[Bug]: FP8 W8A8 LLM Compressor Models fail to load in vLLM

[selalipop]

Your current environment

The output of `python collect_env.py`

PyTorch version: 2.6.0.dev20241122+rocm6.2 Is debug build: False CUDA used to build PyTorch: N/A ROCM used to build PyTorch: 6.2.41133-dd7f95766

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: 18.0.0git (https://github.com/RadeonOpenCompute/llvm-project roc-6.2.0 24292 26466ce804ac523b398608f17388eb6d605a3f09)
CMake version: version 3.26.4
Libc version: glibc-2.31

Python version: 3.9.19 (main, May 6 2024, 19:43:03) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-28-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: AMD Instinct MI300X (gfx942:sramecc+:xnack-)
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: 6.2.41133
MIOpen runtime version: 3.2.0
Is XNNPACK available: True

CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
Address sizes: 52 bits physical, 57 bits virtual
CPU(s): 192
On-line CPU(s) list: 0-191
Thread(s) per core: 2
Core(s) per socket: 48
Socket(s): 2
NUMA node(s): 2
Vendor ID: AuthenticAMD
CPU family: 25
Model: 17
Model name: AMD EPYC 9474F 48-Core Processor
Stepping: 1
Frequency boost: enabled
CPU MHz: 1500.000
CPU max MHz: 4113.2808
CPU min MHz: 1500.0000
BogoMIPS: 7189.53
Virtualization: AMD-V
L1d cache: 3 MiB
L1i cache: 3 MiB
L2 cache: 96 MiB
L3 cache: 512 MiB
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 Retbleed: Not affected
Vulnerability Spec rstack overflow: Mitigation; Safe RET
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, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
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 mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d

Versions of relevant libraries:
[pip3] mypy==1.8.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] optree==0.9.1
[pip3] pynvml==11.5.3
[pip3] pytorch-triton-rocm==3.1.0+cf34004b8a
[pip3] pyzmq==26.2.0
[pip3] torch==2.6.0.dev20241122+rocm6.2
[pip3] torchvision==0.20.0.dev20241206+rocm6.2
[pip3] transformers==4.46.0
[pip3] triton==3.0.0
[conda] No relevant packages
ROCM Version: 6.2.41133-dd7f95766
Neuron SDK Version: N/A
vLLM Version: 0.6.6.dev82+g720b10fd
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
============================ ROCm System Management Interface ============================
================================ Weight between two GPUs =================================
GPU0
GPU0 0

================================= Hops between two GPUs ==================================
GPU0
GPU0 0

=============================== Link Type between two GPUs ===============================
GPU0
GPU0 0

======================================= Numa Nodes =======================================
GPU[0] : (Topology) Numa Node: 0
GPU[0] : (Topology) Numa Affinity: 0
================================== End of ROCm SMI Log ===================================

PYTORCH_TUNABLEOP_TUNING=0
PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda
PYTORCH_TUNABLEOP_ENABLED=0
PYTORCH_TEST_WITH_ROCM=1
PYTORCH_ROCM_ARCH=gfx90a;gfx942
MAX_JOBS=32
LD_LIBRARY_PATH=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/cv2/../../lib64:/opt/ompi/lib:/opt/rocm/lib:/usr/local/lib::/opt/rocm/lib/:/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib:
VLLM_USE_TRITON_FLASH_ATTN=0
PYTORCH_TUNABLEOP_FILENAME=/app/tuned_gemm_csv/afo_tune_device_%d_full.csv
VLLM_WORKER_MULTIPROC_METHOD=spawn
CUDA_MODULE_LOADING=LAZY

Model Input Dumps

No response

🐛 Describe the bug

Running vllm serve mistralai/mistral-123B-instruct --host 0.0.0.0 --port 8000 --enable-chunked-prefill False --max-seq-len-to-capture 16384 --num-scheduler-steps 10 fails with a raised exception after the model is loaded

Model llm-compressor recipe:

