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A high-throughput and memory-efficient inference and serving engine for LLMs
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[Bug]: Cannot load fp8 model of internlm2-chat-7b offline #6558

Open EstellaXinyuZhang opened 1 month ago

EstellaXinyuZhang commented 1 month ago

Your current environment

PyTorch version: 2.3.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 11 (bullseye) (x86_64)
GCC version: (Debian 10.2.1-6) 10.2.1 20210110
Clang version: Could not collect
CMake version: version 3.30.0
Libc version: glibc-2.31

Python version: 3.9.2 (default, Feb 28 2021, 17:03:44)  [GCC 10.2.1 20210110] (64-bit runtime)
Python platform: Linux-5.4.143.bsk.8-amd64-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: A100-SXM-80GB
Nvidia driver version: 450.191.01
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
Byte Order:                      Little Endian
Address sizes:                   46 bits physical, 57 bits virtual
CPU(s):                          128
On-line CPU(s) list:             0-127
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       2
NUMA node(s):                    4
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           106
Model name:                      Intel(R) Xeon(R) Platinum 8336C CPU @ 2.30GHz
Stepping:                        6
CPU MHz:                         2999.998
CPU max MHz:                     3500.0000
CPU min MHz:                     800.0000
BogoMIPS:                        4600.00
Virtualization:                  VT-x
L1d cache:                       3 MiB
L1i cache:                       2 MiB
L2 cache:                        80 MiB
L3 cache:                        108 MiB
NUMA node0 CPU(s):               0-15,64-79
NUMA node1 CPU(s):               16-31,80-95
NUMA node2 CPU(s):               32-47,96-111
NUMA node3 CPU(s):               48-63,112-127
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          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
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 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 invpcid_single 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 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 rdpid md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] torch==2.3.0
[pip3] torchvision==0.18.0
[pip3] transformers==4.42.4
[pip3] triton==2.3.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    mlx5_0  mlx5_1  mlx5_2  mlx5_3  CPU Affinity    NUMA Affinity
GPU0     X      SYS     SYS     SYS     PXB     48-63,112-127   3
mlx5_0  SYS      X      NODE    SYS     SYS
mlx5_1  SYS     NODE     X      SYS     SYS
mlx5_2  SYS     SYS     SYS      X      SYS
mlx5_3  PXB     SYS     SYS     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

🐛 Describe the bug

I use auto_fp8 to quantize the model internlm/internlm2-chat-7b.

from transformers import AutoTokenizer
from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig

pretrained_model_dir = "internlm/internlm2-chat-7b"
quantized_model_dir = "model/internlm2-chat-7b_hf_fp8"

# Define quantization config with static activation scales

tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)
examples = ["auto_fp8 is an easy-to-use model quantization library"]
examples = tokenizer(examples, return_tensors="pt").to("cuda")

quantize_config = BaseQuantizeConfig(quant_method="fp8", activation_scheme="dynamic")

# Load the model, quantize, and save checkpoint
model = AutoFP8ForCausalLM.from_pretrained(pretrained_model_dir, quantize_config, trust_remote_code=True)
model.quantize(examples)
model.save_quantized(quantized_model_dir)

Then I would like to load the fp8 model, but got error.

from vllm import LLM, SamplingParams

prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm_fp8 = LLM(model="model/internlm2-chat-7b_hf_fp8/", trust_remote_code=True)
outputs = llm_fp8.generate(prompts, 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}")

error is 

INFO 07-19 10:03:53 llm_engine.py:169] Initializing an LLM engine (v0.5.1) with config: model='model/internlm2-chat-7b_hf_fp8/', speculative_config=None, tokenizer='model/internlm2-chat-7b_hf_fp8/', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=65536, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=fp8, 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), seed=0, served_model_name=model/internlm2-chat-7b_hf_fp8/, use_v2_block_manager=False, enable_prefix_caching=False)
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
WARNING 07-19 10:03:55 fp8.py:45] Detected fp8 checkpoint. Please note that the format is experimental and subject to change.
[rank0]: Traceback (most recent call last):
[rank0]:   File "/root/code/vllm_demo/fp8_test/main.py", line 14, in <module>
[rank0]:     llm_fp8 = LLM(model="model/internlm2-chat-7b_hf_fp8/", trust_remote_code=True)
[rank0]:   File "/root/vene_list/fp8Venv/lib/python3.9/site-packages/vllm/entrypoints/llm.py", line 149, in __init__
[rank0]:     self.llm_engine = LLMEngine.from_engine_args(
[rank0]:   File "/root/vene_list/fp8Venv/lib/python3.9/site-packages/vllm/engine/llm_engine.py", line 414, in from_engine_args
[rank0]:     engine = cls(
[rank0]:   File "/root/vene_list/fp8Venv/lib/python3.9/site-packages/vllm/engine/llm_engine.py", line 243, in __init__
[rank0]:     self.model_executor = executor_class(
[rank0]:   File "/root/vene_list/fp8Venv/lib/python3.9/site-packages/vllm/executor/executor_base.py", line 42, in __init__
[rank0]:     self._init_executor()
[rank0]:   File "/root/vene_list/fp8Venv/lib/python3.9/site-packages/vllm/executor/gpu_executor.py", line 24, in _init_executor
[rank0]:     self.driver_worker.load_model()
[rank0]:   File "/root/vene_list/fp8Venv/lib/python3.9/site-packages/vllm/worker/worker.py", line 133, in load_model
[rank0]:     self.model_runner.load_model()
[rank0]:   File "/root/vene_list/fp8Venv/lib/python3.9/site-packages/vllm/worker/model_runner.py", line 243, in load_model
[rank0]:     self.model = get_model(
[rank0]:   File "/root/vene_list/fp8Venv/lib/python3.9/site-packages/vllm/model_executor/model_loader/__init__.py", line 21, in get_model
[rank0]:     return loader.load_model(model_config=model_config,
[rank0]:   File "/root/vene_list/fp8Venv/lib/python3.9/site-packages/vllm/model_executor/model_loader/loader.py", line 270, in load_model
[rank0]:     model.load_weights(
[rank0]:   File "/root/vene_list/fp8Venv/lib/python3.9/site-packages/vllm/model_executor/models/internlm2.py", line 321, in load_weights
[rank0]:     loaded_weight.shape[-1])
[rank0]: IndexError: tuple index out of range
artetaout commented 1 month ago

does "GPU 0: A100-SXM-80GB" support FP8 ?

EstellaXinyuZhang commented 1 month ago

does "GPU 0: A100-SXM-80GB" support FP8 ?

yes, I used auto-fp8 to quantize models such as internlm-chat-7b and facebook/opt-125m and loaded the quantized models. They can work.

robertgshaw2-neuralmagic commented 1 month ago

Can you post the model to the hf hub so I can take a look?