[Bug]: Generation sometimes slows to a crawl for all requests when there is a DRY sampler request

[Nero10578]

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 22.04.5 LTS (x86_64)
GCC version: (conda-forge gcc 11.3.0-19) 11.3.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:36:13) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-6.8.0-48-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090
GPU 3: NVIDIA GeForce RTX 3090

Nvidia driver version: 550.120
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.4.0
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):                               16
On-line CPU(s) list:                  0-15
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz
CPU family:                           6
Model:                                79
Thread(s) per core:                   2
Core(s) per socket:                   8
Socket(s):                            1
Stepping:                             1
CPU max MHz:                          4100,0000
CPU min MHz:                          1200,0000
BogoMIPS:                             6400.01
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 monitor ds_cpl vmx 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 pti intel_ppin ssbd ibrs ibpb stibp tpr_shadow 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 vnmi md_clear flush_l1d
Virtualization:                       VT-x
L1d cache:                            256 KiB (8 instances)
L1i cache:                            256 KiB (8 instances)
L2 cache:                             2 MiB (8 instances)
L3 cache:                             20 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-15
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          KVM: Mitigation: VMX disabled
Vulnerability L1tf:                   Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:                    Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:               Mitigation; PTI
Vulnerability Mmio stale data:        Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   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; Retpolines; IBPB conditional; IBRS_FW; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Mitigation; Clear CPU buffers; SMT vulnerable

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.45.2
[pip3] triton==3.0.0
[conda] blas                      2.16                        mkl    conda-forge
[conda] libblas                   3.8.0                    16_mkl    conda-forge
[conda] libcblas                  3.8.0                    16_mkl    conda-forge
[conda] liblapack                 3.8.0                    16_mkl    conda-forge
[conda] liblapacke                3.8.0                    16_mkl    conda-forge
[conda] mkl                       2020.2                      256  
[conda] nccl                      2.23.4.1             h52f6c39_2    conda-forge
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] pytorch-cuda              12.4                 hc786d27_7    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] pyzmq                     26.2.0                   pypi_0    pypi
[conda] torch                     2.4.0                    pypi_0    pypi
[conda] torchvision               0.19.0                   pypi_0    pypi
[conda] transformers              4.45.2                   pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
Aphrodite Version: 0.6.4
Aphrodite Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PHB     PHB     PHB     0-15    0               N/A
GPU1    PHB      X      PHB     PHB     0-15    0               N/A
GPU2    PHB     PHB      X      PHB     0-15    0               N/A
GPU3    PHB     PHB     PHB      X      0-15    0               N/A

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

🐛 Describe the bug

Not sure how to show this but if there is a DRY sampler being processed by Aphrodite, occasionally it would slow generations down to a crawl for all the requests currently being processed and not just for the DRY sampler request.

Otherwise DRY sampler seems to be working great now!

[gitzaidi]

I think this has to be expected, since DRY relies on purely CPU based operation. Aphrodite needs to process one request at a time for the CPU part of the code, right?

I presented two options to make DRY faster in another comment to the DRY PR, following the approach taken in ooba: allowing for a reduced dry_range and switching from tensors to lists in the CPU part of the code.

[AlpinDale]

@gitzaidi I've added range in #855.

I did play around with using python lists, but I remember running into issues due to the batched nature of our samplers - we need all parameters to Tensors. If you have a solution that works when batched, would be happy to review a PR!

[gitzaidi]

@AlpinDale Thanks for your reactivity, very impressive ! I will look into it then.

Also, do we agree that, as of now, the implementation considers the same dry_sequence_breaker_ids for each entry in the batch?

[AlpinDale]

@AlpinDale Thanks for your reactivity, very impressive ! I will look into it then.

Also, do we agree that, as of now, the implementation considers the same dry_sequence_breaker_ids for each entry in the batch?

I think you're right - that's a huge oversight. I'll fix ASAP. Also, I decided to try my hand at porting over the z-algorithm implementation at #856. Can you take a look?

[AlpinDale]

Fixed the sequence breaker ID issue at c6e0ae0

[gitzaidi]

Right now, I do not see issues compared to the ooba implementation, looks faithful to the ooba implementation (just noticed 2 typos, see comments)

[AlpinDale]

DRY should be faster now but still very slow. I'm attempting to write kernels to bypass this issue. Progress will be logged here: https://github.com/AlpinDale/dry_sampling_kernel

[gitzaidi]

@Nero10578 did this PR fix the issue on your end?

[AlpinDale]

#868 partially solved this issue. DRY is a lot faster now, but not as fast as other samplers. I think we can close this issue once a new release is made.