[Bugfix] Enable V1 usage stats (#16986)

Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: Nick Hill <nhill@redhat.com>
Co-authored-by: Nick Hill <nhill@redhat.com>

vllm/usage/usage_lib.py

@@ -19,6 +19,7 @@ import torch
 
 import vllm.envs as envs
 from vllm.connections import global_http_connection
+from vllm.utils import cuda_device_count_stateless, cuda_get_device_properties
 from vllm.version import __version__ as VLLM_VERSION
 
 _config_home = envs.VLLM_CONFIG_ROOT
@@ -168,10 +169,9 @@ class UsageMessage:
         # Platform information
         from vllm.platforms import current_platform
         if current_platform.is_cuda_alike():
-            device_property = torch.cuda.get_device_properties(0)
-            self.gpu_count = torch.cuda.device_count()
-            self.gpu_type = device_property.name
-            self.gpu_memory_per_device = device_property.total_memory
+            self.gpu_count = cuda_device_count_stateless()
+            self.gpu_type, self.gpu_memory_per_device = (
+                cuda_get_device_properties(0, ("name", "total_memory")))
         if current_platform.is_cuda():
             self.cuda_runtime = torch.version.cuda
         self.provider = _detect_cloud_provider()

vllm/utils.py

@@ -38,11 +38,13 @@ from asyncio import FIRST_COMPLETED, AbstractEventLoop, Task
 from collections import UserDict, defaultdict
 from collections.abc import (AsyncGenerator, Awaitable, Generator, Hashable,
                              Iterable, Iterator, KeysView, Mapping)
+from concurrent.futures.process import ProcessPoolExecutor
 from dataclasses import dataclass, field
 from functools import cache, lru_cache, partial, wraps
 from types import MappingProxyType
 from typing import (TYPE_CHECKING, Any, Callable, Generic, Literal, NamedTuple,
-                    Optional, Tuple, Type, TypeVar, Union, cast, overload)
+                    Optional, Sequence, Tuple, Type, TypeVar, Union, cast,
+                    overload)
 from uuid import uuid4
 
 import cachetools
@@ -1235,6 +1237,22 @@ def cuda_is_initialized() -> bool:
     return torch.cuda.is_initialized()
 
 
+def cuda_get_device_properties(device,
+                               names: Sequence[str],
+                               init_cuda=False) -> tuple[Any, ...]:
+    """Get specified CUDA device property values without initializing CUDA in
+    the current process."""
+    if init_cuda or cuda_is_initialized():
+        props = torch.cuda.get_device_properties(device)
+        return tuple(getattr(props, name) for name in names)
+
+    # Run in subprocess to avoid initializing CUDA as a side effect.
+    mp_ctx = multiprocessing.get_context("fork")
+    with ProcessPoolExecutor(max_workers=1, mp_context=mp_ctx) as executor:
+        return executor.submit(cuda_get_device_properties, device, names,
+                               True).result()
+
+
 def weak_bind(bound_method: Callable[..., Any], ) -> Callable[..., None]:
     """Make an instance method that weakly references
     its associated instance and no-ops once that

vllm/v1/engine/async_llm.py

@@ -36,6 +36,7 @@ from vllm.v1.executor.abstract import Executor
 from vllm.v1.metrics.loggers import (LoggingStatLogger, PrometheusStatLogger,
                                      StatLoggerBase)
 from vllm.v1.metrics.stats import IterationStats, SchedulerStats
+from vllm.v1.utils import report_usage_stats
 
 logger = init_logger(__name__)
 
@@ -114,6 +115,9 @@ class AsyncLLM(EngineClient):
         except RuntimeError:
             pass
 
+        # If usage stat is enabled, collect relevant info.
+        report_usage_stats(vllm_config, usage_context)
+
     @classmethod
     def from_vllm_config(
         cls,

vllm/v1/engine/llm_engine.py

@@ -28,6 +28,7 @@ from vllm.v1.engine.output_processor import OutputProcessor
 from vllm.v1.engine.parallel_sampling import ParentRequest
 from vllm.v1.engine.processor import Processor
 from vllm.v1.executor.abstract import Executor
+from vllm.v1.utils import report_usage_stats
 
 logger = init_logger(__name__)
 
@@ -99,6 +100,9 @@ class LLMEngine:
             # for v0 compatibility
             self.model_executor = self.engine_core.engine_core.model_executor  # type: ignore
 
+        # If usage stat is enabled, collect relevant info.
+        report_usage_stats(vllm_config, usage_context)
+
     @classmethod
     def from_vllm_config(
         cls,

vllm/v1/utils.py

@@ -12,6 +12,8 @@ import torch
 
 from vllm.logger import init_logger
 from vllm.model_executor.models.utils import extract_layer_index
+from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled,
+                                  usage_message)
 from vllm.utils import get_mp_context, kill_process_tree
 
 if TYPE_CHECKING:
@@ -201,3 +203,45 @@ def copy_slice(from_tensor: torch.Tensor, to_tensor: torch.Tensor,
     Returns the sliced target tensor.
     """
     return to_tensor[:length].copy_(from_tensor[:length], non_blocking=True)
+
+
+def report_usage_stats(vllm_config, usage_context: UsageContext) -> 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,
+        })