(self, item: Union[int, slice], value: Union[T, list[T]]):
    raise Exception("Cannot set item in a constant list")

append 

append(item)
Source code in vllm/v1/utils.py 
39
40
def append(self, item):
    raise Exception("Cannot append to a constant list")

clear 

clear()
Source code in vllm/v1/utils.py 
54
55
def clear(self):
    raise Exception("Cannot clear a constant list")

extend 

extend(item)
Source code in vllm/v1/utils.py 
42
43
def extend(self, item):
    raise Exception("Cannot extend a constant list")

index 

index(
    item: T, start: int = 0, stop: Optional[int] = None
) -> int
Source code in vllm/v1/utils.py 
57
58
59
60
61
62
def index(self,
          item: T,
          start: int = 0,
          stop: Optional[int] = None) -> int:
    return self._x.index(item, start,
                         stop if stop is not None else len(self._x))

insert 

insert(item)
Source code in vllm/v1/utils.py 
45
46
def insert(self, item):
    raise Exception("Cannot insert into a constant list")

pop 

pop(item)
Source code in vllm/v1/utils.py 
48
49
def pop(self, item):
    raise Exception("Cannot pop from a constant list")

remove 

remove(item)
Source code in vllm/v1/utils.py 
51
52
def remove(self, item):
    raise Exception("Cannot remove from a constant list")

bind_kv_cache 

bind_kv_cache(
    kv_caches: dict[str, Tensor],
    forward_context: dict[str, Attention],
    runner_kv_caches: list[Tensor],
) -> None

Bind the allocated KV cache to both ModelRunner and forward context so that the KV cache can be used in the forward pass.

This function

1) Fills the ModelRunner's kv cache list (runner_kv_caches) with kv_caches. 2) Associates each attention layer in the forward_context with its corresponding KV cache in kv_caches.

Parameters:

Name Type Description Default
kv_caches dict[str, Tensor]

The allocated kv_caches with layer names as keys.

 required
forward_context dict[str, Attention]

The global forward context containing all Attention

required
runner_kv_caches list[Tensor]

The kv_cache declared by ModelRunner.

 required
Source code in vllm/v1/utils.py 
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
def bind_kv_cache(
    kv_caches: dict[str, torch.Tensor],
    forward_context: dict[str, "Attention"],
    runner_kv_caches: list[torch.Tensor],
) -> None:
    """
    Bind the allocated KV cache to both ModelRunner and forward context so
    that the KV cache can be used in the forward pass.

    This function:
      1) Fills the ModelRunner's kv cache list (`runner_kv_caches`) with
         kv_caches.
      2) Associates each attention layer in the `forward_context` with its 
         corresponding KV cache in kv_caches.

    Args:
        kv_caches: The allocated kv_caches with layer names as keys.
        forward_context: The global forward context containing all Attention 
        layers with layer names as keys.
        runner_kv_caches: The kv_cache declared by ModelRunner.
    """
    # Bind kv_caches to ModelRunner
    assert len(runner_kv_caches) == 0

    # Convert kv_caches dict to a list of tensors in the order of layer_index.
    index2name = defaultdict(list)
    for layer_name in kv_caches:
        index2name[extract_layer_index(layer_name)].append(layer_name)

    for layer_index in sorted(index2name.keys()):
        layer_names = index2name[layer_index]
        if len(layer_names) > 1:
            # One typical case is encoder-decoder model, e.g., bart.
            # The cross attention and self attention in the same decoder layer
            # has different layer_name but the same layer_index.
            raise NotImplementedError
        layer_name = layer_names[0]
        runner_kv_caches.append(kv_caches[layer_name])

    # Bind kv_caches to forward context
    for layer_name, kv_cache in kv_caches.items():
        # NOTE: Use list because of v0 PP virtual engine.
        forward_context[layer_name].kv_cache = [kv_cache]

copy_slice 

copy_slice(
    from_tensor: Tensor, to_tensor: Tensor, length: int
) -> Tensor

Copy the first length elements of a tensor into another tensor in a non-blocking manner.

Used to copy pinned CPU tensor data to pre-allocated GPU tensors.

Returns the sliced target tensor.

Source code in vllm/v1/utils.py 
323
324
325
326
327
328
329
330
331
332
333
def copy_slice(from_tensor: torch.Tensor, to_tensor: torch.Tensor,
               length: int) -> torch.Tensor:
    """
    Copy the first length elements of a tensor into another tensor in a
    non-blocking manner.

    Used to copy pinned CPU tensor data to pre-allocated GPU tensors.

    Returns the sliced target tensor.
    """
    return to_tensor[:length].copy_(from_tensor[:length], non_blocking=True)

get_engine_client_zmq_addr 

get_engine_client_zmq_addr(
    local_only: bool, host: str, port: int = 0
) -> str

Assign a new ZMQ socket address.

If local_only is True, participants are colocated and so a unique IPC address will be returned.

Otherwise, the provided host and port will be used to construct a TCP address (port == 0 means assign an available port).

Source code in vllm/v1/utils.py 
102
103
104
105
106
107
108
109
110
111
112
113
114
def get_engine_client_zmq_addr(local_only: bool,
                               host: str,
                               port: int = 0) -> str:
    """Assign a new ZMQ socket address.

    If local_only is True, participants are colocated and so a unique IPC
    address will be returned.

