search

vllm-project / vllm

A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
Apache License 2.0
27.65k stars 4.08k forks source link

[Bug]: Dynamic FP8 Marlin quantization fails on `0.5.4` #7216

Closed mgoin closed 1 month ago

mgoin commented 1 month ago

Your current environment

``` 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 22.04.4 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.30.2 Libc version: glibc-2.35 Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-5.15.0-107-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.5.82 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A100-SXM4-80GB GPU 1: NVIDIA A100-SXM4-80GB GPU 2: NVIDIA A100-SXM4-80GB GPU 3: NVIDIA A100-SXM4-80GB GPU 4: NVIDIA A100-SXM4-80GB GPU 5: NVIDIA A100-SXM4-80GB GPU 6: NVIDIA A100-SXM4-80GB GPU 7: NVIDIA A100-SXM4-80GB Nvidia driver version: 550.54.15 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): 128 On-line CPU(s) list: 0-127 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Platinum 8462Y+ CPU family: 6 Model: 143 Thread(s) per core: 2 Core(s) per socket: 32 Socket(s): 2 Stepping: 8 BogoMIPS: 5600.00 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 invpcid_single 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 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 Virtualization: VT-x L1d cache: 3 MiB (64 instances) L1i cache: 2 MiB (64 instances) L2 cache: 128 MiB (64 instances) L3 cache: 120 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,116,118,120,122,124,126 NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103,105,107,109,111,113,115,117,119,121,123,125,127 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; 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-nccl-cu12==2.20.5 [pip3] torch==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.44.0 [pip3] triton==3.0.0 [conda] Could not collect ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.5.4 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NV12 NV12 NV12 NV12 NV12 NV12 NV12 PXB SYS SYS SYS SYS SYS SYS SYS 0,2,4,6,8,10 0 N/A GPU1 NV12 X NV12 NV12 NV12 NV12 NV12 NV12 SYS PXB SYS SYS SYS SYS SYS SYS 0,2,4,6,8,10 0 N/A GPU2 NV12 NV12 X NV12 NV12 NV12 NV12 NV12 SYS SYS PXB SYS SYS SYS SYS SYS 0,2,4,6,8,10 0 N/A GPU3 NV12 NV12 NV12 X NV12 NV12 NV12 NV12 SYS SYS SYS PXB SYS SYS SYS SYS 0,2,4,6,8,10 0 N/A GPU4 NV12 NV12 NV12 NV12 X NV12 NV12 NV12 SYS SYS SYS SYS PXB SYS SYS SYS 1,3,5,7,9,11 1 N/A GPU5 NV12 NV12 NV12 NV12 NV12 X NV12 NV12 SYS SYS SYS SYS SYS PXB SYS SYS 1,3,5,7,9,11 1 N/A GPU6 NV12 NV12 NV12 NV12 NV12 NV12 X NV12 SYS SYS SYS SYS SYS SYS PXB SYS 1,3,5,7,9,11 1 N/A GPU7 NV12 NV12 NV12 NV12 NV12 NV12 NV12 X SYS SYS SYS SYS SYS SYS SYS PXB 1,3,5,7,9,11 1 N/A NIC0 PXB SYS SYS SYS SYS SYS SYS SYS X SYS SYS SYS SYS SYS SYS SYS NIC1 SYS PXB SYS SYS SYS SYS SYS SYS SYS X SYS SYS SYS SYS SYS SYS NIC2 SYS SYS PXB SYS SYS SYS SYS SYS SYS SYS X SYS SYS SYS SYS SYS NIC3 SYS SYS SYS PXB SYS SYS SYS SYS SYS SYS SYS X SYS SYS SYS SYS NIC4 SYS SYS SYS SYS PXB SYS SYS SYS SYS SYS SYS SYS X SYS SYS SYS NIC5 SYS SYS SYS SYS SYS PXB SYS SYS SYS SYS SYS SYS SYS X SYS SYS NIC6 SYS SYS SYS SYS SYS SYS PXB SYS SYS SYS SYS SYS SYS SYS X SYS NIC7 SYS SYS SYS SYS SYS SYS SYS PXB SYS SYS SYS SYS 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 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 ```

🐛 Describe the bug

Dynamic FP8 works fine on H100 but fails on A100. This is an issue with the dynamic FP8 Marlin backend.

vllm serve meta-llama/Meta-Llama-3-8B-Instruct --quantization="fp8" --port 9000 
...
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/model_executor/layers/quantization/utils/marlin_utils.py", line 172, in marlin_permute_scales
    s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single]
RuntimeError: shape '[-1, 32]' is invalid for input of size 1

It does work fine with models that are already quantized to FP8 on A100:

vllm serve neuralmagic/Meta-Llama-3-8B-Instruct-FP8 --quantization="fp8" --port 9000
...
INFO:     Uvicorn running on http://0.0.0.0:9000 (Press CTRL+C to quit)

Full command and output/stacktrace:

vllm serve meta-llama/Meta-Llama-3-8B-Instruct --quantization="fp8" --port 9000     
INFO 08-06 19:27:35 api_server.py:339] vLLM API server version 0.5.4
