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abetlen / llama-cpp-python

Python bindings for llama.cpp
https://llama-cpp-python.readthedocs.io
MIT License
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llama-cpp-python does not work with new released model gemma from Google #1211

Closed jingnanzhou closed 6 months ago

jingnanzhou commented 6 months ago

Prerequisites

Please answer the following questions for yourself before submitting an issue.

Expected Behavior

Please provide a detailed written description of what you were trying to do, and what you expected llama-cpp-python to do. run the following code model = Llama(model_path=model_path, chat_format="llama-2")

Current Behavior

error message: Segmentation fault (core dumped) Please provide a detailed written description of what llama-cpp-python did, instead. The gemma gguf file is https://huggingface.co/mlabonne/gemma-2b-GGUF/resolve/main/gemma-2b.Q4_K_S.gguf

Environment and Context

ubuntu 20 Please provide detailed information about your computer setup. This is important in case the issue is not reproducible except for under certain specific conditions.

$ lscpu Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 39 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 4 On-line CPU(s) list: 0-3 Vendor ID: GenuineIntel Model name: Intel(R) Core(TM) i5-4300M CPU @ 2.60GHz CPU family: 6 Model: 60 Thread(s) per core: 2 Core(s) per socket: 2 Socket(s): 1 Stepping: 3 CPU max MHz: 3300.0000 CPU min MHz: 800.0000 BogoMIPS: 5188.44 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 rdts cp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est t m2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm cpuid_fault ep b invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsa veopt dtherm ida arat pln pts md_clear flush_l1d Virtualization features: Virtualization: VT-x Caches (sum of all):
L1d: 64 KiB (2 instances) L1i: 64 KiB (2 instances) L2: 512 KiB (2 instances) L3: 3 MiB (1 instance) NUMA:
NUMA node(s): 1 NUMA node0 CPU(s): 0-3 Vulnerabilities:
Gather data sampling: Not affected Itlb multihit: KVM: Mitigation: VMX disabled L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable Mds: Mitigation; Clear CPU buffers; SMT vulnerable Meltdown: Mitigation; PTI Mmio stale data: Unknown: No mitigations Retbleed: Not affected Spec rstack overflow: Not affected Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected Srbds: Mitigation; Microcode Tsx async abort: Not affected

$ uname -a Linux isbdev 5.15.0-94-generic #104-Ubuntu SMP Tue Jan 9 15:25:40 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux

$ python3 --version
Python 3.10.12
$ make --version
GNU Make 4.3
Built for x86_64-pc-linux-gnu
Copyright (C) 1988-2020 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.
$ g++ --version

gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0

Failure Information (for bugs)

Please help provide information about the failure if this is a bug. If it is not a bug, please remove the rest of this template.

Steps to Reproduce

Please provide detailed steps for reproducing the issue. We are not sitting in front of your screen, so the more detail the better.

  1. step 1
  2. step 2
  3. step 3
  4. etc.

Note: Many issues seem to be regarding functional or performance issues / differences with llama.cpp. In these cases we need to confirm that you're comparing against the version of llama.cpp that was built with your python package, and which parameters you're passing to the context.

Try the following:

  1. git clone https://github.com/abetlen/llama-cpp-python
  2. cd llama-cpp-python
  3. rm -rf _skbuild/ # delete any old builds
  4. python -m pip install .
  5. cd ./vendor/llama.cpp
  6. Follow llama.cpp's instructions to cmake llama.cpp
  7. Run llama.cpp's ./main with the same arguments you previously passed to llama-cpp-python and see if you can reproduce the issue. If you can, log an issue with llama.cpp

Failure Logs

Please include any relevant log snippets or files. If it works under one configuration but not under another, please provide logs for both configurations and their corresponding outputs so it is easy to see where behavior changes.

Also, please try to avoid using screenshots if at all possible. Instead, copy/paste the console output and use Github's markdown to cleanly format your logs for easy readability.

Example environment info:

llama-cpp-python$ git log | head -1
commit 47b0aa6e957b93dbe2c29d53af16fbae2dd628f2

llama-cpp-python$ python3 --version
Python 3.10.10

llama-cpp-python$ pip list | egrep "uvicorn|fastapi|sse-starlette|numpy"
fastapi                  0.95.0
numpy                    1.24.3
sse-starlette            1.3.3
uvicorn                  0.21.1

llama-cpp-python/vendor/llama.cpp$ git log | head -3
commit 66874d4fbcc7866377246efbcee938e8cc9c7d76
Author: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
Date:   Thu May 25 20:18:01 2023 -0600
abetlen commented 6 months ago

https://github.com/abetlen/llama-cpp-python/issues/1207#issuecomment-1958678473

The first version of the model files had an issue that's since been resolved.