search

vllm-project / vllm

A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
Apache License 2.0
30.38k stars 4.6k forks source link

[Bug]: KeyError: 'layers.60.mlp.gate_up_proj.weight' mistral large bitsandbytes #9376

Open copasseron opened 1 month ago

copasseron commented 1 month ago

Your current environment

The output of `python collect_env.py` ```text PyTorch version: 2.4.0+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: Ubuntu 24.04.1 LTS (x86_64) GCC version: (Ubuntu 13.2.0-23ubuntu4) 13.2.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.39 Python version: 3.10.15 | packaged by conda-forge | (main, Sep 30 2024, 17:51:04) [GCC 13.3.0] (64-bit runtime) Python platform: Linux-6.8.0-45-generic-x86_64-with-glibc2.39 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA H100 PCIe GPU 1: NVIDIA H100 PCIe Nvidia driver version: 535.183.06 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, 57 bits virtual Byte Order: Little Endian CPU(s): 192 On-line CPU(s) list: 0-191 Vendor ID: GenuineIntel Model name: INTEL(R) XEON(R) PLATINUM 8568Y+ CPU family: 6 Model: 207 Thread(s) per core: 2 Core(s) per socket: 48 Socket(s): 2 Stepping: 2 CPU(s) scaling MHz: 20% CPU max MHz: 4000.0000 CPU min MHz: 800.0000 BogoMIPS: 4600.00 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 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 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow 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 user_shstk avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req hfi vnmi 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 ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 4.5 MiB (96 instances) L1i cache: 3 MiB (96 instances) L2 cache: 192 MiB (96 instances) L3 cache: 600 MiB (2 instances) NUMA node(s): 2 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 Reg file data sampling: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Not affected 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; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] nvidia-cublas-cu12==12.1.3.1 [pip3] nvidia-cuda-cupti-cu12==12.1.105 [pip3] nvidia-cuda-nvrtc-cu12==12.1.105 [pip3] nvidia-cuda-runtime-cu12==12.1.105 [pip3] nvidia-cudnn-cu12==9.1.0.70 [pip3] nvidia-cufft-cu12==11.0.2.54 [pip3] nvidia-curand-cu12==10.3.2.106 [pip3] nvidia-cusolver-cu12==11.4.5.107 [pip3] nvidia-cusparse-cu12==12.1.0.106 [pip3] nvidia-ml-py==12.560.30 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] nvidia-nvjitlink-cu12==12.6.77 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] pyzmq==26.2.0 [pip3] torch==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.45.2 [pip3] triton==3.0.0 [conda] numpy 1.26.4 pypi_0 pypi [conda] nvidia-cublas-cu12 12.1.3.1 pypi_0 pypi [conda] nvidia-cuda-cupti-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cuda-nvrtc-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cuda-runtime-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi [conda] nvidia-cufft-cu12 11.0.2.54 pypi_0 pypi [conda] nvidia-curand-cu12 10.3.2.106 pypi_0 pypi [conda] nvidia-cusolver-cu12 11.4.5.107 pypi_0 pypi [conda] nvidia-cusparse-cu12 12.1.0.106 pypi_0 pypi [conda] nvidia-ml-py 12.560.30 pypi_0 pypi [conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi [conda] nvidia-nvjitlink-cu12 12.6.77 pypi_0 pypi [conda] nvidia-nvtx-cu12 12.1.105 pypi_0 pypi [conda] pyzmq 26.2.0 pypi_0 pypi [conda] torch 2.4.0 pypi_0 pypi [conda] torchvision 0.19.0 pypi_0 pypi [conda] transformers 4.45.2 pypi_0 pypi [conda] triton 3.0.0 pypi_0 pypi ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: N/A (dev) vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X SYS 0-47,96-143 0 N/A GPU1 SYS X 48-95,144-191 1 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 ```

🐛 Describe the bug

while trying to deploy mistral-large with bitsandbytes in-flight quantization on 2 H100 80GB, I came across this error.

[rank0]: KeyError: 'layers.60.mlp.gate_up_proj.weight'

minimal code to reproduce it: 

