Closed pandada8 closed 1 day ago
pandada8
commented
1 day ago https://github.com/vllm-project/vllm/issues/2248#issuecomment-1868866988 suggest counting used vRAM before loading model and exclude it when calculating the available_kv_cache_memory. it seems works well when you staggered startup different vLLM worker in the same card
pandada8
commented
1 day ago proposed change
diff --git a/vllm/worker/worker.py b/vllm/worker/worker.py
index 80fd7bc3..793812e5 100644
--- a/vllm/worker/worker.py
+++ b/vllm/worker/worker.py
@@ -150,6 +150,8 @@ class Worker(LocalOrDistributedWorkerBase):
set_random_seed(self.model_config.seed)
def load_model(self):
+ free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info()
+ self.gpu_mem_pre_occupied = total_gpu_memory - free_gpu_memory
self.model_runner.load_model()
def save_sharded_state(
@@ -214,9 +216,7 @@ class Worker(LocalOrDistributedWorkerBase):
if non_torch_allocations > 0:
peak_memory += non_torch_allocations
- available_kv_cache_memory = (
- total_gpu_memory * self.cache_config.gpu_memory_utilization -
- peak_memory)
+ available_kv_cache_memory = total_gpu_memory * gpu_memory_utilization - (peak_memory - self.gpu_mem_pre_occupied)
# Calculate the number of blocks that can be allocated with the
# profiled peak memory.
DarkLight1337
commented
1 day ago Please see #10511
Your current environment
The output of `python collect_env.py`
```text PyTorch version: 2.5.1+cu124 Is debug build: False CUDA used to build PyTorch: 12.4 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.4 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.35 Python version: 3.12.7 (main, Oct 1 2024, 08:52:12) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-5.15.0-73-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A100-SXM4-80GB Nvidia driver version: 535.113.01 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 16 On-line CPU(s) list: 0-15 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Platinum 8369B CPU @ 2.90GHz CPU family: 6 Model: 106 Thread(s) per core: 2 Core(s) per socket: 8 Socket(s): 1 Stepping: 6 BogoMIPS: 5799.99 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves wbnoinvd arat avx512vbmi avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm arch_capabilities Hypervisor vendor: KVM Virtualization type: full L1d cache: 384 KiB (8 instances) L1i cache: 256 KiB (8 instances) L2 cache: 10 MiB (8 instances) L3 cache: 48 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-15 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown Vulnerability Retbleed: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] flashinfer==0.1.6+cu121torch2.4 [pip3] numpy==1.26.4 [pip3] nvidia-cublas-cu12==12.4.5.8 [pip3] nvidia-cuda-cupti-cu12==12.4.127 [pip3] nvidia-cuda-nvrtc-cu12==12.4.127 [pip3] nvidia-cuda-runtime-cu12==12.4.127 [pip3] nvidia-cudnn-cu12==9.1.0.70 [pip3] nvidia-cufft-cu12==11.2.1.3 [pip3] nvidia-curand-cu12==10.3.5.147 [pip3] nvidia-cusolver-cu12==11.6.1.9 [pip3] nvidia-cusparse-cu12==12.3.1.170 [pip3] nvidia-ml-py==12.560.30 [pip3] nvidia-nccl-cu12==2.21.5 [pip3] nvidia-nvjitlink-cu12==12.4.127 [pip3] nvidia-nvtx-cu12==12.4.127 [pip3] pyzmq==26.2.0 [pip3] torch==2.5.1 [pip3] torchvision==0.20.1 [pip3] transformers==4.46.2 [pip3] triton==3.1.0 [conda] Could not collect ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.6.4.post1 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X 0-15 N/A N/A Legend: X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) PIX = Connection traversing at most a single PCIe bridge NV# = Connection traversing a bonded set of # NVLinks NVIDIA_VISIBLE_DEVICES=all NVIDIA_REQUIRE_CUDA=cuda>=12.4 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=525,driver<526 brand=unknown,driver>=525,driver<526 brand=nvidia,driver>=525,driver<526 brand=nvidiartx,driver>=525,driver<526 brand=geforce,driver>=525,driver<526 brand=geforcertx,driver>=525,driver<526 brand=quadro,driver>=525,driver<526 brand=quadrortx,driver>=525,driver<526 brand=titan,driver>=525,driver<526 brand=titanrtx,driver>=525,driver<526 brand=tesla,driver>=535,driver<536 brand=unknown,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=geforce,driver>=535,driver<536 brand=geforcertx,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=titan,driver>=535,driver<536 brand=titanrtx,driver>=535,driver<536 NVIDIA_DRIVER_CAPABILITIES=compute,utility VLLM_USAGE_SOURCE=production-docker-image CUDA_VERSION=12.4.1 LD_LIBRARY_PATH=/usr/local/lib/python3.12/dist-packages/cv2/../../lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64 CUDA_MODULE_LOADING=LAZY ```Model Input Dumps
No response
🐛 Describe the bug
I want to run Qwen2.5-14B-Instruct-GPTQ-Int4 and Qwen2.5-72B-Instruct-GPTQ-Int4 with one 80G A100.
with following command
Both commands succeed when running separately, consuming approximately 75% and 20% of the vRAM respectively. However when you start the 72B first and then 14b. the 14b will refused to start with Exception
Note that the 14B vllm server outputs the following log
which has negative kv_cache_size.
it looks like currently https://github.com/vllm-project/vllm/blob/1cfde82ffd6edfca6029a7e312c848386ea322c1/vllm/worker/worker.py#L213-L219 peak_memory would include the other vllm used vram and result in negative available_kv_cache_memory
Before submitting a new issue...