    Otherwise, the provided host and port will be used to construct a TCP
    address (port == 0 means assign an available port)."""

    return get_open_zmq_ipc_path() if local_only else (get_tcp_uri(
        host, port or get_open_port()))

report_usage_stats 

report_usage_stats(
    vllm_config,
    usage_context: UsageContext = ENGINE_CONTEXT,
) -> None

Report usage statistics if enabled.

Source code in vllm/v1/utils.py 
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
def report_usage_stats(
        vllm_config,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT) -> None:
    """Report usage statistics if enabled."""

    if not is_usage_stats_enabled():
        return

    from vllm.model_executor.model_loader import get_architecture_class_name

    usage_message.report_usage(
        get_architecture_class_name(vllm_config.model_config),
        usage_context,
        extra_kvs={
            # Common configuration
            "dtype":
            str(vllm_config.model_config.dtype),
            "tensor_parallel_size":
            vllm_config.parallel_config.tensor_parallel_size,
            "block_size":
            vllm_config.cache_config.block_size,
            "gpu_memory_utilization":
            vllm_config.cache_config.gpu_memory_utilization,

            # Quantization
            "quantization":
            vllm_config.model_config.quantization,
            "kv_cache_dtype":
            str(vllm_config.cache_config.cache_dtype),

            # Feature flags
            "enable_lora":
            bool(vllm_config.lora_config),
            "enable_prompt_adapter":
            bool(vllm_config.prompt_adapter_config),
            "enable_prefix_caching":
            vllm_config.cache_config.enable_prefix_caching,
            "enforce_eager":
            vllm_config.model_config.enforce_eager,
            "disable_custom_all_reduce":
            vllm_config.parallel_config.disable_custom_all_reduce,
        })

shutdown 

shutdown(procs: list[BaseProcess])
Source code in vllm/v1/utils.py 
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
def shutdown(procs: list[BaseProcess]):
    # Shutdown the process.
    for proc in procs:
        if proc.is_alive():
            proc.terminate()

    # Allow 5 seconds for remaining procs to terminate.
    deadline = time.monotonic() + 5
    for proc in procs:
        remaining = deadline - time.monotonic()
        if remaining <= 0:
            break
        if proc.is_alive():
            proc.join(remaining)

    for proc in procs:
        if proc.is_alive() and (pid := proc.pid) is not None:
            kill_process_tree(pid)

wait_for_completion_or_failure 

wait_for_completion_or_failure(
    api_server_manager: APIServerProcessManager,
    engine_manager: Optional[
        Union[CoreEngineProcManager, CoreEngineActorManager]
    ] = None,
    coordinator: Optional[DPCoordinator] = None,
) -> None

Wait for all processes to complete or detect if any fail.

Raises an exception if any process exits with a non-zero status.

Parameters:

Name Type Description Default
api_server_manager APIServerProcessManager

The manager for API servers.

 required
engine_manager Optional[Union[CoreEngineProcManager, CoreEngineActorManager]]

The manager for engine processes. If CoreEngineProcManager, it manages local engines; if CoreEngineActorManager, it manages all engines.

 None
coordinator Optional[DPCoordinator]

The coordinator for data parallel.

 None
Source code in vllm/v1/utils.py 
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
def wait_for_completion_or_failure(
        api_server_manager: APIServerProcessManager,
        engine_manager: Optional[Union["CoreEngineProcManager",
                                       "CoreEngineActorManager"]] = None,
        coordinator: Optional["DPCoordinator"] = None) -> None:
    """Wait for all processes to complete or detect if any fail.

    Raises an exception if any process exits with a non-zero status.

    Args:
        api_server_manager: The manager for API servers.
        engine_manager: The manager for engine processes.
            If CoreEngineProcManager, it manages local engines;
            if CoreEngineActorManager, it manages all engines.
        coordinator: The coordinator for data parallel.
    """

    from vllm.v1.engine.utils import (CoreEngineActorManager,
                                      CoreEngineProcManager)

    try:
        logger.info("Waiting for API servers to complete ...")
        # Create a mapping of sentinels to their corresponding processes
        # for efficient lookup
        sentinel_to_proc: dict[Any, BaseProcess] = {
            proc.sentinel: proc
            for proc in api_server_manager.processes
        }

        if coordinator:
            sentinel_to_proc[coordinator.proc.sentinel] = coordinator.proc

        actor_run_refs = []
        if isinstance(engine_manager, CoreEngineProcManager):
            for proc in engine_manager.processes:
                sentinel_to_proc[proc.sentinel] = proc
        elif isinstance(engine_manager, CoreEngineActorManager):
            actor_run_refs = engine_manager.get_run_refs()

        # Check if any process terminates
        while sentinel_to_proc or actor_run_refs:
            # Wait for any process to terminate
            ready_sentinels: list[Any] = connection.wait(sentinel_to_proc,
                                                         timeout=5)

            # Process any terminated processes
            for sentinel in ready_sentinels:
                proc = sentinel_to_proc.pop(sentinel)

                # Check if process exited with error
                if proc.exitcode != 0:
                    raise RuntimeError(
                        f"Process {proc.name} (PID: {proc.pid}) "
                        f"died with exit code {proc.exitcode}")

            if actor_run_refs:
                import ray
                _, actor_run_refs = ray.wait(actor_run_refs, timeout=5)

    except KeyboardInterrupt:
        logger.info("Received KeyboardInterrupt, shutting down API servers...")
    except Exception as e:
        logger.exception("Exception occurred while running API servers: %s",
                         str(e))
        raise
    finally:
        logger.info("Terminating remaining processes ...")
        api_server_manager.close()
        if coordinator:
            coordinator.close()
        if engine_manager:
            engine_manager.close()