[Bug]: Unsloth bitsandbytes quantized model cannot be run due to: KeyError: 'layers.42.mlp.down_proj.weight.absmax

[kerem-coemert]

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 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.29.2
Libc version: glibc-2.35

Python version: 3.11.7 (main, Dec 15 2023, 18:12:31) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-35-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.5.119
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA RTX A6000
GPU 1: NVIDIA RTX A6000

Nvidia driver version: 525.147.05
cuDNN version: Could not collect
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:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             64
On-line CPU(s) list:                0-63
Vendor ID:                          AuthenticAMD
Model name:                         AMD Ryzen Threadripper PRO 5975WX 32-Cores
CPU family:                         25
Model:                              8
Thread(s) per core:                 2
Core(s) per socket:                 32
Socket(s):                          1
Stepping:                           2
Frequency boost:                    enabled
CPU max MHz:                        7006.6401
CPU min MHz:                        1800.0000
BogoMIPS:                           7187.24
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 aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin brs arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm
Virtualization:                     AMD-V
L1d cache:                          1 MiB (32 instances)
L1i cache:                          1 MiB (32 instances)
L2 cache:                           16 MiB (32 instances)
L3 cache:                           128 MiB (4 instances)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-63
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 Retbleed:             Not affected
Vulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode
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 always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] mypy==1.8.0
[pip3] mypy-extensions==1.0.0
[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==26.2.0
[pip3] torch==2.5.1
[pip3] torchvision==0.20.1
[pip3] transformers==4.46.3
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.1.0
[pip3] vllm-nccl-cu12==2.18.1.0.4.0
[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                     26.2.0                   pypi_0    pypi
[conda] torch                     2.5.1                    pypi_0    pypi
[conda] torchvision               0.20.1                   pypi_0    pypi
[conda] transformers              4.46.3                   pypi_0    pypi
[conda] transformers-stream-generator 0.0.5                    pypi_0    pypi
[conda] triton                    3.1.0                    pypi_0    pypi
[conda] vllm-nccl-cu12            2.18.1.0.4.0             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:
GPU0    GPU1    CPU Affinity    NUMA Affinity
GPU0     X      SYS     0-63            N/A
GPU1    SYS      X      0-63            N/A

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

LD_LIBRARY_PATH=/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/cv2/../../lib64:
CUDA_MODULE_LOADING=LAZY

Model Input Dumps

No response

🐛 Describe the bug

Hello,

I would like to run unsloth/Meta-Llama-3.1-70B-bnb-4bit using the AsyncLLMEngine in the following way:

return AsyncLLMEngineWithLoRA(
            async_llm_engine=AsyncLLMEngine.from_engine_args(
                AsyncEngineArgs(
                    model=base_model_config.model_name,
                    enable_lora=finetuned_model_config is not None,
                    max_loras=1,
                    device=device,
                    trust_remote_code=True,
                    # max_model_len=121296
                )
            ),
            lora_request=lora_request,
        )

Please note that lora_request is None and enable_lora is False in this case. I get:

