[BUG]: VirtualMemoryResource `host_numa` allocations always fail

Is this a duplicate?

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Type of Bug

Runtime Error

Component

cuda.core

Describe the bug

Every VirtualMemoryResource allocation with location_type="host_numa" fails with CUDA_ERROR_INVALID_VALUE, on a system that reports host_numa_id 0 and host_numa_virtual_memory_management_supported True. VirtualMemoryResource.allocate passes prop.location.id = -1 whenever the resource has no device, which __init__ sets for every host location type, and cuMemCreate rejects CU_MEM_LOCATION_TYPE_HOST_NUMA with that id. cuda_core/cuda/core/typing.py documents HOST_NUMA as "host memory pinned to a specific NUMA node", and VirtualMemoryResourceOptions has no field that carries a node id.

location_type="host_numa_current" also fails, for a separate reason: cuMemCreate rejects CU_MEM_LOCATION_TYPE_HOST_NUMA_CURRENT for every node id I passed it, including 0.

test_vmm_host_location_types_report_host_accessible in cuda_core/tests/test_memory.py parametrizes over host, host_numa and host_numa_current, but it only checks mr.device and mr.is_host_accessible, so it stays green without ever calling allocate.

How to Reproduce

from cuda.core import Device, VirtualMemoryResource, VirtualMemoryResourceOptions

dev = Device()
dev.set_current()
print("host_numa_id =", dev.properties.host_numa_id)
for location_type in ("host", "host_numa"):
    opts = VirtualMemoryResourceOptions(location_type=location_type, handle_type=None)
    buf = VirtualMemoryResource(dev, config=opts).allocate(4096)
    print(location_type, "allocated", buf.size, "bytes")
    buf.close()

host_numa_id = 0
host allocated 2097152 bytes
Traceback (most recent call last):
  File "/tmp/vmm_host_numa.py", line 8, in <module>
    buf = VirtualMemoryResource(dev, config=opts).allocate(4096)
  File "/home/vyron-vasileiadis/projects/forks/cuda-python/cuda_core/cuda/core/_memory/_virtual_memory_resource.py", line 555, in allocate
    raise_if_driver_error(res)
    ~~~~~~~~~~~~~~~~~~~~~^^^^^
  File "cuda/core/_utils/cuda_utils.pyx", line 134, in cuda.core._utils.cuda_utils._check_driver_error
  File "cuda/core/_utils/cuda_utils.pyx", line 145, in cuda.core._utils.cuda_utils._check_driver_error
cuda.core._utils.cuda_utils.CUDAError: CUDA_ERROR_INVALID_VALUE: This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.

Expected behavior

allocate should either return a buffer pinned to a NUMA node, the way location_type="host" and location_type="device" already do, or fail at construction with an error naming the missing node id. Driving cuMemCreate directly on this system, CU_MEM_LOCATION_TYPE_HOST_NUMA returns CUDA_SUCCESS at location.id = 0 and CUDA_ERROR_INVALID_VALUE at location.id = -1, so the location id is the only thing standing between the current behaviour and a working allocation.

Operating System

Ubuntu 26.04 LTS

nvidia-smi output

Tue Aug 25 11:22:23 2026       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 595.84                 Driver Version: 595.84         CUDA Version: 13.2     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA GeForce RTX 3050 ...    Off |   00000000:01:00.0 Off |                  N/A |
| N/A   46C    P8              3W /   30W |      11MiB /   4096MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

Inizia da cuda_core/cuda/core/_memory/_virtual_memory_resource.py, in particolare da VirtualMemoryResource.init e allocate, e verifica le definizioni di HOST_NUMA in cuda_core/cuda/core/typing.py. Esegui cuda_core/tests/test_memory.py ed estendi la copertura delle posizioni dell’host per testare l’allocazione, inclusi i casi segnalati host_numa e host_numa_current. Il lavoro è completato quando il comportamento delle posizioni dell’host supportate è testato e l’allocazione non fallisce più perché manca l’ID della posizione.