[misc][cuda] use nvml to avoid accidentally cuda initialization (#6007)

tests/kernels/test_cutlass.py

@@ -8,12 +8,13 @@ import pytest
 import torch
 
 from vllm import _custom_ops as ops
+from vllm.utils import get_device_capability_stateless
 
 CUDA_DEVICES = [
     f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
 ]
 
-capability = torch.cuda.get_device_capability()
+capability = get_device_capability_stateless()
 capability = capability[0] * 10 + capability[1]
 
 

tests/quantization/utils.py

@@ -1,6 +1,7 @@
 import torch
 
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
+from vllm.utils import get_device_capability_stateless
 
 
 def is_quant_method_supported(quant_method: str) -> bool:
@@ -8,7 +9,7 @@ def is_quant_method_supported(quant_method: str) -> bool:
     if not torch.cuda.is_available():
         return False
 
-    capability = torch.cuda.get_device_capability()
+    capability = get_device_capability_stateless()
     capability = capability[0] * 10 + capability[1]
     return (capability >=
             QUANTIZATION_METHODS[quant_method].get_min_capability())

vllm/attention/ops/blocksparse_attention/interface.py

@@ -2,13 +2,13 @@ import math
 
 import torch
 
-from vllm.utils import is_cpu, is_hip
+from vllm.utils import get_device_capability_stateless, is_cpu, is_hip
 
 from .utils import (dense_to_crow_col, get_head_sliding_step,
                     get_sparse_attn_mask)
 
 IS_COMPUTE_8_OR_ABOVE = (torch.cuda.is_available()
-                         and torch.cuda.get_device_capability()[0] >= 8)
+                         and get_device_capability_stateless()[0] >= 8)
 
 if IS_COMPUTE_8_OR_ABOVE:
     from .blocksparse_attention_kernel import blocksparse_flash_attn_varlen_fwd

vllm/attention/ops/prefix_prefill.py

@@ -5,6 +5,8 @@ import torch
 import triton
 import triton.language as tl
 
+from vllm.utils import get_device_capability_stateless
+
 if triton.__version__ >= "2.1.0":
 
     @triton.jit
@@ -683,7 +685,7 @@ if triton.__version__ >= "2.1.0":
                               alibi_slopes=None,
                               sliding_window=None):
 
-        cap = torch.cuda.get_device_capability()
+        cap = get_device_capability_stateless()
         BLOCK = 128 if cap[0] >= 8 else 64
         # shape constraints
         Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]

vllm/distributed/device_communicators/custom_all_reduce.py

@@ -11,66 +11,18 @@ from vllm.distributed.device_communicators.custom_all_reduce_utils import (
     gpu_p2p_access_check)
 from vllm.distributed.parallel_state import is_in_the_same_node
 from vllm.logger import init_logger
-from vllm.utils import cuda_device_count_stateless
+from vllm.utils import cuda_device_count_stateless, is_full_nvlink
 
 try:
-    import pynvml
+    assert ops.is_custom_op_supported("_C_custom_ar::meta_size")
-
-    # Simulate ImportError if custom_ar ops are not supported.
-    if not ops.is_custom_op_supported("_C_custom_ar::meta_size"):
-        raise ImportError("custom_ar", __file__)
-
     custom_ar = True
-
+except Exception:
-    @contextmanager
+    # For AMD GPUs and CPUs
-    def _nvml():
-        try:
-            pynvml.nvmlInit()
-            yield
-        finally:
-            pynvml.nvmlShutdown()
-
-except ImportError:
-    # For AMD GPUs
     custom_ar = False
-    pynvml = None
-
-    @contextmanager
-    def _nvml():
-        try:
-            yield
-        finally:
-            pass
-
 
 logger = init_logger(__name__)
 
