[Bug]: Combine flashinfer with chunked prefill, LLM's answers become nonsense

[Juelianqvq]

Your current environment

The output of `python collect_env.py`

🐛 Describe the bug

1.flashinfer + chunked prefill outputs garbage
2.missing support of copy / swap blocks with flashinfer backend?

[962086838]

Similar issue here.

Environment:

Collecting environment information...
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 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: Could not collect
CMake version: version 3.30.2
Libc version: glibc-2.35

Python version: 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-4.18.0-348.el8.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A800-SXM4-80GB
GPU 1: NVIDIA A800-SXM4-80GB
GPU 2: NVIDIA A800-SXM4-80GB
GPU 3: NVIDIA A800-SXM4-80GB
GPU 4: NVIDIA A800-SXM4-80GB
GPU 5: NVIDIA A800-SXM4-80GB
GPU 6: NVIDIA A800-SXM4-80GB
GPU 7: NVIDIA A800-SXM4-80GB

Nvidia driver version: 535.104.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.1
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):                          128
On-line CPU(s) list:             0-127
Vendor ID:                       GenuineIntel
BIOS Vendor ID:                  Intel(R) Corporation
Model name:                      Intel(R) Xeon(R) Platinum 8358P CPU @ 2.60GHz
BIOS Model name:                 Intel(R) Xeon(R) Platinum 8358P CPU @ 2.60GHz
CPU family:                      6
Model:                           106
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       2
Stepping:                        6
Frequency boost:                 enabled
CPU max MHz:                     2601.0000
CPU min MHz:                     800.0000
BogoMIPS:                        5200.00
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 art 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 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust sgx bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid sgx_lc fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       3 MiB (64 instances)
L1i cache:                       2 MiB (64 instances)
L2 cache:                        80 MiB (64 instances)
L3 cache:                        96 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-31,64-95
NUMA node1 CPU(s):               32-63,96-127
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] flashinfer==0.1.2+cu121torch2.4
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] pyzmq==26.1.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.44.0
[pip3] triton==3.0.0
[conda] flashinfer                0.1.2+cu121torch2.4          pypi_0    pypi
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] pyzmq                     26.1.0                   pypi_0    pypi
[conda] torch                     2.4.0                    pypi_0    pypi
[conda] torchvision               0.19.0                   pypi_0    pypi
[conda] transformers              4.44.0                   pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: N/A
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    CPU AffinityNUMA Affinity   GPU NUMA ID
GPU0     X      NV8     NV8     NV8     NV8     NV8     NV8     NV8     PXB     NODE    SYS     SYS     SYS     NODE    0-31,64-95  0               N/A
GPU1    NV8      X      NV8     NV8     NV8     NV8     NV8     NV8     PXB     NODE    SYS     SYS     SYS     NODE    0-31,64-95  0               N/A
GPU2    NV8     NV8      X      NV8     NV8     NV8     NV8     NV8     NODE    PXB     SYS     SYS     SYS     NODE    0-31,64-95  0               N/A
GPU3    NV8     NV8     NV8      X      NV8     NV8     NV8     NV8     NODE    PXB     SYS     SYS     SYS     NODE    0-31,64-95  0               N/A
GPU4    NV8     NV8     NV8     NV8      X      NV8     NV8     NV8     SYS     SYS     NODE    PXB     NODE    SYS     32-63,96-127        1               N/A
GPU5    NV8     NV8     NV8     NV8     NV8      X      NV8     NV8     SYS     SYS     NODE    PXB     NODE    SYS     32-63,96-127        1               N/A
GPU6    NV8     NV8     NV8     NV8     NV8     NV8      X      NV8     SYS     SYS     NODE    NODE    PXB     SYS     32-63,96-127        1               N/A
GPU7    NV8     NV8     NV8     NV8     NV8     NV8     NV8      X      SYS     SYS     NODE    NODE    PXB     SYS     32-63,96-127        1               N/A
NIC0    PXB     PXB     NODE    NODE    SYS     SYS     SYS     SYS      X      NODE    SYS     SYS     SYS     NODE
NIC1    NODE    NODE    PXB     PXB     SYS     SYS     SYS     SYS     NODE     X      SYS     SYS     SYS     NODE
NIC2    SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    SYS     SYS      X      NODE    NODE    SYS
NIC3    SYS     SYS     SYS     SYS     PXB     PXB     NODE    NODE    SYS     SYS     NODE     X      NODE    SYS
NIC4    SYS     SYS     SYS     SYS     NODE    NODE    PXB     PXB     SYS     SYS     NODE    NODE     X      SYS
NIC5    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     NODE    NODE    SYS     SYS     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_2
  NIC1: mlx5_3
  NIC2: mlx5_4
  NIC3: mlx5_5
  NIC4: mlx5_6
  NIC5: mlx5_bond_0

Bug description

If I use

export VLLM_ATTENTION_BACKEND=FLASH_ATTN
CUDA_VISIBLE_DEVICES=0,1,2,3 vllm serve /PATH_TO_MODEL/Meta-Llama-3.1-70B-Instruct/ --tensor-parallel-size 4 --port 20000

The generated text is Fine.

But if I use

export VLLM_ATTENTION_BACKEND=FLASHINFER
CUDA_VISIBLE_DEVICES=0,1,2,3 vllm serve /PATH_TO_MODEL/Meta-Llama-3.1-70B-Instruct/ --tensor-parallel-size 4 --port 20000

The output is meaningless. And I have to set max_tokens=50 to stop the model generate repeated meaningless text.

The query body is simple:

{
    "model": "Meta-Llama-3.1-70B-Instruct/",
    "messages": [
        {
            "role": "user", 
            "content": "Hello, who are you?"
        }
    ],
    "temperature": 0.5,
    "max_tokens": 50
}