[Bug]: : CPU silently doesn't support multi-step (--num-scheduler-steps)

[wallashss]

Your current environment

The output of `python collect_env.py`

Collecting environment information...
INFO 09-13 19:13:45 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
PyTorch version: 2.4.0+cpu
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 12.3.0-1ubuntu1~22.04) 12.3.0
Clang version: Could not collect
CMake version: version 3.30.2
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jul 29 2024, 16:56:48) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-4.18.0-372.46.1.el8_6.x86_64-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: 
GPU 0: NVIDIA A100-SXM4-80GB
  MIG 3g.40gb     Device  0:

Nvidia driver version: 535.104.05
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):                          80
On-line CPU(s) list:             0-79
Vendor ID:                       GenuineIntel
Model name:                      Intel Xeon Processor (Icelake)
CPU family:                      6
Model:                           134
Thread(s) per core:              2
Core(s) per socket:              20
Socket(s):                       2
Stepping:                        0
BogoMIPS:                        5600.03
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq vmx ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves wbnoinvd arat avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid fsrm md_clear arch_capabilities
Virtualization:                  VT-x
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       2.5 MiB (80 instances)
L1i cache:                       2.5 MiB (80 instances)
L2 cache:                        160 MiB (40 instances)
L3 cache:                        32 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-39
NUMA node1 CPU(s):               40-79
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed:          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 IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] intel_extension_for_pytorch==2.4.0+gitfbaa4bc
[pip3] numpy==1.26.4
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0+cpu
[pip3] torchvision==0.19.0+cpu
[pip3] transformers==4.44.2
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.0@36bf8150cc3a048d69d9d2196128462014b9599d
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
�[4mGPU0	NIC0	CPU Affinity	NUMA Affinity	GPU NUMA ID�[0m
GPU0	 X 	SYS	40-79	1		N/A
NIC0	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

Model Input Dumps

No response

🐛 Describe the bug

I tested the following script running on a CPU backend which set num_scheduler_steps > 1 to force use mult-step:

from vllm import LLM, SamplingParams

llm = LLM(model="facebook/opt-125M", 
          gpu_memory_utilization=0.4, 
          max_model_len=1024,  
          num_scheduler_steps=8
          )


params = SamplingParams(seed=123,  prompt_logprobs=5, temperature=1)

prompts = ["How to make pizza?"]
outputs = llm.generate(prompts, sampling_params=params )

for o in outputs:
    print('_________')
    print('### Text')
    print('_________')
    for o2 in o.outputs:
        print(o2.text)

Got the following output:

INFO 09-13 19:33:10 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
WARNING 09-13 19:33:13 arg_utils.py:902] Enabled BlockSpaceManagerV2 because it is required for multi-step (--num-scheduler-steps > 1)
WARNING 09-13 19:33:13 config.py:370] Async output processing is only supported for CUDA or TPU. Disabling it for other platforms.
INFO 09-13 19:33:13 llm_engine.py:213] Initializing an LLM engine (v0.6.0) with config: model='facebook/opt-125M', speculative_config=None, tokenizer='facebook/opt-125M', 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=False, dtype=torch.float16, max_seq_len=1024, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cpu, 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=facebook/opt-125M, use_v2_block_manager=True, num_scheduler_steps=8, enable_prefix_caching=False, use_async_output_proc=False)
/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
  warnings.warn(
WARNING 09-13 19:33:14 cpu_executor.py:321] float16 is not supported on CPU, casting to bfloat16.
WARNING 09-13 19:33:14 cpu_executor.py:324] CUDA graph is not supported on CPU, fallback to the eager mode.
WARNING 09-13 19:33:14 cpu_executor.py:350] Environment variable VLLM_CPU_KVCACHE_SPACE (GB) for CPU backend is not set, using 4 by default.
INFO 09-13 19:33:14 selector.py:183] Cannot use _Backend.FLASH_ATTN backend on CPU.
INFO 09-13 19:33:14 selector.py:128] Using Torch SDPA backend.
INFO 09-13 19:33:15 selector.py:183] Cannot use _Backend.FLASH_ATTN backend on CPU.
INFO 09-13 19:33:15 selector.py:128] Using Torch SDPA backend.
INFO 09-13 19:33:15 weight_utils.py:235] Using model weights format ['*.bin']
Loading pt checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
/usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader/weight_utils.py:417: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state = torch.load(bin_file, map_location="cpu")
Loading pt checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  5.83it/s]
Loading pt checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  5.82it/s]

INFO 09-13 19:33:16 cpu_executor.py:208] # CPU blocks: 7281
Processed prompts: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:03<00:00,  3.86s/it, est. speed input: 1.55 toks/s, output: 4.14 toks/s]
_________
### Text
_________


Method

However accordingly to #8198 prompt logs causes vllm to crash when using multi-step. Which does not happen in the above log. This was actually a dummy way to check if the feature is actually active. Moreover checking the code, CPU has several specific classes that make a parallel implementation for CPU backend and it looks like it is not using the parameters of multi-step scheduling. There is also no warning in the log that inform the feature is not workig.

Expectation

Add a checking in the code to raise an exception or warning to inform the user that the feature is not supported.

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