mio-19 commented 10 months ago

Prerequisites

Before submitting your issue, please ensure the following:

[x] I am running the latest version of PowerInfer. Development is rapid, and as of now, there are no tagged versions.
[ ] I have carefully read and followed the instructions in the README.md.
[ ] I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).

Expected Behavior

Current Behavior

Output of nvidia-smi after model is loaded

Wed Jan  3 07:23:06 2024       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.29.06              Driver Version: 545.29.06    CUDA Version: 12.3     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 3060 ...    Off | 00000000:01:00.0 Off |                  N/A |
| N/A   57C    P0              24W /  80W |   5228MiB /  6144MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|    0   N/A  N/A    146126      C   ./build/bin/main                           5222MiB |
+---------------------------------------------------------------------------------------+

Environment and Context

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

Physical (or virtual) hardware you are using, e.g. for Linux:

$ 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):                  16
  On-line CPU(s) list:   0-15
Vendor ID:               GenuineIntel
  Model name:            11th Gen Intel(R) Core(TM) i7-11800H @ 2.30GHz
    CPU family:          6
    Model:               141
    Thread(s) per core:  2
    Core(s) per socket:  8
    Socket(s):           1
    Stepping:            1
    CPU(s) scaling MHz:  89%
    CPU max MHz:         4600.0000
    CPU min MHz:         800.0000
    BogoMIPS:            4609.00
    Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi m
                         mx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon p
                         ebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq 
                         dtes64 monitor ds_cpl vmx est tm2 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 3dnowprefetch cpuid_
                         fault epb cat_l2 invpcid_single cdp_l2 ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi 
                         flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdt_a av
                         x512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512b
                         w avx512vl xsaveopt xsavec xgetbv1 xsaves split_lock_detect dtherm ida arat pln pts hwp hw
                         p_notify hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke avx512_vbmi2 gfni va
                         es vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid movdiri movdir64b fsrm avx5
                         12_vp2intersect md_clear ibt flush_l1d arch_capabilities
Virtualization features: 
  Virtualization:        VT-x
Caches (sum of all):     
  L1d:                   384 KiB (8 instances)
  L1i:                   256 KiB (8 instances)
  L2:                    10 MiB (8 instances)
  L3:                    24 MiB (1 instance)
NUMA:                    
  NUMA node(s):          1
  NUMA node0 CPU(s):     0-15
Vulnerabilities:         
  Gather data sampling:  Vulnerable: No microcode
  Itlb multihit:         Not affected
  L1tf:                  Not affected
  Mds:                   Not affected
  Meltdown:              Not affected
  Mmio stale data:       Not affected
  Retbleed:              Not affected
  Spec rstack overflow:  Not affected
  Spec store bypass:     Mitigation; Speculative Store Bypass disabled via prctl
  Spectre v1:            Mitigation; usercopy/swapgs barriers and __user pointer sanitization
  Spectre v2:            Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
  Srbds:                 Not affected
  Tsx async abort:       Not affected

Operating System, e.g. for Linux: Archlinux

$ uname -a

uname -a
Linux ***** 6.1.70-1-lts #1 SMP PREEMPT_DYNAMIC Mon, 01 Jan 2024 13:44:01 +0000 x86_64 GNU/Linux

SDK version, e.g. for Linux:

$ python3 --version
Python 3.11.6
$ make --version
GNU Make 4.4.1
Built for x86_64-pc-linux-gnu
Copyright (C) 1988-2023 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <https://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
g++ (GCC) 13.2.1 20230801
Copyright (C) 2023 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

Failure Information (for bugs)

Please help provide information about the failure / bug.

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.

step 1
step 2
step 3
etc.

Failure Logs

$ git log | head -1commit 74c5c5895b9acda1fc2224bb3ac87a9767d451f6

llama.cpp$ pip list | egrep "torch|numpy|sentencepiece"
egrep: warning: egrep is obsolescent; using grep -E
numpy                    1.26.2
sentencepiece            0.1.99
torch                    2.1.2

$ md5sum llama-13b-relu.powerinfer.gguf 
d8daf12964ce178e9f9cef6eaf3c7be1  llama-13b-relu.powerinfer.gguf

command used:

./build/bin/main -m ../llama-13b-relu.powerinfer.gguf 
-n 128 -t 8  --vram-budget 5 -p "Once upon a time"

bottom part of the log

llm_load_gpu_split: offloaded 0.00 MiB of FFN weights to GPU
llama_new_context_with_model: n_ctx      = 512
llama_new_context_with_model: freq_base  = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_new_context_with_model: kv self size  =  400.00 MB
llama_build_graph: non-view tensors processed: 684/1044
llama_build_graph: ****************************************************************
llama_build_graph: not all non-view tensors have been processed with a callback
llama_build_graph: this can indicate an inefficiency in the graph implementation
llama_build_graph: build with LLAMA_OFFLOAD_DEBUG for more info
llama_build_graph: ref: https://github.com/ggerganov/llama.cpp/pull/3837
llama_build_graph: ****************************************************************
llama_new_context_with_model: compute buffer total size = 8.25 MB
llama_new_context_with_model: VRAM scratch buffer: 6.69 MB
llama_new_context_with_model: total VRAM used: 5107.20 MB (model: 5100.51 MB, context: 6.69 MB)

system_info: n_threads = 8 / 16 | AVX = 1 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | 
sampling: 
        repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
        top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800
        mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
generate: n_ctx = 512, n_batch = 32, n_predict = 128, n_keep = 0

Once upon a time, the world had only one planet. Humans lived on this⏎

JoshuaLam21 commented 10 months ago

Same problem......

bluusun commented 10 months ago

Same here. After initial load model loads quickly but inference relies on CPU and is slow ....

hodlen commented 10 months ago

In this scenario, the GPU is indeed utilized for token generation, but the performance bottleneck primarily lies with the CPU. This imbalance causes the GPU to frequently wait for the CPU's computation results, leading to low GPU utilization.

To leverage optimal performance advantage with PowerInfer, we generally recommend using models that are 2-3x larger than the available VRAM. In such configurations, most of the densely activated tensors can be offloaded to the GPU, while the CPU processes only the sparsely activated tensors. So there is a more balanced workload distribution between these two sides.

Erickrus commented 5 months ago

I'm using T4 GPU. The same as above. Only GPU RAM 0.1 GB is used GPU RAM 0.1 / 15.0 GB

SJTU-IPADS / PowerInfer

GPU is not used after model is loaded #110