[Usage]: AttributeError: 'Parameter' object has no attribute 'load_column_parallel_weight' #24950
Description
Your current environment
The output of `python collect_env.py`
INFO 09-16 15:35:11 [__init__.py:239] Automatically detected platform cuda.
Collecting environment information...
/root/miniconda3/lib/python3.11/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.
warnings.warn("Setuptools is replacing distutils.")
==============================
System Info
==============================
OS : Ubuntu 20.04.4 LTS (x86_64)
GCC version : (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version : Could not collect
CMake version : version 3.16.3
Libc version : glibc-2.31
==============================
PyTorch Info
==============================
PyTorch version : 2.6.0+cu124
Is debug build : False
CUDA used to build PyTorch : 12.4
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.11.5 (main, Sep 11 2023, 13:54:46) [GCC 11.2.0] (64-bit runtime)
Python platform : Linux-5.15.0-89-generic-x86_64-with-glibc2.31
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : Could not collect
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration :
GPU 0: NVIDIA H200
GPU 1: NVIDIA H200
GPU 2: NVIDIA H200
GPU 3: NVIDIA H200
GPU 4: NVIDIA H200
GPU 5: NVIDIA H200
GPU 6: NVIDIA H200
GPU 7: NVIDIA H200
Nvidia driver version : 570.133.20
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
Address sizes: 46 bits physical, 57 bits virtual
CPU(s): 192
On-line CPU(s) list: 0
Off-line CPU(s) list: 1-191
Thread(s) per core: 0
Core(s) per socket: 48
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 207
Model name: INTEL(R) XEON(R) PLATINUM 8558
Stepping: 2
CPU MHz: 3834.712
CPU max MHz: 4000.0000
CPU min MHz: 800.0000
BogoMIPS: 4200.00
Virtualization: VT-x
L1d cache: 2.3 MiB
L1i cache: 1.5 MiB
L2 cache: 96 MiB
L3 cache: 260 MiB
NUMA node0 CPU(s): 0-47,96-143
NUMA node1 CPU(s): 48-95,144-191
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: 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
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 tsc_known_freq 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 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 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 avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.2.3
[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-cudnn-frontend==1.14.1
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-cufile-cu12==1.11.1.6
[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-cusparselt-cu12==0.6.2
[pip3] nvidia-ml-py==12.575.51
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pynvml==12.0.0
[pip3] pyzmq==27.0.2
[pip3] torch==2.6.0
[pip3] torch_memory_saver==0.0.8
[pip3] torchao==0.9.0
[pip3] torchaudio==2.6.0
[pip3] torchmetrics==1.8.2
[pip3] torchvision==0.21.0
[pip3] transformers==4.51.1
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.2.0
[pip3] zmq==0.0.0
[conda] flashinfer-python 0.2.3 pypi_0 pypi
[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-cudnn-frontend 1.14.1 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.2.1.3 pypi_0 pypi
[conda] nvidia-cufile-cu12 1.11.1.6 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-cusparselt-cu12 0.6.2 pypi_0 pypi
[conda] nvidia-ml-py 12.575.51 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] pynvml 12.0.0 pypi_0 pypi
[conda] pyzmq 27.0.2 pypi_0 pypi
[conda] torch 2.6.0 pypi_0 pypi
[conda] torch-memory-saver 0.0.8 pypi_0 pypi
[conda] torchao 0.9.0 pypi_0 pypi
[conda] torchaudio 2.6.0 pypi_0 pypi
[conda] torchmetrics 1.8.2 pypi_0 pypi
[conda] torchvision 0.21.0 pypi_0 pypi
[conda] transformers 4.51.1 pypi_0 pypi
[conda] transformers-stream-generator 0.0.5 pypi_0 pypi
[conda] triton 3.2.0 pypi_0 pypi
[conda] zmq 0.0.0 pypi_0 pypi
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.8.3
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 NIC8 NIC9 NIC10 NIC11 NIC12 NIC13 NIC14 NIC15 NIC16 NIC17 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE NODE NODE 0 0 N/A
GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 NODE PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE NODE 0 0 N/A
GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE 0 0 N/A
GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE 0 0 N/A
GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE N/A
GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE N/A
GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE N/A
GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE N/A
NIC0 PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE NODE NODE
NIC1 NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE NODE
