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A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
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[Bug]: Qwen2 Moe FP8 not supported on L40 #6264

Open TopIdiot opened 3 months ago

TopIdiot commented 3 months ago

Your current environment

Collecting environment information...
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: Ubuntu 22.04.3 LTS (x86_64)
GCC version: Could not collect
Clang version: Could not collect
CMake version: version 3.30.0
Libc version: glibc-2.35

Python version: 3.11.9 (main, Apr 19 2024, 16:48:06) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.241-1-tlinux4-0017.6-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: NVIDIA L40
GPU 1: NVIDIA L40

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:                   52 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          384
On-line CPU(s) list:             0-383
Vendor ID:                       AuthenticAMD
Model name:                      AMD EPYC 9K84 96-Core Processor
CPU family:                      25
Model:                           17
Thread(s) per core:              2
Core(s) per socket:              96
Socket(s):                       2
Stepping:                        0
BogoMIPS:                        5200.06
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
 rep_good nopl cpuid extd_apicid amd_dcm tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hyperviso
r lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single ibpb vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx
512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 avx512_bf16 clzero xsaveerptr wbnoinvd arat avx512vbmi umip avx
512_vbmi2 vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       6 MiB (192 instances)
L1i cache:                       6 MiB (192 instances)
L2 cache:                        192 MiB (192 instances)
L3 cache:                        768 MiB (24 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-191
NUMA node1 CPU(s):               192-383
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 store bypass: Vulnerable
Vulnerability Spectre v1:        Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:        Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

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==4.42.3
[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    GPU1    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NODE    0-191   0               N/A
GPU1    NODE     X      0-191   0               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

After loading a fp8 qwen2 moe model

 python -m vllm.entrypoints.openai.api_server   --model ./moe   --port 8081   --host 0.
0.0.0   --trust-remote-code   --tensor-parallel-size 2
INFO 07-09 12:19:33 api_server.py:206] vLLM API server version 0.5.1
INFO 07-09 12:19:33 api_server.py:207] args: Namespace(host='0.0.0.0', port=8081, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'],
 allowed_headers=['*'], api_key=None, lora_modules=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, r
oot_path=None, middleware=[], model='./moe', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_r
emote_code=True, download_dir=None, load_format='auto', dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=None, guided_decoding_backend='outlines',
 distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=2, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block
_size=16, enable_prefix_caching=False, disable_sliding_window=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, gpu_memory_utilization=0.9, num_gpu_
blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eage
r=False, max_context_len_to_capture=None, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_con
fig=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=
False, device='auto', scheduler_delay_factor=0.0, enable_chunked_prefill=False, speculative_model=None, num_speculative_tokens=None, speculative_draft_tensor_parallel_size=None,
speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sam
pler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, model_loader_extra_config=None, preemption_mode=None, served_model_na
me=None, qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, engine_use_ray=False, disable_log_requests=False, max_log_len=None)
INFO 07-09 12:19:33 config.py:698] Defaulting to use mp for distributed inference
INFO 07-09 12:19:33 llm_engine.py:169] Initializing an LLM engine (v0.5.1) with config: model='./moe', speculative_config=None, tokenizer='./moe', skip_tokenizer_init=False, toke
nizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.float16, max_seq_len=16384, download_dir=None, lo
ad_format=LoadFormat.AUTO, tensor_parallel_size=2, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=fp8, enforce_eager=False, kv_cache_dtype=auto, quantiza
tion_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=./moe, use_v2_block_manager=False, enable_prefix_caching=False)
WARNING 07-09 12:19:34 tokenizer.py:126] Using a slow tokenizer. This might cause a significant slowdown. Consider using a fast tokenizer instead.
(VllmWorkerProcess pid=22996) INFO 07-09 12:19:34 multiproc_worker_utils.py:215] Worker ready; awaiting tasks
(VllmWorkerProcess pid=22996) INFO 07-09 12:19:35 utils.py:741] Found nccl from library libnccl.so.2
INFO 07-09 12:19:35 utils.py:741] Found nccl from library libnccl.so.2
INFO 07-09 12:19:35 pynccl.py:63] vLLM is using nccl==2.20.5
(VllmWorkerProcess pid=22996) INFO 07-09 12:19:35 pynccl.py:63] vLLM is using nccl==2.20.5
INFO 07-09 12:19:35 custom_all_reduce_utils.py:232] reading GPU P2P access cache from /root/.config/vllm/gpu_p2p_access_cache_for_0,1.json
(VllmWorkerProcess pid=22996) INFO 07-09 12:19:35 custom_all_reduce_utils.py:232] reading GPU P2P access cache from /root/.config/vllm/gpu_p2p_access_cache_for_0,1.json
WARNING 07-09 12:19:35 fp8.py:45] Detected fp8 checkpoint. Please note that the format is experimental and subject to change.
(VllmWorkerProcess pid=22996) WARNING 07-09 12:19:35 fp8.py:45] Detected fp8 checkpoint. Please note that the format is experimental and subject to change.
(VllmWorkerProcess pid=22996) INFO 07-09 12:21:45 model_runner.py:255] Loading model weights took 8.0809 GB
INFO 07-09 12:21:46 model_runner.py:255] Loading model weights took 8.0809 GB
Conversion from/to f8e4m3nv is only supported on compute capability >= 90