ank0]: Traceback (most recent call last):
[rank0]:   File "<frozen runpy>", line 198, in _run_module_as_main
[rank0]:   File "<frozen runpy>", line 88, in _run_code
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/uvicorn/__main__.py", line 4, in <module>
[rank0]:     uvicorn.main()
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/click/core.py", line 1157, in __call__
[rank0]:     return self.main(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/click/core.py", line 1078, in main
[rank0]:     rv = self.invoke(ctx)
[rank0]:          ^^^^^^^^^^^^^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/click/core.py", line 1434, in invoke
[rank0]:     return ctx.invoke(self.callback, **ctx.params)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/click/core.py", line 783, in invoke
[rank0]:     return __callback(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/uvicorn/main.py", line 418, in main
[rank0]:     run(
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/uvicorn/main.py", line 587, in run
[rank0]:     server.run()
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/uvicorn/server.py", line 62, in run
[rank0]:     return asyncio.run(self.serve(sockets=sockets))
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/asyncio/runners.py", line 190, in run
[rank0]:     return runner.run(main)
[rank0]:            ^^^^^^^^^^^^^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/asyncio/runners.py", line 118, in run
[rank0]:     return self._loop.run_until_complete(task)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "uvloop/loop.pyx", line 1517, in uvloop.loop.Loop.run_until_complete
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/uvicorn/server.py", line 69, in serve
[rank0]:     config.load()
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/uvicorn/config.py", line 458, in load
[rank0]:     self.loaded_app = import_from_string(self.app)
[rank0]:                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/uvicorn/importer.py", line 21, in import_from_string
[rank0]:     module = importlib.import_module(module_str)
[rank0]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/importlib/__init__.py", line 126, in import_module
[rank0]:     return _bootstrap._gcd_import(name[level:], package, level)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "<frozen importlib._bootstrap>", line 1204, in _gcd_import
[rank0]:   File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
[rank0]:   File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
[rank0]:   File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
[rank0]:   File "<frozen importlib._bootstrap_external>", line 940, in exec_module
[rank0]:   File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
[rank0]:   File "/home/kerem/ai.webservice.local-llm-inference/src/main.py", line 2, in <module>
[rank0]:     from local_llm_inference.router import ROUTER
[rank0]:   File "/home/kerem/ai.webservice.local-llm-inference/src/local_llm_inference/router.py", line 92, in <module>
[rank0]:     LLM: InferenceModel | AsyncLLMEngineWithLoRA = init_llm(
[rank0]:                                                    ^^^^^^^^^
[rank0]:   File "/home/kerem/ai.webservice.local-llm-inference/src/local_llm_inference/infer.py", line 91, in init_llm
[rank0]:     async_llm_engine=AsyncLLMEngine.from_engine_args(
[rank0]:                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/engine/async_llm_engine.py", line 691, in from_engine_args
[rank0]:     engine = cls(
[rank0]:              ^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/engine/async_llm_engine.py", line 578, in __init__
[rank0]:     self.engine = self._engine_class(*args, **kwargs)
[rank0]:                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/engine/async_llm_engine.py", line 264, in __init__
[rank0]:     super().__init__(*args, **kwargs)
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/engine/llm_engine.py", line 347, in __init__
[rank0]:     self.model_executor = executor_class(vllm_config=vllm_config, )
[rank0]:                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/executor/executor_base.py", line 36, in __init__
[rank0]:     self._init_executor()
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/executor/gpu_executor.py", line 40, in _init_executor
[rank0]:     self.driver_worker.load_model()
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/worker/worker.py", line 152, in load_model
[rank0]:     self.model_runner.load_model()
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/worker/model_runner.py", line 1074, in load_model
[rank0]:     self.model = get_model(vllm_config=self.vllm_config)
[rank0]:                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/model_executor/model_loader/__init__.py", line 12, in get_model
[rank0]:     return loader.load_model(vllm_config=vllm_config)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/model_executor/model_loader/loader.py", line 334, in load_model
[rank0]:     model.load_weights(self._get_all_weights(model_config, model))
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/model_executor/models/llama.py", line 586, in load_weights
[rank0]:     loader.load_weights(
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/model_executor/models/utils.py", line 229, in load_weights
[rank0]:     autoloaded_weights = list(self._load_module("", self.module, weights))
[rank0]:                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/model_executor/models/utils.py", line 190, in _load_module
[rank0]:     yield from self._load_module(prefix,
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/model_executor/models/utils.py", line 175, in _load_module
[rank0]:     module_load_weights(weights)
[rank0]:   File "/home/kerem/miniconda3/envs/webservice-local-llm-inference/lib/python3.11/site-packages/vllm/model_executor/models/llama.py", line 407, in load_weights
[rank0]:     param = params_dict[name]
[rank0]:             ~~~~~~~~~~~^^^^^^
[rank0]: KeyError: 'layers.42.mlp.down_proj.weight.absmax'

although I was under the impression that with the newer vllm versions bnb quantized models are supported.

Could it be something like what I do in the following function, which requires manual patching to the weights in some cases for the PEFTModels:

def remove_base_layer_weights(finetuned_model_path: Path, logger: Logger) -> None:
    """
    This is necessary because when loading the LoRA weights for a PEFT-finetuned model,
    we get:
    lib/python3.11/site-packages/vllm/lora/utils.py", line 33, in parse_fine_tuned_lora_name
    assert parts[-2] == "lora_A" or parts[-2] == "lora_B"
                                    ^^^^^^^^^^^^^^^^^^^^^
    AssertionError

    See: https://github.com/vllm-project/vllm/issues/3404#issuecomment-2007431273
    """
    import safetensors.torch
    from torch import Tensor

    adapter_model_path: Path = finetuned_model_path.joinpath(
        "adapter_model.safetensors"
    )
    tensors: dict[str, Tensor] = safetensors.torch.load_file(
        filename=adapter_model_path
    )
    nonlora_keys = []
    for k in list(tensors.keys()):
        if "lora" not in k:
            nonlora_keys.append(k)
    logger.info(
        f"Deleting {nonlora_keys=}. See: https://github.com/vllm-project/vllm/issues/3404#issuecomment-2007431273"
    )
    for k in nonlora_keys:
        del tensors[k]
    safetensors.torch.save_file(tensors, adapter_model_path)

Thanks in advance!

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