NIC2 NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE
NIC3 NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE
NIC4 NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE
NIC5 NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE
NIC6 NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE
NIC7 NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE
NIC8 PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE NODE NODE NODE NODE NODE NODE
NIC9 NODE PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE NODE NODE NODE NODE NODE
NIC10 NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE NODE NODE NODE NODE
NIC11 NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE NODE NODE NODE
NIC12 NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE NODE NODE
NIC13 NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE NODE
NIC14 NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE NODE
NIC15 NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE X NODE NODE
NIC16 NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE X NODE
NIC17 NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE 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
NIC2: mlx5_2
NIC3: mlx5_3
NIC4: mlx5_4
NIC5: mlx5_5
NIC6: mlx5_6
NIC7: mlx5_7
NIC8: mlx5_20
NIC9: mlx5_21
NIC10: mlx5_22
NIC11: mlx5_23
NIC12: mlx5_24
NIC13: mlx5_25
NIC14: mlx5_26
NIC15: mlx5_27
NIC16: mlx5_bond_0
NIC17: mlx5_data_0
==============================
Environment Variables
==============================
CUDA_PATH=/usr/local/cuda
LD_LIBRARY_PATH=/mnt/shared-storage-user/zoutong/geruijun/cuda-12.4/lib64:/usr/local/nvidia/lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
CUDA_HOME=/mnt/shared-storage-user/zoutong/geruijun/cuda-12.4
CUDA_HOME=/mnt/shared-storage-user/zoutong/geruijun/cuda-12.4
VLLM_USE_V1=1
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root
CUDA_MODULE_LOADING=LAZY
When I run with vllm version 0.8.3 and export VLLM_USE_V1=1, I met the following problem:
in update_weight
[rank4]: self.model_runner.model.load_weights(weights=[(name, weight)])
[rank4]: File "/root/miniconda3/lib/python3.11/site-packages/flash_rl/vllm_patch.py", line 701, in hacked_load_weights
[rank4]: updated_params = original_load_weights(
[rank4]: ^^^^^^^^^^^^^^^^^^^^^^
[rank4]: File "/root/miniconda3/lib/python3.11/site-packages/vllm/model_executor/models/qwen2_5_vl.py", line 1115, in load_weights
[rank4]: return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank4]: File "/root/miniconda3/lib/python3.11/site-packages/vllm/model_executor/models/utils.py", line 261, in load_weights
[rank4]: autoloaded_weights = set(self._load_module("", self.module, weights))
[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank4]: File "/root/miniconda3/lib/python3.11/site-packages/vllm/model_executor/models/utils.py", line 222, in _load_module
[rank4]: yield from self._load_module(prefix,
[rank4]: File "/root/miniconda3/lib/python3.11/site-packages/vllm/model_executor/models/utils.py", line 195, in _load_module
[rank4]: loaded_params = module_load_weights(weights)
[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank4]: File "/root/miniconda3/lib/python3.11/site-packages/vllm/model_executor/models/qwen2_5_vl.py", line 720, in load_weights
[rank4]: weight_loader(param, loaded_weight)
[rank4]: File "/root/miniconda3/lib/python3.11/site-packages/vllm/model_executor/layers/linear.py", line 465, in weight_loader_v2
[rank4]: param.load_column_parallel_weight(loaded_weight=loaded_weight)
[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank4]: AttributeError: 'Parameter' object has no attribute 'load_column_parallel_weight'
I want to add fp8 rollout feature using flash_rl. The workerwrap code is as follows:
from vllm.v1.worker.gpu_worker import Worker as WorkerV1
class WorkerWrapV1(WorkerV1):
def update_weight(self, name, dtype, shape, weight, empty_cache=False): # pylint: disable=R0917, W0613
assert dtype == self.model_config.dtype, f"mismatch dtype: src {dtype}, dst {self.model_config.dtype}"
self.model_runner.model.load_weights(weights=[(name, weight)])
del weight
if empty_cache:
torch.cuda.empty_cache()
I found that this "AttributeError: 'Parameter' object has no attribute 'load_column_parallel_weight'" error happens when the type of blocks.0.attn.qkv.weight parameter is tensor. When the type of blocks.0.attn.qkv.weight parameter is ModelWeightParameter, it can be loaded successfully.
Why the type of this parameter can change? How can I fix this problem? Thank you for your help.
How would you like to use vllm
I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.
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