UNREACHABLE executed at /project/lib/Conversion/TritonGPUToLLVM/ElementwiseOpToLLVM.cpp:823!
Conversion from/to f8e4m3nv is only supported on compute capability >= 90

UNREACHABLE executed at /project/lib/Conversion/TritonGPUToLLVM/ElementwiseOpToLLVM.cpp:823!
/home/qspace/data/mmsprwelmmodelsvra/rce2004389934/vllm/lib/python3.11/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 2 leaked shared_
memory objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '
Aborted (core dumped)

The config.json is 

{
  "_name_or_path": "/data/home/yowenchen/vllm/model_qwen_moe",
  "architectures": [
    "Qwen2MoeForCausalLM"
  ],
  "attention_dropout": 0,
  "decoder_sparse_step": 1,
  "eos_token_id": 2,
  "hidden_act": "silu",
  "hidden_size": 2048,
  "initializer_range": 0.02,
  "intermediate_size": 1408,
  "max_position_embeddings": 16384,
  "max_window_layers": 28,
  "mlp_only_layers": [],
  "model_type": "qwen2_moe",
  "moe_intermediate_size": 1408,
  "norm_topk_prob": true,
  "num_attention_heads": 16,
  "num_experts": 64,
  "num_experts_per_tok": 6,
  "num_hidden_layers": 28,
  "num_key_value_heads": 16,
  "output_router_logits": false,
  "quantization_config": {
    "activation_scheme": "dynamic",
    "quant_method": "fp8"
  },
  "rms_norm_eps": 1e-05,
  "rope_theta": 10000.0,
  "router_aux_loss_coef": 0.001,
  "shared_expert_intermediate_size": 2816,
  "sliding_window": 4096,
  "tie_word_embeddings": false,
  "torch_dtype": "float16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "use_sliding_window": false,
  "vocab_size": 102400
}
robertgshaw2-neuralmagic commented 3 months ago

fp8 not yet supported for Qwen. WIP PR: https://github.com/vllm-project/vllm/pull/6088

TopIdiot commented 2 months ago

fp8 not yet supported for Qwen. WIP PR: #6088

@robertgshaw2-neuralmagic Hello, the error still exists in version 0.5.3 .

robertgshaw2-neuralmagic commented 2 months ago

Fp8 is now supported for Qwen, but MoE Fp8 requires compute_capability == 9.0 (aka Hopper GPUs)

Our MoE kernels are currently implemented using Triton, which require triton==3.0 for Fp8 on Ada Lovelace. We are limited by PyTorch's version of triton

We look forward to supporting Fp8 MoE on Ada Lovelace once these dependencies are enabled