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[Bug]: GGUF Model Output Repeats Nonsensically #10600

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Mayflyyh opened this issue Nov 24, 2024 · 1 comment · May be fixed by #10675
Open
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[Bug]: GGUF Model Output Repeats Nonsensically #10600

Mayflyyh opened this issue Nov 24, 2024 · 1 comment · May be fixed by #10675
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@Mayflyyh
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Mayflyyh commented Nov 24, 2024 •
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Your current environment

The output of `python collect_env.py` 
Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
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 3.22.1
Libc version: glibc-2.35

Python version: 3.12.3 | packaged by Anaconda, Inc. | (main, May  6 2024, 19:46:43) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-78-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 4090
Nvidia driver version: 550.107.02
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:                   52 bits physical, 57 bits virtual
Byte Order:                      Little Endian
CPU(s):                          128
On-line CPU(s) list:             0-127
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Platinum 8352V CPU @ 2.10GHz
CPU family:                      6
Model:                           106
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       2
Stepping:                        6
CPU max MHz:                     3500.0000
CPU min MHz:                     800.0000
BogoMIPS:                        4000.00
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 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 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       3 MiB (64 instances)
L1i cache:                       2 MiB (64 instances)
L2 cache:                        80 MiB (64 instances)
L3 cache:                        108 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-31,64-95
NUMA node1 CPU(s):               32-63,96-127
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:          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; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pyzmq==26.0.3
[pip3] torch==2.5.1
[pip3] torchvision==0.20.1
[pip3] transformers==4.46.2
[pip3] triton==3.1.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-cublas-cu12        12.4.5.8                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.4.127                 pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.4.127                 pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.4.127                 pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.1.0.70                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.2.1.3                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.5.147               pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.6.1.9                 pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.3.1.170               pypi_0    pypi
[conda] nvidia-ml-py              12.560.30                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.21.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.4.127                 pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.4.127                 pypi_0    pypi
[conda] pyzmq                     26.0.3                   pypi_0    pypi
[conda] torch                     2.5.1                    pypi_0    pypi
[conda] torchvision               0.20.1                   pypi_0    pypi
[conda] transformers              4.46.2                   pypi_0    pypi
[conda] triton                    3.1.0                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.4.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
�[4mGPU0	NIC0	NIC1	CPU Affinity	NUMA Affinity	GPU NUMA ID�[0m
GPU0	 X 	SYS	SYS	0-31,64-95	0		N/A
NIC0	SYS	 X 	PIX				
NIC1	SYS	PIX	 X 				

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

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1

OMP_NUM_THREADS=16
MKL_NUM_THREADS=16
LD_LIBRARY_PATH=/root/miniconda3/lib/python3.12/site-packages/cv2/../../lib64:
CUDA_MODULE_LOADING=LAZY

Model Input Dumps

No response

🐛 Describe the bug

When using the following models: MaziyarPanahi/Llama-3.2-1B-Instruct-GGUF, MaziyarPanahi/Llama-3.2-3B-Instruct-GGUF, Qwen/Qwen2-0.5B-Instruct-GGUF, Qwen/Qwen2.5-0.5B-Instruct-GGUF, and MaziyarPanahi/Qwen2-1.5B-Instruct-GGUF, the models load correctly without errors. However, the output generated is nonsensical and consists only of repetitive characters such as "!!!!!!!!!!!!!!!!!!!!!!" instead of meaningful text.

The following code and output are generated using Qwen/Qwen2.5-0.5B-Instruct. The code and results for the other models are similar.

code:

import os

os.environ['VLLM_LOGGING_LEVEL'] = 'DEBUG'

from vllm import LLM, SamplingParams

# In this script, we demonstrate how to pass input to the chat method:
conversation = [
   {
      "role": "system",
      "content": "You are a helpful assistant"
   },
   {
      "role": "user",
      "content": "Hello"
   },
   {
      "role": "assistant",
      "content": "Hello! How can I assist you today?"
   },
   {
      "role": "user",
      "content": "Write an essay about the importance of higher education.",
   },
]

# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

# Create an LLM.
llm = LLM(model="./qwen2.5-0.5b-instruct-fp16.gguf",
         tokenizer="Qwen/Qwen2.5-0.5B-Instruct")
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.chat(conversation, sampling_params)

