llama3-8b-instruct awq量化oom

[Edisonwei54]

Describe the bug What the bug is, and how to reproduce, better with screenshots(描述bug以及复现过程，最好有截图) 微调后的llama3-8B拿去量化，4*24G显存都会oom？已经把quant_n_samples和quant_seqlen减小到32/128了 CUDA_VISIBLE_DEVICES=0,1,2,3 swift export
--model_type llama3-8b-instruct
--ckpt_dir/home/greatwall/app/edison/output/llama3-8b-instruct/v2-20240427-073919/checkpoint-438-merged
--quant_bits 4
--quant_method awq
--quant_device_map auto
--quant_n_samples 32
--dataset ms-bench-mini
--quant_seqlen 128

Your hardware and system info Write your system info like CUDA version/system/GPU/torch version here(在这里给出硬件信息和系统信息，如CUDA版本，系统，GPU型号和torch版本等) PyTorch version: 2.1.2+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 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: version 3.26.4 Libc version: glibc-2.35

Python version: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-102-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.1.105 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090 GPU 1: NVIDIA GeForce RTX 3090 GPU 2: NVIDIA GeForce RTX 3090 GPU 3: NVIDIA GeForce RTX 3090

Nvidia driver version: 535.171.04 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0 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): 88 On-line CPU(s) list: 0-87 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) CPU E5-2696 v4 @ 2.20GHz CPU family: 6 Model: 79 Thread(s) per core: 2 Core(s) per socket: 22 Socket(s): 2 Stepping: 1 CPU max MHz: 3700.0000 CPU min MHz: 1200.0000 BogoMIPS: 4400.16 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d Virtualization: VT-x L1d cache: 1.4 MiB (44 instances) L1i cache: 1.4 MiB (44 instances) L2 cache: 11 MiB (44 instances) L3 cache: 110 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-21,44-65 NUMA node1 CPU(s): 22-43,66-87 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Meltdown: Mitigation; PTI Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: 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; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable

Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] torch==2.1.2 [pip3] torchvision==0.16.2 [pip3] triton==2.1.0 [conda] numpy 1.26.4 pypi_0 pypi [conda] torch 2.1.2 pypi_0 pypi [conda] torchvision 0.16.2 pypi_0 pypi [conda] triton 2.1.0 pypi_0 pypiROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.3.0 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 GPU2 GPU3 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X PHB SYS SYS 0-21,44-65 0 N/A GPU1 PHB X SYS SYS 0-21,44-65 0 N/A GPU2 SYS SYS X PHB 22-43,66-87 1 N/A GPU3 SYS SYS PHB X 22-43,66-87 1 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

Additional context Add any other context about the problem here(在这里补充其他信息) 这样的配置量化qwen2-14B都没oom

[Edisonwei54]

@Jintao-Huang @tastelikefeet

[Jintao-Huang]

用alpaca-zh alpaca-en量化好了

--quant_device_map cpu
--dataset alpaca-zh alpaca-en