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PygmalionAI / aphrodite-engine

Large-scale LLM inference engine
https://aphrodite.pygmalion.chat
GNU Affero General Public License v3.0
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[Bug]: ```QKVParallelLinear``` object has no attribute ```state``` #585

Closed francescotaioli closed 1 week ago

francescotaioli commented 3 weeks ago

Your current environment

PyTorch version: 2.3.0
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: (conda-forge gcc 11.3.0-19) 11.3.0
Clang version: Could not collect 
CMake version: version 3.30.2
Libc version: glibc-2.35
Python version: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:36:13) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-6.5.0-26-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 TITAN RTX
GPU 1: NVIDIA TITAN RTX
GPU 2: NVIDIA TITAN RTX
GPU 3: NVIDIA TITAN RTX

Nvidia driver version: 550.54.15
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.0.0
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):                             16
On-line CPU(s) list:                0-15
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Core(TM) i7-9800X CPU @ 3.80GHz
CPU family:                         6
Model:                              85
Thread(s) per core:                 2
Core(s) per socket:                 8
Socket(s):                          1
Stepping:                           4
CPU max MHz:                        4500,0000
CPU min MHz:                        1200,0000
BogoMIPS:                           7599.80
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 art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx 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 ssbd mba ibrs ibpb stibp tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi md_clear flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          256 KiB (8 instances)
L1i cache:                          256 KiB (8 instances)
L2 cache:                           8 MiB (8 instances)
L3 cache:                           16,5 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-15
Vulnerability Gather data sampling: Mitigation; Microcode
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:             Mitigation; IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; IBRS, IBPB conditional, 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==2.3.0
[pip3] torchvision==0.18.0
[pip3] triton==2.3.0
[conda] blas                      2.16                        mkl    conda-forge
[conda] libblas                   3.8.0                    16_mkl    conda-forge
[conda] libcblas                  3.8.0                    16_mkl    conda-forge
[conda] liblapack                 3.8.0                    16_mkl    conda-forge
[conda] liblapacke                3.8.0                    16_mkl    conda-forge
[conda] mkl                       2020.2                      256  
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] pytorch                   2.3.0           py3.11_cuda12.1_cudnn8.9.2_0    pytorch
[conda] pytorch-cuda              12.1                 ha16c6d3_5    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchtriton               2.3.0                     py311    pytorch
[conda] torchvision               0.18.0                   pypi_0    pypiROCM Version: Could not collect 
Aphrodite Version: 0.5.4-dev
Aphrodite Build Flags:
CUDA Archs: Not Set; ROCm: Disabled

🐛 Describe the bug

Installation from rc 054: git clone -b rc_054 https://github.com/PygmalionAI/aphrodite-engine.git

Running CUDA_VISIBLE_DEVICES="0,1" aphrodite run mistralai/Mistral-7B-Instruct-v0.3 --dtype=float16 --tensor-parallel-size=2 --gpu-memory-utilization 0.6 --load-in-8bit causes AttributeError: 'QKVParallelLinear' object has no attribute 'state'

Same error with CUDA_VISIBLE_DEVICES="0,1" aphrodite run mistralai/Mistral-7B-Instruct-v0.3 --dtype=float16 --tensor-parallel-size=1 --gpu-memory-utilization 0.6 --load-in-8bit

same error with different models (i.e llama 3.1)

AlpinDale commented 3 weeks ago

Unfortunately, the --load-in-{4bit,8bit,smooth} quants are broken in the rc_054 branch. If you have an Ampere (sm_80) or above GPU, you can do -q fp8 and it'll work the same, but much faster. Otherwise, you'll need to quantize the model with GPTQ or EETQ beforehand. Sorry for the inconvenience.

AlpinDale commented 2 weeks ago

Ah right, I forgot to mention...

rc_054 branch has support for other on-the-fly quant methods as well; but they're not as performant at ~4bit range yet.

For bitsandbytes (NF4): --load-format bitsandbytes -q bitsandbytes

For deepspeedfp (4bit, 6bit, 8bit, 12bit): -q deepspeedfp --deepspeed_fp_bits 6

francescotaioli commented 1 week ago

Thanks @AlpinDale for the answer.