[Bug]: Can't load/compile Mixtral-8x7B-Instruct-v0.1 on TPU

[hosseinsarshar]

Your current environment

The output of `python collect_env.py`

Collecting environment information...
PyTorch version: 2.6.0.dev20241126+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.31.1
Libc version: glibc-2.35

Python version: 3.10.15 (main, Oct  3 2024, 07:27:34) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.8.0-1015-gcp-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
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:                        52 bits physical, 57 bits virtual
Byte Order:                           Little Endian
CPU(s):                               180
On-line CPU(s) list:                  0-179
Vendor ID:                            AuthenticAMD
Model name:                           AMD EPYC 9B14
CPU family:                           25
Model:                                17
Thread(s) per core:                   1
Core(s) per socket:                   90
Socket(s):                            2
Stepping:                             1
BogoMIPS:                             5199.99
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 tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext ssbd ibrs ibpb stibp vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_bf16 clzero xsaveerptr wbnoinvd arat avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            5.6 MiB (180 instances)
L1i cache:                            5.6 MiB (180 instances)
L2 cache:                             180 MiB (180 instances)
L3 cache:                             768 MiB (24 instances)
NUMA node(s):                         2
NUMA node0 CPU(s):                    0-89
NUMA node1 CPU(s):                    90-179
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 Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Mitigation; Safe RET
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; Retpolines; IBPB conditional; IBRS_FW; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] pyzmq==26.2.0
[pip3] torch==2.6.0.dev20241126+cpu
[pip3] torch-xla==2.6.0+git39e67b5
[pip3] torchvision==0.20.0.dev20241126+cpu
[pip3] transformers==4.46.3
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] pyzmq                     26.2.0                   pypi_0    pypi
[conda] torch                     2.6.0.dev20241126+cpu          pypi_0    pypi
[conda] torch-xla                 2.6.0+git39e67b5          pypi_0    pypi
[conda] torchvision               0.20.0.dev20241126+cpu          pypi_0    pypi
[conda] transformers              4.46.3                   pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.4.post2.dev207+ga4c4daf3
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
Could not collect

LD_LIBRARY_PATH=/home/hosseins/miniconda3/envs/vllm/lib/python3.10/site-packages/cv2/../../lib64:

Model Input Dumps

No response

🐛 Describe the bug

Can't load/compile Mixtral-8x7B-Instruct-v0.1 on TPU

I'm trying to run Mixtral-8x7B-Instruct-v0.1 on a TPU v6e-8 but it fails at the compilation phase - here is the launcher command:

vllm serve "mistralai/Mixtral-8x7B-Instruct-v0.1" --download_dir /dev/shm --num-scheduler-steps 4 --swap-space 16 --disable-log-requests --tensor_parallel_size=8 --max-model-len=2048

Here are the error logs - (I uploaded them as the trace is long):

Error-log-short.txt

Verbose error:
mixtral-error-log-verbose.txt

My setup:

pip list | grep torch
torch                             2.6.0.dev20241126+cpu
torch-xla                         2.6.0+git39e67b5
torchvision                       0.20.0.dev20241126+cpu

pip list | grep libtpu
libtpu_nightly                    0.1.dev20241122+nightly

pip list | grep jax
jax                               0.4.36.dev20241122
jaxlib                            0.4.36.dev20241122

Before submitting a new issue...

• Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

[avshalomman]

changing x = x * topk_weights.unsqueeze_ to the non in-place version x = x * topk_weights.unsqueeze fixes compilation, but the results are bad.
@robertgshaw2-neuralmagic seems like the pallas moe implementation is broken, I'm taking a look but if there's anyone with more expertise that can also take a look it will probably be more effective.

[bvrockwell]

cc @lsy323 (I don't know everyone's handle on github but will follow up on this!)

[hosseinsarshar]

Thanks for the fix - I tested the fix to serve Mixtral-8x7B and it's working as expected.

$ vllm serve "mistralai/Mixtral-8x7B-Instruct-v0.1" --download_dir /dev/shm --num-scheduler-steps 4 --swap-space 16 --disable-log-requests --tensor_parallel_size=8 --max-model-len=2048 --tokenizer=mistralai/Mixtral-8x7B-Instruct-v0.1


$ python benchmarks/benchmark_serving.py \
    --backend vllm \
    --model "mistralai/Mixtral-8x7B-Instruct-v0.1"  \
    --dataset-name sharegpt \
    --dataset-path ~/data/ShareGPT_V3_unfiltered_cleaned_split.json  \
    --num-prompts 1000
INFO 01-14 00:57:14 __init__.py:179] Automatically detected platform tpu.
Namespace(backend='vllm', base_url=None, host='localhost', port=8000, endpoint='/v1/completions', dataset=None, dataset_name='sharegpt', dataset_path='/home/hosseins/data/ShareGPT_V3_unfiltered_cleaned_split.json', max_concurrency=None, model='mistralai/Mixtral-8x7B-Instruct-v0.1', tokenizer=None, best_of=1, use_beam_search=False, num_prompts=1000, logprobs=None, request_rate=inf, burstiness=1.0, seed=0, trust_remote_code=False, disable_tqdm=False, profile=False, save_result=False, metadata=None, result_dir=None, result_filename=None, ignore_eos=False, percentile_metrics='ttft,tpot,itl', metric_percentiles='99', goodput=None, sonnet_input_len=550, sonnet_output_len=150, sonnet_prefix_len=200, sharegpt_output_len=None, random_input_len=1024, random_output_len=128, random_range_ratio=1.0, random_prefix_len=0, hf_subset=None, hf_split=None, hf_output_len=None, tokenizer_mode='auto')
/home/hosseins/vllm-moe-fix/benchmarks/benchmark_serving.py:793: FutureWarning: It is strongly recommended to run mistral models with `--tokenizer-mode "mistral"` to ensure correct encoding and decoding.
  tokenizer = get_tokenizer(tokenizer_id,
Starting initial single prompt test run...
Initial test run completed. Starting main benchmark run...
Traffic request rate: inf
Burstiness factor: 1.0 (Poisson process)
Maximum request concurrency: None
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:06<00:00, 15.09it/s]
============ Serving Benchmark Result ============
Successful requests:                     923       
Benchmark duration (s):                  66.29     
Total input tokens:                      212217    
Total generated tokens:                  34974     
Request throughput (req/s):              13.92     
Output token throughput (tok/s):         527.59    
Total Token throughput (tok/s):          3728.94   
---------------Time to First Token----------------
Mean TTFT (ms):                          19743.75  
Median TTFT (ms):                        18576.39  
P99 TTFT (ms):                           44316.19  
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          5427.93   
Median TPOT (ms):                        1891.74   
P99 TPOT (ms):                           35799.81  
---------------Inter-token Latency----------------
Mean ITL (ms):                           139.99    
Median ITL (ms):                         21.28     
P99 ITL (ms):                            3105.49   
==================================================