DEFAULT_stage:
  DEFAULT_modifiers:
    QuantizationModifier:
      ignore: [lm_head]
      targets: [Linear]
      scheme: FP8_DYNAMIC

Traceback:

      ERROR 12-27 00:09:08 engine.py:366] 'QKVParallelLinear' object has no attribute 'input_scale'
      ERROR 12-27 00:09:08 engine.py:366] Traceback (most recent call last):
      ERROR 12-27 00:09:08 engine.py:366]   File "/workspace/vllm/vllm/engine/multiprocessing/engine.py", line 357, in run_mp_engine
      ERROR 12-27 00:09:08 engine.py:366]     engine = MQLLMEngine.from_engine_args(engine_args=engine_args,
      ERROR 12-27 00:09:08 engine.py:366]   File "/workspace/vllm/vllm/engine/multiprocessing/engine.py", line 119, in from_engine_args
      ERROR 12-27 00:09:08 engine.py:366]     return cls(ipc_path=ipc_path,
      ERROR 12-27 00:09:08 engine.py:366]   File "/workspace/vllm/vllm/engine/multiprocessing/engine.py", line 71, in __init__
      ERROR 12-27 00:09:08 engine.py:366]     self.engine = LLMEngine(*args, **kwargs)
      ERROR 12-27 00:09:08 engine.py:366]   File "/workspace/vllm/vllm/engine/llm_engine.py", line 273, in __init__
      ERROR 12-27 00:09:08 engine.py:366]     self.model_executor = executor_class(vllm_config=vllm_config, )
      ERROR 12-27 00:09:08 engine.py:366]   File "/workspace/vllm/vllm/executor/executor_base.py", line 36, in __init__
      ERROR 12-27 00:09:08 engine.py:366]     self._init_executor()
      ERROR 12-27 00:09:08 engine.py:366]   File "/workspace/vllm/vllm/executor/gpu_executor.py", line 35, in _init_executor
      ERROR 12-27 00:09:08 engine.py:366]     self.driver_worker.load_model()
      ERROR 12-27 00:09:08 engine.py:366]   File "/workspace/vllm/vllm/worker/worker.py", line 155, in load_model
      ERROR 12-27 00:09:08 engine.py:366]     self.model_runner.load_model()
      ERROR 12-27 00:09:08 engine.py:366]   File "/workspace/vllm/vllm/worker/multi_step_model_runner.py", line 649, in load_model
      ERROR 12-27 00:09:08 engine.py:366]     self._base_model_runner.load_model()
      ERROR 12-27 00:09:08 engine.py:366]   File "/workspace/vllm/vllm/worker/model_runner.py", line 1096, in load_model
      ERROR 12-27 00:09:08 engine.py:366]     self.model = get_model(vllm_config=self.vllm_config)
      ERROR 12-27 00:09:08 engine.py:366]   File "/workspace/vllm/vllm/model_executor/model_loader/__init__.py", line 12, in get_model
      ERROR 12-27 00:09:08 engine.py:366]     return loader.load_model(vllm_config=vllm_config)
      ERROR 12-27 00:09:08 engine.py:366]   File "/workspace/vllm/vllm/model_executor/model_loader/loader.py", line 386, in load_model
      ERROR 12-27 00:09:08 engine.py:366]     quant_method.process_weights_after_loading(module)
      ERROR 12-27 00:09:08 engine.py:366]   File "/workspace/vllm/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py", line 475, in process_weights_after_loading
      ERROR 12-27 00:09:08 engine.py:366]     layer.scheme.process_weights_after_loading(layer)
      ERROR 12-27 00:09:08 engine.py:366]   File "/workspace/vllm/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py", line 64, in process_weights_after_loading
      ERROR 12-27 00:09:08 engine.py:366]     input_scale=layer.input_scale)
      ERROR 12-27 00:09:08 engine.py:366]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1935, in __getattr__
      ERROR 12-27 00:09:08 engine.py:366]     raise AttributeError(
      ERROR 12-27 00:09:08 engine.py:366] AttributeError: 'QKVParallelLinear' object has no attribute 'input_scale'

Compressed Tensors version:

Name: compressed-tensors
Version: 0.8.1
Summary: Library for utilization of compressed safetensors of neural network models
Home-page: https://github.com/neuralmagic/compressed-tensors
Author: Neuralmagic, Inc.
Author-email: [email protected]
License: Apache 2.0
Location: /opt/conda/envs/py_3.9/lib/python3.9/site-packages
Requires: pydantic, torch, transformers
Required-by: llmcompressor, vllm

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[robertgshaw2-redhat]

Can you share your config.json?