 
-@_nvml()
-def _is_full_nvlink(device_ids: List[int]) -> bool:
-    """
-    query if the set of gpus are fully connected by nvlink (1 hop)
-    Note that `pynvml` is not affected by `CUDA_VISIBLE_DEVICES`,
-    so it works on real physical device ids.
-    """
-    handles = [pynvml.nvmlDeviceGetHandleByIndex(i) for i in device_ids]
-    for i, handle in enumerate(handles):
-        for j, peer_handle in enumerate(handles):
-            if i < j:
-                try:
-                    p2p_status = pynvml.nvmlDeviceGetP2PStatus(
-                        handle, peer_handle, pynvml.NVML_P2P_CAPS_INDEX_NVLINK)
-                    if p2p_status != pynvml.NVML_P2P_STATUS_OK:
-                        return False
-                except pynvml.NVMLError as error:
-                    logger.error(
-                        "NVLink detection failed. This is normal if your"
-                        " machine has no NVLink equipped.",
-                        exc_info=error)
-                    return False
-    return True
-
-
 def _can_p2p(rank: int, world_size: int) -> bool:
     for i in range(world_size):
         if i == rank:
@@ -161,7 +113,7 @@ class CustomAllreduce:
         # test nvlink first, this will filter out most of the cases
         # where custom allreduce is not supported
         # this checks hardware and driver support for NVLink
-        full_nvlink = _is_full_nvlink(physical_device_ids)
+        full_nvlink = is_full_nvlink(physical_device_ids)
         if world_size > 2 and not full_nvlink:
             logger.warning(
                 "Custom allreduce is disabled because it's not supported on"

vllm/lora/punica.py

@@ -5,13 +5,14 @@ from typing import Optional
 import torch
 
 from vllm import _custom_ops as ops
+from vllm.utils import get_device_capability_stateless
 
 
 def _check_punica_support():
     if ops.is_custom_op_supported("_punica_C::dispatch_bgmv"):
         return
 
-    if torch.cuda.get_device_capability() < (8, 0):
+    if get_device_capability_stateless() < (8, 0):
         raise ImportError(
             "punica LoRA kernels require compute capability >= 8.0")
     else:

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -14,6 +14,7 @@ from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
 from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
     CompressionFormat, QuantizationArgs, QuantizationStrategy,
     find_first_name_or_class_match)
+from vllm.utils import get_device_capability_stateless
 
 
 class CompressedTensorsConfig(QuantizationConfig):
@@ -84,7 +85,7 @@ class CompressedTensorsConfig(QuantizationConfig):
         return []
 
     def _check_gptq_and_marlin_can_run(self):
-        capability = torch.cuda.get_device_capability()
+        capability = get_device_capability_stateless()
         capability = capability[0] * 10 + capability[1]
         if capability < 80:
             raise RuntimeError("The quantization config is not supported for ",

vllm/model_executor/layers/quantization/fp8.py

@@ -10,7 +10,7 @@ from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.utils import set_weight_attrs
-from vllm.utils import print_warning_once
+from vllm.utils import get_device_capability_stateless, print_warning_once
 
 ACTIVATION_SCHEMES = ["static", "dynamic"]
 
@@ -18,7 +18,7 @@ logger = init_logger(__name__)
 
 
 def cutlass_fp8_supported() -> bool:
-    capability = torch.cuda.get_device_capability()
+    capability = get_device_capability_stateless()
     capability = capability[0] * 10 + capability[1]
 
     return ops.cutlass_scaled_mm_supports_fp8(capability)

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -11,6 +11,7 @@ from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                set_weight_attrs)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.utils import get_device_capability_stateless
 
 logger = init_logger(__name__)
 
@@ -165,7 +166,7 @@ class GPTQMarlinConfig(QuantizationConfig):
             return False
 
         # If the capability of the device is too low, cannot convert.
-        major, minor = torch.cuda.get_device_capability()
+        major, minor = get_device_capability_stateless()
         device_capability = major * 10 + minor
         if device_capability < cls.get_min_capability():
             return False

vllm/model_executor/layers/quantization/utils/marlin_utils.py

@@ -12,8 +12,9 @@ from vllm.model_executor.layers.quantization.utils.marlin_perms import (
     marlin_perm, marlin_scale_perm, marlin_scale_perm_single)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     get_pack_factor, quantize_weights, sort_weights)
+from vllm.utils import get_device_capability_stateless
 
