[Bug]: (Maybe) Input Preprocessing Blocks The Async Operations

May 9, 2025 by ADMIN 63 views

Introduction

In this article, we will discuss a potential issue with the input preprocessing in the async operations of the VLLM project. The issue is related to the add_request() function, which is slow and runs in sequential when the input has big data. We will explore the possibility of executing this operation in another process, waiting for the process to finish, and then transferring the processed data back to the main process.

Current Environment

The current environment is as follows:

PyTorch version: 2.6.0+cu124
CUDA version: 12.4.1
NVIDIA driver version: 550.54.15
Operating System: Ubuntu 22.04.4 LTS (x86_64)
Python version: 3.11.11

Describe the Bug

The issue is related to the following lines of code:

https://github.com/vllm-project/vllm/blob/3d1e3876520ae60271b14e009829a53e1cfb3e86/vllm/v1/engine/async_llm.py#L223-L226

The add_request() function is slow and runs in sequential when the input has big data. This can block the async operation and prevent the program from running efficiently.

Possible Solution

One possible solution to this issue is to execute the add_request() function in another process, wait for the process to finish, and then transfer the processed data back to the main process. This can be achieved using the multiprocessing module in Python.

Here is an example of how this can be done:

import multiprocessing

def process_request(request):
    # Process the request in this function
    # ...
    return processed_request

def add_request(request):
    # Create a new process to process the request
    p = multiprocessing.Process(target=process_request, args=(request,))
    # Wait for the process to finish
    p.join()
    # Get the processed request from the process
    processed_request = p.get()
    return processed_request

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Conclusion

In conclusion, the add_request() function in the VLLM project can block the async operation when the input has big data. One possible solution to this issue is to execute the add_request() function in another process, wait for the process to finish, and then transfer the processed data back to the main process. This can be achieved using the multiprocessing module in Python.

Additional Information

The current environment is as follows:

PyTorch version: 2.6.0+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
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 3.31.4
Libc version: glibc-2.35

Python version: 3.11.11 | packaged by conda-forge | (main, Dec  5 2024, 14:17:24) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-5.10.134-008.15.kangaroo.al8.x86_64-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: NVIDIA H20
GPU 1: NVIDIA H20
GPU 2: NVIDIA H20
GPU 3: NVIDIA H20
GPU 4: NVIDIA H20
GPU 5: NVIDIA H20
GPU 6: NVIDIA H20
GPU 7: NVIDIA H20

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:                   52 bits physical, 57 bits virtual
Byte Order:                      Little Endian
CPU(s):                          180
On-line CPU(s) list:             0-179
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Processor
CPU family:                      6
Model:                           143
Thread(s) per core:              1
Core(s) per socket:              180
Socket(s):                       1
Stepping:                        8
BogoMIPS:                        5200.00
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 nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pdcm 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 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 avx_vnni avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk avx512_fp16 arch_capabilities
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       4.2 MiB (90 instances)
L1i cache:                       2.8 Mi (90 instances)
L2 cache:                        180 MiB (90 instances)
L3 cache:                        195 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-89
NUMA node1 CPU(s):               90-179
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: Vulnerable
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] flashinfer-python==0.2.2.post1+cu124torch2.6
[pip3] numpy==2.2.2
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-cusparselt-cu12==0.6.2
[pip3] nvidia-ml-py==12.570.86
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] optree==0.14.0
[pip3] pyzmq==26.4.0
[pip3] torch==2.6.0+cu124
[pip3] torchaudio==2.6.<br/>
# [Bug]: (Maybe) Input preprocessing blocks the async operations

## Q&A

### Q: What is the issue with the input preprocessing in the async operations of the VLLM project?

A: The issue is that the `add_request()` function is slow and runs in sequential when the input has big data, which can block the async operation and prevent the program from running efficiently.

### Q: Why is the `add_request()` function slow?

A: The `add_request()` function is slow because it processes the input data sequentially, which can take a long time for big data inputs.

### Q: How can we fix this issue?

A: One possible solution is to execute the `add_request()` function in another process, wait for the process to finish, and then transfer the processed data back to the main process. This can be achieved using the `multiprocessing` module in Python.

### Q: What are the benefits of using multiprocessing to fix this issue?

A: Using multiprocessing can help to:

* Improve the performance of the program by running the `add_request()` function in parallel with the main process.
* Reduce the time it takes to process big data inputs.
* Make the program more efficient and scalable.

### Q: How can we implement multiprocessing in Python?

A: To implement multiprocessing in Python, you can use the `multiprocessing` module to create a new process that runs the `add_request()` function. You can then use the `join()` method to wait for the process to finish and the `get()` method to get the processed data back from the process.

### Q: What are some common pitfalls to avoid when using multiprocessing?

A: Some common pitfalls to avoid when using multiprocessing include:

* Not properly synchronizing access to shared data between processes.
* Not handling exceptions properly in the child process.
* Not using the `join()` method to wait for the child process to finish.

### Q: How can we debug multiprocessing issues?

A: To debug multiprocessing issues, you can use tools such as the `pdb` module to set breakpoints and inspect variables in the child process. You can also use the `multiprocessing` module's built-in debugging features, such as the `debug()` function.

### Q: What are some best practices for using multiprocessing in Python?

A: Some best practices for using multiprocessing in Python include:

* Using the `multiprocessing` module's built-in functions and classes to create and manage processes.
* Properly synchronizing access to shared data between processes.
* Handling exceptions properly in the child process.
* Using the `join()` method to wait for the child process to finish.

### Q: How can we optimize the performance of the program using multiprocessing?

A: To optimize the performance of the program using multiprocessing, you can:

* Use the `multiprocessing` module's built-in functions and classes to create and manage processes.
* Use the `join()` method to wait for the child process to finish.
* Use the `get()` method to get the processed data back from the process.
* Use the `debug()` function to debug multiprocessing issues.

### Q: What are some common use cases for multiprocessing in Python?

A: Some common use cases for multiprocessing in Python include:

* Running computationally intensive tasks in parallel with the main process.
* Improving the performance of programs that involve data processing or analysis.
 Making programs more efficient and scalable.

### Q: How can we measure the performance of the program using multiprocessing?

A: To measure the performance of the program using multiprocessing, you can use tools such as the `time` module to measure the time it takes to run the program. You can also use the `multiprocessing` module's built-in functions and classes to measure the performance of the program.

### Q: What are some best practices for measuring the performance of the program using multiprocessing?

A: Some best practices for measuring the performance of the program using multiprocessing include:

* Using the `time` module to measure the time it takes to run the program.
* Using the `multiprocessing` module's built-in functions and classes to measure the performance of the program.
* Using tools such as the `pdb` module to set breakpoints and inspect variables in the child process.
* Using the `debug()` function to debug multiprocessing issues.