Also, it does not look like you are passing a quantized model --> mistralai/mistral-123B-instruct does not seem to exist on the HF hub

[selalipop]

As I stated in the title, the model is quantized to FP8 W8A8. It's a private model so the model ID above is a placeholder.

  {
    "_name_or_path": "/.../",
    "architectures": [
      "MistralForCausalLM"
    ],
    "attention_dropout": 0.0,
    "bos_token_id": 1,
    "eos_token_id": 2,
    "head_dim": 128,
    "hidden_act": "silu",
    "hidden_size": 12288,
    "initializer_range": 0.02,
    "intermediate_size": 28672,
    "max_position_embeddings": 131072,
    "model_type": "mistral",
    "num_attention_heads": 96,
    "num_hidden_layers": 88,
    "num_key_value_heads": 8,
    "quantization_config": {
      "config_groups": {
        "group_0": {
          "input_activations": {
            "actorder": null,
            "block_structure": null,
            "dynamic": true,
            "group_size": null,
            "num_bits": 8,
            "observer": null,
            "observer_kwargs": {},
            "strategy": "token",
            "symmetric": true,
            "type": "float"
          },
          "output_activations": null,
          "targets": [
            "Linear"
          ],
          "weights": {
            "actorder": null,
            "block_structure": null,
            "dynamic": false,
            "group_size": null,
            "num_bits": 8,
            "observer": "minmax",
            "observer_kwargs": {},
            "strategy": "channel",
            "symmetric": true,
            "type": "float"
          }
        }
      },
      "format": "float-quantized",
      "global_compression_ratio": 1.4645315540129722,
      "ignore": [
        "lm_head"
      ],
      "kv_cache_scheme": null,
      "quant_method": "compressed-tensors",
      "quantization_status": "compressed"
    },
    "rms_norm_eps": 1e-05,
    "rope_theta": 1000000.0,
    "sliding_window": null,
    "tie_word_embeddings": false,
    "torch_dtype": "bfloat16",
    "transformers_version": "4.46.3",
    "use_cache": true,
    "vocab_size": 32768
  }

[MengqingCao]

I think you can check if the model.safetensors.index.json of the quantized model contains input_scale. The names of the quantization parameters obtained by different quantization tools may be different.

[selalipop]

I think you can check if the model.safetensors.index.json of the quantized model contains input_scale. The names of the quantization parameters obtained by different quantization tools may be different.

It doesn't: https://gist.github.com/selalipop/f661f43013cc2d1ec5169361f3c7be4b#file-model-safetensors-index-json

I noticed that with CUDA I'm able to run this model successfully, and looking at the code it seems like a bug in the ROCM path:

        if current_platform.is_rocm():
                        weight, weight_scale, input_scale = \
                            normalize_e4m3fn_to_e4m3fnuz(
                                weight=weight,
                                weight_scale=layer.weight_scale,
                                input_scale=layer.input_scale)        # Attribute accessed without check
                        if input_scale is not None:   
                            layer.input_scale = Parameter(input_scale,
                                                          requires_grad=False)
                    else:
                        weight_scale = layer.weight_scale.data
        
                    layer.weight = Parameter(weight.t(), requires_grad=False)
                    # required by torch.compile to be torch.nn.Parameter
                    layer.weight_scale = Parameter(weight_scale, requires_grad=False)

The guards imply input_scale is known to be optional, but there's no check to see if the attribute is present before accessing it.

I'm adding some checks and seeing if things work from there