-__cuda_arch = torch.cuda.get_device_capability()
+__cuda_arch = get_device_capability_stateless()
 
 MARLIN_TILE = 16
 

vllm/model_executor/model_loader/loader.py

@@ -35,7 +35,7 @@ from vllm.model_executor.model_loader.weight_utils import (
 from vllm.model_executor.models.interfaces import (supports_lora,
                                                    supports_vision)
 from vllm.model_executor.utils import set_weight_attrs
-from vllm.utils import is_tpu
+from vllm.utils import get_device_capability_stateless, is_tpu
 
 logger = init_logger(__name__)
 
@@ -46,7 +46,7 @@ def _get_quantization_config(
     """Get the quantization config."""
     if model_config.quantization is not None:
         quant_config = get_quant_config(model_config, load_config)
-        capability = torch.cuda.get_device_capability()
+        capability = get_device_capability_stateless()
         capability = capability[0] * 10 + capability[1]
         if capability < quant_config.get_min_capability():
             raise ValueError(

vllm/utils.py

@@ -816,6 +816,63 @@ def cuda_device_count_stateless() -> int:
     return _cuda_device_count_stateless(envs.CUDA_VISIBLE_DEVICES)
 
 
+# NVML utils
+# Note that NVML is not affected by `CUDA_VISIBLE_DEVICES`,
+# all the related functions work on real physical device ids.
+# the major benefit of using NVML is that it will not initialize CUDA
+
+try:
+    import pynvml
+except ImportError:
+    # For non-NV devices
+    pynvml = None
+
+
+def with_nvml_context(fn):
+
+    @wraps(fn)
+    def wrapper(*args, **kwargs):
+        if pynvml is not None:
+            pynvml.nvmlInit()
+        try:
+            return fn(*args, **kwargs)
+        finally:
+            if pynvml is not None:
+                pynvml.nvmlShutdown()
+
+    return wrapper
+
+
+@with_nvml_context
+def is_full_nvlink(device_ids: List[int]) -> bool:
+    """
+    query if the set of gpus are fully connected by nvlink (1 hop)
+    """
+    handles = [pynvml.nvmlDeviceGetHandleByIndex(i) for i in device_ids]
+    for i, handle in enumerate(handles):
+        for j, peer_handle in enumerate(handles):
+            if i < j:
+                try:
+                    p2p_status = pynvml.nvmlDeviceGetP2PStatus(
+                        handle, peer_handle, pynvml.NVML_P2P_CAPS_INDEX_NVLINK)
+                    if p2p_status != pynvml.NVML_P2P_STATUS_OK:
+                        return False
+                except pynvml.NVMLError as error:
+                    logger.error(
+                        "NVLink detection failed. This is normal if your"
+                        " machine has no NVLink equipped.",
+                        exc_info=error)
+                    return False
+    return True
+
+
+@lru_cache(maxsize=8)
+@with_nvml_context
+def get_device_capability_stateless(device_id: int = 0) -> Tuple[int, int]:
+    handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)
+    return pynvml.nvmlDeviceGetCudaComputeCapability(handle)
+
+
 #From: https://stackoverflow.com/a/4104188/2749989
 def run_once(f):
 

vllm/worker/worker.py

@@ -16,6 +16,7 @@ from vllm.lora.request import LoRARequest
 from vllm.model_executor import set_random_seed
 from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
 from vllm.sequence import ExecuteModelRequest
+from vllm.utils import get_device_capability_stateless
 from vllm.worker.cache_engine import CacheEngine
 from vllm.worker.embedding_model_runner import EmbeddingModelRunner
 from vllm.worker.model_runner import GPUModelRunnerBase, ModelRunner
@@ -322,7 +323,7 @@ def init_worker_distributed_environment(
 def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
     # Check if the GPU supports the dtype.
     if torch_dtype == torch.bfloat16:
-        compute_capability = torch.cuda.get_device_capability()
+        compute_capability = get_device_capability_stateless()
         if compute_capability[0] < 8:
             gpu_name = torch.cuda.get_device_name()
             raise ValueError(