[Bug] Long context: Decoding performance degradation

[DayDayupupupup]

Checklist

• 1. I have searched related issues but cannot get the expected help.
• 2. The bug has not been fixed in the latest version.

Describe the bug

Compared with vllm 0.4.2, when the input length is 200k, the decoding time is significantly increased.

• prompt = 1k, lmdeploy TPOP=7.3ms, vllm TPOP=8.66ms
• prompt = 200k, lmdeploy TPOP=63ms, vllm TPOP=30ms

Reproduction

TEST Environment

• IMAGE: openmmlab/lmdeploy:v0.4.1
• GPU: H800 Driver Version: 535.129.03 CUDA Version: 12.2
• TP: 1

Model

• Meta-Llama-3-8B-Instruct: Change the max_position_embeddings in config.json to 409600

Testing script

• llama3_lmdeploy.py

import time
from lmdeploy import pipeline, TurbomindEngineConfig, GenerationConfig
from lmdeploy.model import ChatTemplateConfig
# Sample prompts.
# chat template length: 10, 
max_input_len = 200*1024-10
print(f'max input len = {max_input_len//1024}k       ')
model_path = 'Meta-Llama-3-8B-Instruct'
prompts = "hi" * (max_input_len - 1)

engine_config = TurbomindEngineConfig(quant_policy=0,
                                      tp=1,
                                      #cache_max_entry_count=0.8,
                                      session_len=max_input_len+2000)
#chat_template_config = ChatTemplateConfig.from_json("llama3_chat_template_config.json")
pipe = pipeline(model_path=model_path,
                backend_config=engine_config,
                #chat_template_config=chat_template_config,
                log_level='WARNING') 
gen_config = GenerationConfig(max_new_tokens=1023, 
                              temperature=0.6,
                              top_p=0.9,
                              ignore_eos=True,
                              stop_words=['<|end_of_text|>','<|eot_id|>'],
                              skip_special_tokens=False)
# Create an LLM.
start_time = time.perf_counter()
outputs = pipe([prompts], gen_config=gen_config)
end_time = time.perf_counter()
latency = end_time - start_time
print(f'warmup time: {latency*1000:.2f} ms')

start_time = time.perf_counter()
outputs = pipe([prompts], gen_config=gen_config)
end_time = time.perf_counter()
latency = end_time - start_time
print(f'infer time: {latency*1000:.2f} ms')

# Print the outputs.
for output in outputs:
    generated_text = output.text
    #print(f"Generated text: {generated_text!r}")
    print(f'prompt_tokens={output.input_token_len} gen_tokens={output.generate_token_len}')
    print(f'finish_reason: {output.finish_reason}')

• llama3_vllm.py

from vllm import LLM, SamplingParams
import time
from transformers import AutoTokenizer
import torch
# Sample prompts.
max_input_len = 200*1024-10
print(f'                 max input len = {max_input_len//1024}k       ')
model_path = 'Meta-Llama-3-8B-Instruct'

prompts = "hi" * (max_input_len - 1)
chat_prompt = '''<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n''' + prompts + '''<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n'''
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.6,
                                 top_p=0.9,
                                 ignore_eos=True,
                                 max_tokens=1024,
                                 )
                                 #stop=['<|end_of_text|>','<|eot_id|>'])
# Create an LLM.
llm = LLM(model=model_path,
          tensor_parallel_size=1,
          trust_remote_code=True,
          dtype='bfloat16',
          max_model_len=max_input_len+2000,
          #kv_cache_dtype="fp8",
          )

start_time = time.perf_counter()
outputs = llm.generate(chat_prompt, sampling_params)
end_time = time.perf_counter()
latency = end_time - start_time
print(f'warmup time: {latency*1000:.2f} ms')

start_time = time.perf_counter()
outputs = llm.generate(chat_prompt, sampling_params=sampling_params)
end_time = time.perf_counter()
latency = end_time - start_time
print(f'infer time: {latency*1000:.2f} ms')
# Print the outputs. 
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    gen_length = len(output.outputs[0].token_ids)
    print(f'finish_reason={output.outputs[0].finish_reason}')
    print(f'prompt_tokens={len(output.prompt_token_ids)} gen_tokens={gen_length}')

TEST MODE

llama3_lmdeploy.py

1. Set max_new_tokens=0, get TTFT
2. Set max_new_tokens=1023, TPOP = (infer_time - TTFT) / 1024

llama3_vllm.py

1. Set max_tokens=1, get TTFT
2. Set max_tokens=1024, TPOP = (infer_time - TTFT) / 1024

Performance comparison

Environment

sys.platform: linux
Python: 3.8.10 (default, Nov 22 2023, 10:22:35) [GCC 9.4.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0,1,2,3,4,5,6,7: NVIDIA H800
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.8, V11.8.89
GCC: x86_64-linux-gnu-gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
PyTorch: 2.1.0+cu118
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 11.8
  - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90
  - CuDNN 8.7
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.16.0+cu118
LMDeploy: 0.4.1+14e9953
transformers: 4.40.2
gradio: 3.50.2
fastapi: 0.111.0
pydantic: 2.7.1
triton: 2.1.0

Error traceback

No response

[lvhan028]

@lzhangzz is working on this issue. The related PR is #1606
@zhulinJulia24 may pay attention to the test case.

[lzhangzz]

With the script above, on A100-SXM4-80G

        output=1023     output=1      
before: 152453.82 ms - 66456.73 ms = 85997.09 ms
 #1606:  94043.37 ms - 68165.34 ms = 25878.03 ms

[DayDayupupupup]

UPDATE Performance comparison

With PR#1606, decoding performance has improved significantly! 👍🏻

              input=200k  output=1024 's TPOP 
before:                 63.1ms
 #1606:                 12.24ms

Thanks! @lvhan028 @lzhangzz

[DayDayupupupup]

@lzhangzz Can you test the above llama3_lmdeploy.py script again?
In this case, 200K_1024, PR#1606 vs lmdeploy 0.4.2

I noticed that the decoding speed dropped after upgrading to the new version 0.4.2

[lzhangzz]

@DayDayupupupup I got almost the same result on v0.4.2 compared with #1606

               output=1023  output=1 
- F16  KV cache: 94145.74 - 66536.14 = 27609.6  ms (run 1)
                 94387.54 - 66691.50 = 27696.04 ms (run 2)
- INT8 KV cache: 87000.81 - 65821.41 = 21179.4  ms

[datalee]

mark

[DayDayupupupup]

@lzhangzz Thanks for your reply.

In fact, the decoding performance is consistent between 0.4.2 and 0.4.1 with PR1606。

Correct the above data, 0.4.1+PR1606 performance data is wrong. WIth 0.4.1， I made some changes to support head_dim=64，and then merged PR1606, so TPOP=12.24ms at 200k is not accurate.

# src/turbomind/kernels/attention/decoding.cu +32
# I modifide 128 to 64, so I got TPOP=12.24ms(0.4.1 + my changes + PR1606 + LLAMA3)
# Using the original 128,  TPOP=16.56 ms(0.4.1 + PR1606 + LLAMA3)
static constexpr std::integral_constant<int, 128> kHeadDim{};

Another question, when is head_dim=64 expected to be supported？
Although I supported head_dim=64 and the inference is correct, but the performance is poor for long sequences. 😂

[lzhangzz]

when is head_dim=64 expected to be supported？

Likely in July.

Although I supported head_dim=64 and the inference is correct, but the performance is poor for long sequences

You may try to find the bottleneck using Nsight Compute

[DayDayupupupup]

Thx, I'll give it a try.