[Bug]: free_seq is invoked multiple times unnecessarily when one request is finished.

[tongping]

Your current environment

The output of `python collect_env.py`

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 3.30.0
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-5.4.210.bsk.8-sign-amd64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: Tesla P100-PCIE-16GB
GPU 1: Tesla P100-PCIE-16GB
GPU 2: Tesla P100-PCIE-16GB
GPU 3: Tesla P100-PCIE-16GB
GPU 4: Tesla P100-PCIE-16GB
GPU 5: Tesla P100-PCIE-16GB
GPU 6: Tesla P100-PCIE-16GB
GPU 7: Tesla P100-PCIE-16GB

Nvidia driver version: 470.199.02
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, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          64
On-line CPU(s) list:             0-63
Vendor ID:                       GenuineIntel
BIOS Vendor ID:                  Intel
Model name:                      Intel(R) Xeon(R) CPU E5-2682 v4 @ 2.50GHz
BIOS Model name:                 Intel(R) Xeon(R) CPU E5-2682 v4 @ 2.50GHz
CPU family:                      6
Model:                           79
Thread(s) per core:              2
Core(s) per socket:              16
Socket(s):                       2
Stepping:                        1
CPU max MHz:                     3000.0000
CPU min MHz:                     1200.0000
BogoMIPS:                        4999.90
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 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 cdp_l3 invpcid_single intel_ppin tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts
Virtualization:                  VT-x
L1d cache:                       1 MiB (32 instances)
L1i cache:                       1 MiB (32 instances)
L2 cache:                        8 MiB (32 instances)
L3 cache:                        80 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-15,32-47
NUMA node1 CPU(s):               16-31,48-63
Vulnerability Itlb multihit:     KVM: Mitigation: Split huge pages
Vulnerability L1tf:              Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:               Vulnerable: Clear CPU buffers attempted, no microcode; SMT vulnerable
Vulnerability Meltdown:          Vulnerable
Vulnerability Mmio stale data:   Vulnerable: Clear CPU buffers attempted, no microcode; SMT vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Vulnerable, STIBP: disabled, RSB filling, PBRSB-eIBRS: Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Vulnerable: Clear CPU buffers attempted, no microcode; SMT vulnerable

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-ml-py==12.555.43
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.5.82
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.45.1
[pip3] triton==3.0.0

Model Input Dumps

No response

🐛 Describe the bug

There are two invocations for free_seq() if a request has finished.

One case:
def _process_sequence_group_outputs():
if seq.is_finished():
# print(f"free_seq at line number: {inspect.currentframe().f_lineno}")
for scheduler in self.scheduler:
scheduler.free_seq(seq)

The second case:
def _process_model_outputs():
if finished_now:
for scheduler in self.scheduler:
scheduler.free_finished_seq_groups()

In the second case, free_finished_seq_groups() --> _free_finished_seq_group() --> _free_finished_seqs() --> free_seq().

What problem can be caused:
def free_seq(self, seq: Sequence) -> None:
"""Free a sequence from a block table."""
self.block_manager.free(seq)

However, it is worthy mentioning that block_manager actually avoid the problem due to its implementation:
def free(self, seq: Sequence) -> None:
if seq.seq_id not in self.block_tables:
# Already freed or haven't been scheduled yet.
return
However, it is clear that it is better to avoid such an unnecessary invocation.

from vllm import LLM, SamplingParams

# Sample prompts.
prompts = [
    "Hello, this is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]

prompts = 10 * prompts

# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=512)

# Create an LLM.
llm = LLM(model="facebook/opt-125m", enforce_eager=True)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.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}")



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