[Usage]: Problem when loading my trained model.

[hummingbird2030]

Your current environment

Collecting environment information...
PyTorch version: 2.1.2+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
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: 14.0.0-1ubuntu1.1
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-89-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla V100-SXM2-16GB
Nvidia driver version: 535.161.07
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:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             48
On-line CPU(s) list:                0-47
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz
CPU family:                         6
Model:                              85
Thread(s) per core:                 2
Core(s) per socket:                 12
Socket(s):                          2
Stepping:                           7
CPU max MHz:                        3500.0000
CPU min MHz:                        1000.0000
BogoMIPS:                           4800.00
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 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 cdp_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 mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          768 KiB (24 instances)
L1i cache:                          768 KiB (24 instances)
L2 cache:                           24 MiB (24 instances)
L3 cache:                           33 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-11,24-35
NUMA node1 CPU(s):                  12-23,36-47
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; Enhanced IBRS
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:      Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.1.2
[pip3] triton==2.1.0
[conda] No relevant packagesROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.2.6
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    NIC0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      SYS     0,24    0               N/A
NIC0    SYS      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

How would you like to use vllm

I want to run inference of a llama2 which is trained by myself. I use the following code to save the trained model: '''

                    if args.output_dir is not None:
                        output_dir = os.path.join(args.output_dir, output_dir)
                    accelerator.save_state(output_dir)

                    accelerator.wait_for_everyone()
                    if accelerator.is_main_process:
                        tokenizer.save_pretrained(args.output_dir)
                    unwrapped_model = accelerator.unwrap_model(model)
                    # When doing multi-gpu training, we need to use accelerator.get_state_dict(model) to get the state_dict.
                    # Otherwise, sometimes the model will be saved with only part of the parameters.
                    # Also, accelerator needs to use the wrapped model to get the state_dict.
                    state_dict = accelerator.get_state_dict(model)
                    if args.use_lora:
                        # When using lora, the unwrapped model is a PeftModel, which doesn't support the is_main_process 
                        # and has its own save_pretrained function for only saving lora modules.
                        # We have to mannually specify the is_main_process outside the save_pretrained function.
                        if accelerator.is_main_process:
                            unwrapped_model.save_pretrained(args.output_dir, state_dict=state_dict)
                    else:
                        unwrapped_model.save_pretrained(
                            args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save, state_dict=state_dict
                        )

'''

And then I get following trained model files: When I tried to load the model with model = LLM(model={my_model_dir}, download_dir={my_model_dir}, dtype=args.dtype, tensor_parallel_size=args.world_size,) I got the following AssertionError:

File "run_short_form.py", line 374, in main() File "run_short_form.py", line 302, in main model = LLM(model=gpt, download_dir=args.download_dir, File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/entrypoints/llm.py", line 105, in init self.llm_engine = LLMEngine.from_engine_args(engine_args) File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 250, in from_engine_args engine = cls(engine_configs, File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 110, in init self._init_workers(distributed_init_method) File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 146, in _init_workers self._run_workers( File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 755, in _run_workers self._run_workers_in_batch(workers, method, args, *kwargs)) File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 729, in _run_workers_in_batch output = executor(args, **kwargs) File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/worker/worker.py", line 79, in load_model self.model_runner.load_model() File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/worker/model_runner.py", line 57, in load_model self.model = get_model(self.model_config) File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/model_executor/model_loader.py", line 72, in get_model model.load_weights(model_config.model, model_config.download_dir, File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/model_executor/models/llama.py", line 340, in load_weights weight_loader(param, loaded_weight) File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/model_executor/layers/vocab_parallel_embedding.py", line 80, in weight_loader assert loaded_weight.shape[parallel_dim] == self.num_embeddings AssertionError

This is a problem when I use the code of https://github.com/AkariAsai/self-rag. But when I use the model trained by the author of self-rag, this problem didn't appear. Maybe there's something wrong with my method of saving the model. If you can provide me some help, it will help me a lot.

[zhaochen0110]

I also meet the problem. Is there any solution?

[dlgusals1414]

I also meet the problem. And I fixed the problem.

There is an empty tensor in "model.safetensors". I'm not sure why this weight is being saved, but please check it, delete the file, and try running it again. Below is the code I used to check the safetensor:

from safetensors import safe_open
path = "model.safetensors"

tensors = {}
with safe_open(path, framework="pt", device="cpu") as f:
    for key in f.keys():
       tensors[key] = f.get_tensor(key)
print(tensors)

[hummingbird2030]

I also meet the problem. Is there any solution?

I fixed it by deleting the 'model.safetensors'. Hope to help you.

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