INFO 08-06 19:27:35 api_server.py:340] args: Namespace(model_tag='meta-llama/Meta-Llama-3-8B-Instruct', host=None, port=9000, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, model='meta-llama/Meta-Llama-3-8B-Instruct', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, 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=1, 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, cpu_offload_gb=0, 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='fp8', rope_scaling=None, rope_theta=None, enforce_eager=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_config=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, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', scheduler_delay_factor=0.0, enable_chunked_prefill=None, 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_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, engine_use_ray=False, disable_log_requests=False, max_log_len=None, dispatch_function=<function serve at 0x7f5440e617e0>)
WARNING 08-06 19:27:35 config.py:1454] Casting torch.bfloat16 to torch.float16.
INFO 08-06 19:27:35 llm_engine.py:174] Initializing an LLM engine (v0.5.4) with config: model='meta-llama/Meta-Llama-3-8B-Instruct', speculative_config=None, tokenizer='meta-llama/Meta-Llama-3-8B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=8192, 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=meta-llama/Meta-Llama-3-8B-Instruct, use_v2_block_manager=False, enable_prefix_caching=False)
INFO 08-06 19:27:38 model_runner.py:720] Starting to load model meta-llama/Meta-Llama-3-8B-Instruct...
INFO 08-06 19:27:39 weight_utils.py:225] Using model weights format ['*.safetensors']
Loading safetensors checkpoint shards:   0% Completed | 0/4 [00:00<?, ?it/s]
Loading safetensors checkpoint shards:  25% Completed | 1/4 [00:00<00:00,  7.55it/s]
Loading safetensors checkpoint shards:  50% Completed | 2/4 [00:00<00:00,  2.52it/s]
Loading safetensors checkpoint shards:  75% Completed | 3/4 [00:01<00:00,  1.97it/s]
Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:01<00:00,  1.81it/s]
Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:01<00:00,  2.02it/s]

WARNING 08-06 19:27:41 utils.py:578] Your GPU does not have native support for FP8 computation but FP8 quantization is being used. Weight-only FP8 compression will be used leveraging the Marlin kernel. This may degrade performance for compute-heavy workloads.
Process Process-1:
Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/entrypoints/openai/rpc/server.py", line 217, in run_rpc_server
    server = AsyncEngineRPCServer(async_engine_args, usage_context, port)
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/entrypoints/openai/rpc/server.py", line 25, in __init__
    self.engine = AsyncLLMEngine.from_engine_args(async_engine_args,
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 471, in from_engine_args
    engine = cls(
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 381, in __init__
    self.engine = self._init_engine(*args, **kwargs)
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 552, in _init_engine
    return engine_class(*args, **kwargs)
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 249, in __init__
    self.model_executor = executor_class(
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 47, in __init__
    self._init_executor()
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/executor/gpu_executor.py", line 36, in _init_executor
    self.driver_worker.load_model()
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/worker/worker.py", line 139, in load_model
    self.model_runner.load_model()
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 722, in load_model
    self.model = get_model(model_config=self.model_config,
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/model_executor/model_loader/__init__.py", line 21, in get_model
    return loader.load_model(model_config=model_config,
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/model_executor/model_loader/loader.py", line 344, in load_model
    quant_method.process_weights_after_loading(module)
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/model_executor/layers/quantization/fp8.py", line 212, in process_weights_after_loading
    prepare_fp8_layer_for_marlin(layer)
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/model_executor/layers/quantization/utils/marlin_utils_fp8.py", line 80, in prepare_fp8_layer_for_marlin
    marlin_scales = marlin_permute_scales(s=scales,
  File "/home/mgoin/venvs/vllm-rel/lib/python3.10/site-packages/vllm/model_executor/layers/quantization/utils/marlin_utils.py", line 172, in marlin_permute_scales
    s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single]
RuntimeError: shape '[-1, 32]' is invalid for input of size 1
garycaokai commented 1 month ago

same problem, 0.5.4 works on L20 but fails on A30.

fozziethebeat commented 1 month ago

Oh! I was just about to file this, I was running with docker image vllm/vllm-openai:v0.5.4 and hit the same problems.