[Usage]: how should I do data parallelism using vLLM?

[YuWang916]

Your current environment

Collecting environment information...
PyTorch version: 2.2.1+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: CBL-Mariner/Linux (x86_64)
GCC version: (GCC) 11.2.0
Clang version: Could not collect
CMake version: version 3.21.4
Libc version: glibc-2.35

Python version: 3.10.2 (main, Feb 22 2024, 00:00:03) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.138.1-4.cm2-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100-SXM4-80GB
GPU 1: NVIDIA A100-SXM4-80GB
GPU 2: NVIDIA A100-SXM4-80GB
GPU 3: NVIDIA A100-SXM4-80GB

Nvidia driver version: 525.85.12
cuDNN version: Probably one of the following:
/usr/lib/libcudnn.so.8.9.5
/usr/lib/libcudnn_adv_infer.so.8.9.5
/usr/lib/libcudnn_adv_train.so.8.9.5
/usr/lib/libcudnn_cnn_infer.so.8.9.5
/usr/lib/libcudnn_cnn_train.so.8.9.5
/usr/lib/libcudnn_ops_infer.so.8.9.5
/usr/lib/libcudnn_ops_train.so.8.9.5
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: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 256
On-line CPU(s) list: 0-255
Vendor ID: AuthenticAMD
Model name: AMD EPYC 7763 64-Core Processor
CPU family: 25
Model: 1
Thread(s) per core: 2
Core(s) per socket: 64
Socket(s): 2
Stepping: 1
Frequency boost: enabled
CPU max MHz: 3529.0520
CPU min MHz: 1500.0000
BogoMIPS: 4899.80
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca
Virtualization: AMD-V
L1d cache: 4 MiB (128 instances)
L1i cache: 4 MiB (128 instances)
L2 cache: 64 MiB (128 instances)
L3 cache: 512 MiB (16 instances)
NUMA node(s): 8
NUMA node0 CPU(s): 0-15,128-143
NUMA node1 CPU(s): 16-31,144-159
NUMA node2 CPU(s): 32-47,160-175
NUMA node3 CPU(s): 48-63,176-191
NUMA node4 CPU(s): 64-79,192-207
NUMA node5 CPU(s): 80-95,208-223
NUMA node6 CPU(s): 96-111,224-239
NUMA node7 CPU(s): 112-127,240-255
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Mitigation; safe RET, no microcode
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; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] flake8==4.0.1.1
[pip3] flake8-annotations-complexity==0.0.6.2
[pip3] flake8-bugbear==20.1.4
[pip3] flake8-builtins==1.4.2
[pip3] flake8-pie==0.5.0.1
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.24.3
[pip3] nvidia-nccl-cu11==2.19.3
[pip3] pytorch-lightning==2.2.3
[pip3] torch==2.2.1+cu118
[pip3] torch-lib==0.1.25
[pip3] torchmetrics==1.3.1
[pip3] triton==2.2.0
[pip3] vllm-nccl-cu11==2.18.1.0.4.0
[conda] Could not collectROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.4.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 CPU Affinity NUMA Affinity
GPU0 X NV12 NV12 NV12 48-63,176-191 3
GPU1 NV12 X NV12 NV12 48-63,176-191 3
GPU2 NV12 NV12 X NV12 16-31,144-159 1
GPU3 NV12 NV12 NV12 X 80-95,208-223 5

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

How would you like to use vllm

I want to run offline inference of model mistralai/Mixtral-8x22B-v0.1. I have multiple nodes and within each node, there are 8 A100 GPUs. My data is pretty big, and I would like to run offline inference using multiple nodes, so that

• Data is partitioned by the number of nodes (data parellel size = # of nodes)
• Within each node, perform Tensor Parallelism (tensor_parallel_size=8)

[sethkimmel3]

@YuWang916 did you figure this out? Working on the same thing. cc: @simon-mo

[simon-mo]

checkout the Ray data example here, which does sharding automatically for you.

https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_distributed.py

[sethkimmel3]

Thanks @simon-mo - I did see that example. What I'm curious about is whether you can do both tensor parallelism and data parallelism together. For example: if I'm loading a 70b parameter model onto 4x 40GB GPU's, can I both use tensor parallelism to split the model across the GPU's, and have 4 vllm instantiations - 1 per gpu? Intuitively it seems like the answer should be no, but you know may better.

[simon-mo]

Once TP is enabled the model is sharded across GPU and won't have the full weights per rank. Curious to hear why would be the reason behind this setup?

[sethkimmel3]

I want to maximize throughput for batch use cases. I figured if vllm sits in one GPU and has access to weights across all four (in above case), it could theoretically be parallelized (probably something less than a 4x gain because of clock cycles). Am I crazy or is something like this possible?