[Performance]: FLASHINFER backend is slower than FLASH_ATTN on H100

[tdoublep]

Misc discussion on performance

TLDR: We are observing that FP8 throughput is significantly lower when using FLASHINFER backend vs. using the default backend (FLASH_ATTN) for llama3.1-8b on a single H100 using v0.6.4.dev22+g5b8a1fde.

Here is a simple repo script:

import vllm
import transformers
import time
import numpy as np

model = "neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8"
input_size = 1024
output_size = 1024
batch_size = 64

llm = vllm.LLM(
    model=model,
    max_model_len=input_size+output_size,
    use_v2_block_manager=True,
    num_scheduler_steps=8,
)


# create random batch
np.random.seed(42)
tokenizer = transformers.AutoTokenizer.from_pretrained(model)
tokens = [ [] for _ in range(batch_size) ]
for b in range(batch_size):
    for i in range(input_size):
        tokens[b].append(np.random.randint(tokenizer.vocab_size))


sampling_params = vllm.SamplingParams(
    max_tokens=output_size,
    ignore_eos=True,
)

t0 = time.time()
llm.generate(
    prompt_token_ids=tokens,
    sampling_params=sampling_params,
    use_tqdm=False
)
t_elap = time.time()-t0

tput = batch_size * output_size / t_elap

print("t_elap:     %.2f seconds" % (t_elap))
print("throughput: %.2f tokens/second" % (tput))

Running using FLASH_ATTN backend:

t_elap:     10.92 seconds
throughput: 6003.16 tokens/second

whereas running using FLASHINFER backend:

t_elap:     13.06 seconds
throughput: 5019.79 tokens/second

From reading the FlashInfer blog, I don't think these results are expected. It is a shame because we would really like to use FlashInfer to pick up the FP8 KV cache feature.

Your current environment (if you think it is necessary)

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 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.30.3
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-5.15.0-101-generic-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 H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3

Nvidia driver version: 550.54.15
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
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):                             192
On-line CPU(s) list:                0-191
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Platinum 8474C
CPU family:                         6
Model:                              143
Thread(s) per core:                 2
Core(s) per socket:                 48
Socket(s):                          2
Stepping:                           8
CPU max MHz:                        3800.0000
CPU min MHz:                        800.0000
BogoMIPS:                           4200.00
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 tsc_known_freq pni pclmulqdq dtes64 monitor 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 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust 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 avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          4.5 MiB (96 instances)
L1i cache:                          3 MiB (96 instances)
L2 cache:                           192 MiB (96 instances)
L3 cache:                           195 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-47,96-143
NUMA node1 CPU(s):                  48-95,144-191
Vulnerability Gather data sampling: Not affected
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 rstack overflow: 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, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] flashinfer==0.1.6+cu124torch2.4
[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==12.560.30
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.6.68
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.45.2
[pip3] triton==3.0.0
[conda] flashinfer                0.1.6+cu124torch2.4          pypi_0    pypi
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-cublas-cu12        12.1.3.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.1.105                 pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.1.105                 pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.1.105                 pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.1.0.70                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.0.2.54                pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.2.106               pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.4.5.107               pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.1.0.106               pypi_0    pypi
[conda] nvidia-ml-py              12.560.30                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.6.68                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.1.105                 pypi_0    pypi
[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
vLLM Version: 0.6.4.dev22+g5b8a1fde
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	NIC6	NIC7	NIC8	NIC9	NIC10	NIC11	NIC12	NIC13	NIC14	NIC15	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	0-47,96-143	0		N/A
GPU1	SYS	 X 	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	0-47,96-143	0		N/A
GPU2	SYS	SYS	 X 	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	0-47,96-143	0		N/A
GPU3	SYS	SYS	SYS	 X 	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	0-47,96-143	0		N/A
GPU4	SYS	SYS	SYS	SYS	 X 	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	PIX	SYS	SYS	SYS	SYS	SYS	SYS	48-95,144-191	1		N/A
GPU5	SYS	SYS	SYS	SYS	SYS	 X 	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	PIX	SYS	SYS	SYS	SYS	48-95,144-191	1		N/A
GPU6	SYS	SYS	SYS	SYS	SYS	SYS	 X 	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	PIX	SYS	SYS	48-95,144-191	1		N/A
GPU7	SYS	SYS	SYS	SYS	SYS	SYS	SYS	 X 	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	PIX	48-95,144-191	1		N/A
NIC0	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	 X 	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS				
NIC1	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	 X 	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS				
NIC2	SYS	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	 X 	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS				
NIC3	SYS	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	 X 	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS				
NIC4	SYS	SYS	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	 X 	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS				
NIC5	SYS	SYS	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	 X 	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS				
NIC6	SYS	SYS	SYS	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	 X 	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS				
NIC7	SYS	SYS	SYS	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	 X 	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS				
NIC8	SYS	SYS	SYS	SYS	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	 X 	PIX	SYS	SYS	SYS	SYS	SYS	SYS				
NIC9	SYS	SYS	SYS	SYS	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	 X 	SYS	SYS	SYS	SYS	SYS	SYS				
NIC10	SYS	SYS	SYS	SYS	SYS	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	 X 	PIX	SYS	SYS	SYS	SYS				
NIC11	SYS	SYS	SYS	SYS	SYS	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	 X 	SYS	SYS	SYS	SYS				
NIC12	SYS	SYS	SYS	SYS	SYS	SYS	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	 X 	PIX	SYS	SYS				
NIC13	SYS	SYS	SYS	SYS	SYS	SYS	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	 X 	SYS	SYS				
NIC14	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	 X 	PIX				
NIC15	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	 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
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3
  NIC4: mlx5_4
  NIC5: mlx5_5
  NIC6: mlx5_6
  NIC7: mlx5_7
  NIC8: mlx5_8
  NIC9: mlx5_9
  NIC10: mlx5_10
  NIC11: mlx5_11
  NIC12: mlx5_12
  NIC13: mlx5_13
  NIC14: mlx5_14
  NIC15: mlx5_15

Before submitting a new issue...

• Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

[comaniac]

Since the attention computation is still in FP16, could you benchmark with the original BF16 data type and see if there's still a gap? This could help locate the problem more precisely.

[jeejeelee]

Maybe useful info： flashinfer-ai/flashinfer#521

[tdoublep]

Thanks @jeejeelee but that issue related to prefill performance. A quick look using torch profiler indicates that the majority of time is spent in decode kernel for both backends:

using FLASH_ATTN:

-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg    # of Calls  
-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
void flash_fwd_splitkv_kernel<Flash_fwd_kernel_trait...         0.00%       0.000us         0.00%       0.000us       0.000us        4.548s        43.56%        4.548s     138.797us         32768  
void cutlass::device_kernel<(anonymous namespace)::c...         0.00%       0.000us         0.00%       0.000us       0.000us        3.026s        28.98%        3.026s      23.090us        131072  
                                  _C::cutlass_scaled_mm         0.22%      19.784ms         0.57%      50.497ms      12.328us     685.692ms         6.57%     685.739ms     167.417us          4096  
void cutlass::device_kernel<(anonymous namespace)::c...         0.00%       0.000us         0.00%       0.000us       0.000us     685.692ms         6.57%     685.692ms     167.405us          4096  
void vllm::scaled_fp8_quant_kernel<c10::BFloat16>(c1...         0.00%       0.000us         0.00%       0.000us       0.000us     453.434ms         4.34%     453.434ms       3.355us        135168  
void vllm::act_and_mul_kernel<c10::BFloat16, &(c10::...         0.00%       0.000us         0.00%       0.000us       0.000us     387.954ms         3.72%     387.954ms      11.481us         33792  
                                           aten::linear         0.04%       3.608ms         0.89%      78.460ms      74.299us       0.000us         0.00%     382.781ms     362.482us          1056  
                                           aten::matmul         0.03%       2.210ms         0.78%      68.454ms      64.824us       0.000us         0.00%     382.781ms     362.482us          1056  
                                               aten::mm         0.52%      46.250ms         0.75%      66.244ms      62.731us     382.781ms         3.67%     382.781ms     362.482us          1056  
sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128...         0.00%       0.000us         0.00%       0.000us       0.000us     370.537ms         3.55%     370.537ms     361.852us          1024  
-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  

using FLASHINFER:

-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg    # of Calls  
-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
void flashinfer::BatchDecodeWithPagedKVCacheKernel<(...         0.00%       0.000us         0.00%       0.000us       0.000us        6.172s        49.95%        6.172s     188.351us         32768  
void cutlass::device_kernel<(anonymous namespace)::c...         0.00%       0.000us         0.00%       0.000us       0.000us        3.033s        24.55%        3.033s      23.140us        131072  
                                  _C::cutlass_scaled_mm         0.18%      19.947ms         0.46%      51.879ms      12.666us     682.428ms         5.52%     682.428ms     166.608us          4096  
void cutlass::device_kernel<(anonymous namespace)::c...         0.00%       0.000us         0.00%       0.000us       0.000us     682.428ms         5.52%     682.428ms     166.608us          4096  
void vllm::scaled_fp8_quant_kernel<c10::BFloat16>(c1...         0.00%       0.000us         0.00%       0.000us       0.000us     449.241ms         3.64%     449.241ms       3.324us        135168  
void vllm::act_and_mul_kernel<c10::BFloat16, &(c10::...         0.00%       0.000us         0.00%       0.000us       0.000us     387.014ms         3.13%     387.014ms      11.453us         33792  
                                           aten::linear         0.04%       3.965ms         0.72%      81.374ms      77.059us       0.000us         0.00%     382.844ms     362.542us          1056  
                                           aten::matmul         0.02%       2.199ms         0.61%      69.100ms      65.436us       0.000us         0.00%     382.844ms     362.542us          1056  
                                               aten::mm         0.41%      46.417ms         0.59%      66.901ms      63.353us     382.844ms         3.10%     382.844ms     362.542us          1056  
sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128...         0.00%       0.000us         0.00%       0.000us       0.000us     370.651ms         3.00%     370.651ms     361.964us          1024  
-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  

So it really seems like the Flashinfer decode kernel is slower than FA equivalent.

[tdoublep]

@comaniac Sure, here are the bf16 results, as well as some other datapoints we have collected:

The column FORCE_TENSOR_CORES relates to enabling the changes from this PR: #9497

It looks like the heuristic to determine when to enable the tensor cores isn't working well for this model:

vllm/vllm/attention/backends/flashinfer.py

Line 127 in 1ffc8a7

use_tensor_cores = num_qo_heads // num_kv_heads > 4

Kudos to my colleague @cyang49 for discovering this!

[tdoublep]

This issue seems relevant: flashinfer-ai/flashinfer#520

It sounds like setting use_tensor_cores=True actually invokes the prefill kernel, so the issue that @jeejeelee linked above may indeed be very relevant.

[yzh119]

@tdoublep @jeejeelee @cyang49 Thank you all for the investigation, and yes I do think the original heuristics doesn't work for fp8.

[comaniac]

Closed via #9497