# Print the outputs.
for output in outputs:
   prompt = output.prompt
   generated_text = output.outputs[0].text
   print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

output:

python gguf_inference.py
INFO 11-24 11:30:27 config.py:1861] Downcasting torch.float32 to torch.float16.
INFO 11-24 11:30:32 config.py:350] This model supports multiple tasks: {'generate', 'embedding'}. Defaulting to 'generate'.
WARNING 11-24 11:30:32 config.py:428] gguf quantization is not fully optimized yet. The speed can be slower than non-quantized models.
INFO 11-24 11:30:32 llm_engine.py:249] Initializing an LLM engine (v0.6.4.post1) with config: model='./qwen2.5-0.5b-instruct-fp16.gguf', speculative_config=None, tokenizer='Qwen/Qwen2.5-0.5B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.GGUF, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=gguf, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=./qwen2.5-0.5b-instruct-fp16.gguf, num_scheduler_steps=1, chunked_prefill_enabled=False multi_step_stream_outputs=True, enable_prefix_caching=False, use_async_output_proc=True, use_cached_outputs=False, chat_template_text_format=string, mm_processor_kwargs=None, pooler_config=None)
tokenizer_config.json: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 7.30k/7.30k [00:00<00:00, 10.1MB/s]
vocab.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.78M/2.78M [00:00<00:00, 75.6MB/s]
merges.txt: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.67M/1.67M [00:00<00:00, 54.5MB/s]
tokenizer.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7.03M/7.03M [00:00<00:00, 92.1MB/s]
INFO 11-24 11:31:04 selector.py:135] Using Flash Attention backend.
DEBUG 11-24 11:31:04 parallel_state.py:983] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://172.17.0.4:56559 backend=nccl
INFO 11-24 11:31:04 model_runner.py:1072] Starting to load model ./qwen2.5-0.5b-instruct-fp16.gguf...
DEBUG 11-24 11:31:12 decorators.py:86] Inferred dynamic dimensions for forward method of <class 'vllm.model_executor.models.qwen2.Qwen2Model'>: ['input_ids', 'positions', 'intermediate_tensors', 'inputs_embeds']
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rotary_embedding enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op silu_and_mul enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:12 custom_op.py:68] custom op rms_norm enabled
DEBUG 11-24 11:31:20 utils.py:156] Loaded weight lm_head.qweight_type with shape torch.Size([1])
DEBUG 11-24 11:31:20 utils.py:156] Loaded weight lm_head.qweight with shape torch.Size([151936, 896])
/root/miniconda3/lib/python3.12/site-packages/torch/nested/__init__.py:226: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)
  return _nested.nested_tensor(
INFO 11-24 11:31:20 model_runner.py:1077] Loading model weights took 0.9613 GB
INFO 11-24 11:31:21 worker.py:232] Memory profiling results: total_gpu_memory=23.64GiB initial_memory_usage=1.44GiB peak_torch_memory=2.35GiB memory_usage_post_profile=1.44GiB non_torch_memory=0.48GiB kv_cache_size=18.44GiB gpu_memory_utilization=0.90
INFO 11-24 11:31:21 gpu_executor.py:113] # GPU blocks: 100725, # CPU blocks: 21845
INFO 11-24 11:31:21 gpu_executor.py:117] Maximum concurrency for 8192 tokens per request: 196.73x
INFO 11-24 11:31:23 model_runner.py:1400] Capturing cudagraphs for decoding. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
INFO 11-24 11:31:23 model_runner.py:1404] If out-of-memory error occurs during cudagraph capture, consider decreasing `gpu_memory_utilization` or switching to eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
INFO 11-24 11:31:34 model_runner.py:1518] Graph capturing finished in 12 secs, took 0.21 GiB
Processed prompts:   0%|                                                                    | 0/1 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]DEBUG 11-24 11:31:36 llm_engine.py:1525] Stopping remote worker execution loop.
Processed prompts: 100%|████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 18.62it/s, est. speed input: 895.23 toks/s, output: 298.31 toks/s]
Prompt: '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHello! How can I assist you today?<|im_end|>\n<|im_start|>user\nWrite an essay about the importance of higher education.<|im_end|>\n<|im_start|>assistant\n', Generated text: '!!!!!!!!!!!!!!!!'
[rank0]:[W1124 11:31:36.680739620 ProcessGroupNCCL.cpp:1250] Warning: WARNING: process group has NOT been destroyed before we destruct ProcessGroupNCCL. On normal program exit, the application should call destroy_process_group to ensure that any pending NCCL operations have finished in this process. In rare cases this process can exit before this point and block the progress of another member of the process group. This constraint has always been present,  but this warning has only been added since PyTorch 2.4 (function operator())

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@Mayflyyh Mayflyyh added the bug Something isn't working label Nov 24, 2024
@Isotr0py Isotr0py self-assigned this Nov 24, 2024
@Isotr0py Isotr0py linked a pull request Nov 26, 2024 that will close this issue
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@Mayflyyh I noticed you are using FP16 GGUF checkpoint for inference. Can you check if #10675 fixes your issue?

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[Bugfix] Fix GGUF inference with FP16 unquantized checkpoint Isotr0py/vllm
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@Isotr0py @Mayflyyh