from vllm import LLM, SamplingParams
import torch
model_id = "mistralai/Mistral-Large-Instruct-2407"
llm = LLM(model=model_id, dtype=torch.bfloat16, trust_remote_code=True, \
quantization="bitsandbytes", load_format="bitsandbytes", tensor_parallel_size = 2)
full log trace. ``` WARNING 10-15 14:08:21 config.py:306] bitsandbytes quantization is not fully optimized yet. The speed can be slower than non-quantized models. WARNING 10-15 14:08:21 arg_utils.py:953] Chunked prefill is enabled by default for models with max_model_len > 32K. Currently, chunked prefill might not work with some features or models. If you encounter any issues, please disable chunked prefill by setting --enable-chunked-prefill=False. INFO 10-15 14:08:21 config.py:1005] Chunked prefill is enabled with max_num_batched_tokens=512. INFO 10-15 14:08:21 llm_engine.py:237] Initializing an LLM engine (vdev) with config: model='mistralai/Mistral-Large-Instruct-2407', speculative_config=None, tokenizer='mistralai/Mistral-Large-Instruct-2407', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=LoadFormat.BITSANDBYTES, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=bitsandbytes, 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=mistralai/Mistral-Large-Instruct-2407, use_v2_block_manager=True, num_scheduler_steps=1, chunked_prefill_enabled=True multi_step_stream_outputs=True, enable_prefix_caching=False, use_async_output_proc=True, use_cached_outputs=False, mm_processor_kwargs=None) /home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/transformers_utils/tokenizer_group/tokenizer_group.py:23: FutureWarning: It is strongly recommended to run mistral models with `--tokenizer_mode "mistral"` to ensure correct encoding and decoding. self.tokenizer = get_tokenizer(self.tokenizer_id, **tokenizer_config) INFO 10-15 14:08:35 model_runner.py:1060] Starting to load model mistralai/Mistral-Large-Instruct-2407... INFO 10-15 14:08:35 loader.py:1051] Loading weights with BitsAndBytes quantization. May take a while ... INFO 10-15 14:08:36 weight_utils.py:243] Using model weights format ['*.safetensors'] Loading safetensors checkpoint shards: 0% Completed | 0/102 [00:00 [rank0]: llm = LLM(model=model_id, dtype=torch.bfloat16, trust_remote_code=True, \ [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/entrypoints/llm.py", line 177, in __init__ [rank0]: self.llm_engine = LLMEngine.from_engine_args( [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 574, in from_engine_args [rank0]: engine = cls( [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 335, in __init__ [rank0]: self.model_executor = executor_class( [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 47, in __init__ [rank0]: self._init_executor() [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/executor/gpu_executor.py", line 40, in _init_executor [rank0]: self.driver_worker.load_model() [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/worker/worker.py", line 183, in load_model [rank0]: self.model_runner.load_model() [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 1062, in load_model [rank0]: self.model = get_model(model_config=self.model_config, [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/model_executor/model_loader/__init__.py", line 19, in get_model [rank0]: return loader.load_model(model_config=model_config, [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/model_executor/model_loader/loader.py", line 1148, in load_model [rank0]: self._load_weights(model_config, model) [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/model_executor/model_loader/loader.py", line 1082, in _load_weights [rank0]: model.load_weights(qweight_iterator) [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/model_executor/models/llama.py", line 580, in load_weights [rank0]: loader.load_weights( [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/model_executor/models/utils.py", line 192, in load_weights [rank0]: self._load_module("", self.module, weights) [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/model_executor/models/utils.py", line 173, in _load_module [rank0]: self._load_module(prefix, child_modules[child_prefix], [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/model_executor/models/utils.py", line 160, in _load_module [rank0]: module_load_weights(weights) [rank0]: File "/home/cpassero/miniconda/envs/test/lib/python3.10/site-packages/vllm/model_executor/models/llama.py", line 395, in load_weights [rank0]: param = params_dict[name] [rank0]: KeyError: 'layers.60.mlp.gate_up_proj.weight' Loading safetensors checkpoint shards: 3% Completed | 3/102 [00:02<01:19, 1.25it/s] ```

Other persons mentioned it: posted by @figuernd in https://github.com/vllm-project/vllm/issues/4198#issuecomment-2412149718_

this is related to the packed module here: https://github.com/vllm-project/vllm/blob/v0.6.2/vllm/model_executor/models/llama.py#L354

we have gate and up layers referenced in the safetensor index, but somewhat the packing does not happen. https://huggingface.co/mistralai/Mistral-Large-Instruct-2407/blob/main/model.safetensors.index.json#L523

might be linked to these issues: https://github.com/vllm-project/vllm/issues/4198 https://github.com/vllm-project/vllm/issues/9316

Before submitting a new issue...

DarkLight1337 commented 1 month ago

@mgoin can you help look into this? Thanks!

mgoin commented 1 month ago

I am able to more easily reproduce this with mistral-7b

vllm serve mistralai/Mistral-7B-Instruct-v0.3 --quantization bitsandbytes --load-format bitsandbytes

The issue seems to be that we are confused by the presense of both HF-format and Mistral-format weights within the model repo. All of the weights in the HF-format safetensors get loaded and quantized correctly, however we then try to load in the Mistral-format "consolidated" weights they are not quantized and we fail to resolve the param name.

name before maybe_remap_mistral: model.layers.0.self_attn.k_proj.qweight
name after maybe_remap_mistral: model.layers.0.self_attn.k_proj.qweight
param_name: .qkv_proj
weight_name: .k_proj
shard_id: k
final name: model.layers.0.self_attn.qkv_proj.qweight
...
name before maybe_remap_mistral: layers.0.attention.wk.weight
name after maybe_remap_mistral: model.layers.0.self_attn.k_proj.weight
param_name: .qkv_proj
weight_name: .k_proj
shard_id: k
final name: model.layers.0.self_attn.qkv_proj.weight
# KeyError: 'model.layers.0.self_attn.qkv_proj.weight'

Notice that there is no problem in the first name mapping because it is a proper .qweight param, however the second time around with the mistral format name it is just a .weight param that we don't have in our params_dict

copasseron commented 1 month ago

@mgoin thank you for having looked at it. What would be the way to solve this issue ?

Do you think about a workaround or another way to load mistral large with vLLM on 2 * 80GB H100, without using an already quantized model by someone else than mistralAI ?